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A MICROFLUIDIC BIOCHIP
BASED ON MAGNETORESISTIVE DETECTION OF NANOPARTICLES
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF MATERIALS SCIENCE AND ENGINEERING
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Sebastian Jeremias Osterfeld
December 2009
http://creativecommons.org/licenses/by-nc-sa/3.0/us/
This dissertation is online at: http://purl.stanford.edu/px491tp4561
Includes supplemental files:
1. This file is an open-access publication of some of the magnetic biochip assay results. (SJ
Osterfeld 2008 PNAS Publication.PDF)
2. This file is an open-access publication supplement which shows photos, e.g., of the magnetic
biochip readout hardware. (SJ Osterfeld 2008 PNAS Publication Supplement.PDF)
3. This file is a conference poster detailing the magnetic biochip research progresss in 2006. (SJ
Osterfeld 2006 Conference Poster.pdf)
4. This file is a copy-and-pastable code for Wolfram Mathematica, which calculates the
nanoparticle-sensor interaction a... (SJ Osterfeld 2009 Wolfram Mathematica (R) Code
Example.txt)
© 2010 by Sebastian Jeremias Osterfeld. All Rights Reserved.
Re-distributed by Stanford University under license with the author.
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
ii
I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.
Shan Wang, Primary Adviser
I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.
Nicholas Melosh
I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.
Robert White
Approved for the Stanford University Committee on Graduate Studies.
Patricia J. Gumport, Vice Provost Graduate Education
This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file inUniversity Archives.
iii
iv
ABSTRACT
The detection of magnetic nanoparticle (MNP) labels is a promising alternative
to optical detection of fluorescent labels in biomolecular assays, in part because MNPs
are not susceptible to pH, bleaching, or autofluorescence, but especially because
microscopic quantities of MNPs can be detected with simple and inexpensive
magnetoresistive sensors such as spin valves. The goal of this dissertation was to
develop and demonstrate a biochip based on this detection principle.
The particular novelty of this work is the extensive demonstration of magnetic
biochips in real assays, the establishment of a compatible microfluidic fabrication
process, and the development of a simple mathematical model which explains the
experimentally observed signal scaling trends.
Process challenges included finding a sufficiently durable ultra-thin biosensor
passivation and developing a fabrication process that is compatible with the delicate
nature of spin valve sensors, which cannot withstand high temperatures or corrosive
reagents. For the fluidics, a 30 micron layer of silicone elastomer was affixed to a rigid
glass wafer, thereby combining the advantages of soft lithography microfluidics, such
as low-temperature bonding and conformity, with the high alignment accuracy,
mechanical rigidity, and wafer-level integration that traditionally could only be
achieved with anodically bonded microfluidics.
The resulting open-well and multi-channel fluidic biochips have been validated
in several protein and DNA detection assays. Without employing molecular
amplification, protein detection sensitivities of approximately 1 pg/mL or 5 fM
concentration levels can be easily achieved. Even better performance is anticipated in
the near future as there are many avenues towards additional improvements of the base
technology.
v
ACKNOWLEDGMENTS
I would like to thank my advisor, Professor Shan X. Wang, whose outstanding
character, intellect, vision, and resourcefulness make him one of the best academic
leaders a student could hope for. The tools, infrastructure, and scientific guidance that
he provided allowed me to be productive and creative in my work, for which I am
truly grateful.
I would also like to thank Professor Robert L. White, whose advice and
enthusiasm have been a source of inspiration for me throughout the years.
I also would like to thank Professor Nick Melosh, who has taken an interest in
my work and kindly agreed to serve on my thesis defense and reading committee. I
also thank Professor Mike McGehee and Professor Joseph Liao for chairing my
dissertation defense.
My friend and co-worker Dr. Heng Yu has my sincere thanks and respect for
working tirelessly on the challenging aspects of the assay biochemistry. My thanks
also go out to Professor Nader Pourmand, who has supported this work with his
experience in assay development, and who was instrumental in getting some of the
assay results published in the Proceedings of the National Academy of Sciences.
At Hitachi’s Global Storage Technology division I would like to thank Dr.
Robert Fontana, Dr. Thomas Boone, Stefan Maat, and Jordan Katine for their interest
and for a great scientific collaboration which resulted in some very important data
presented in Chapter 5, Optimization and Characterization.
I also very much would like to thank my fellow students and coworkers in
Professor Wang’s group for much kindness, scientific collaboration, and many great
discussions and ideas: Drew Hall, Richard Gaster, Mingliang Zhang, Donkoun Lee,
Chris Earhart, Dok Won Lee, Liang Xu, Shu-Jen Han, LiangLiang Li, Wei Hu,
Guanxiong Li, Dong-Woon Shin, Seung-Young Bae, Aihua Fu, and Ai Leen Koh.
vi
Special thanks also go out to Dr. Robert Wilson, who first suggested to try MACS
nanoparticles, which turned out to work very well.
The Stanford Nanofabrication Facility was also essential to this work, because
the entire biochip fabrication process had been developed in the SNF cleanroom in
long hours. The people who supported me there were Mahnaz Mansourpour, Mary
Tang, and Uli Thumser.
The Materials Science Department and the Geballe Laboratory for Advanced
Materials were my scientific home, and I would like to thank Christina Konjevich, Fi
Verplanke, Jane Edwards, Professor Robert Sinclair, Professor Bruce Clemens,
Professor Dauskardt, and many more, for welcoming me there.
I also would like to thank The Whitaker Foundation, Leonard Shustek through
the Stanford Graduate Fellowship program, the ARCS Foundation, DARPA, and the
National Institutes of Health for generous funding and support.
Stanford University in general has been a wonderful place, and I have a great
amount of respect for all the kind, inspiring and accomplished people here. I am
thankful to be a part of this truly unique place.
And of course, I would like to thank my parents, Dr. Karina Krasomil-
Osterfeld and Dr. Karl-Hermann Osterfeld, and my dear wife Sheryl Lin, very much
for their trust and support throughout this endeavor.
vii
TABLE OF CONTENTS
List of Figures......................................................................................................................x
Chapter 1. Introduction........................................................................................................1
1.1. Research Aim .........................................................................................................1
1.2. Bioassays ................................................................................................................1
1.2.1. DNA Assays ..................................................................................................3
1.2.2. Protein Assays ...............................................................................................3
1.2.3. Label-Free Bioassay ......................................................................................4
1.2.4. Label-Based Bioassay ...................................................................................5
1.2.5. Homogeneous vs. Heterogeneous Bioassay ..................................................5
1.2.6. Multiplex Bioassay........................................................................................6
1.3. Magnetic Biochips..................................................................................................7
1.3.1. Principle of Operation – Magnetoresistive Sensors ......................................7
1.3.2. Principle of Operation – Nanoparticle Detection ........................................10
1.3.3. Benefits of Magnetic Labeling ....................................................................13
1.3.4. Prior Developments in the Field of Magnetic Biosensors...........................15
Chapter 2. Magnetic Biochip Development ......................................................................19
2.1. Biochip Fabrication Process .................................................................................19
2.1.1. Sensor Passivation .......................................................................................21
2.1.2. Sensor Geometry Development...................................................................23
2.2. Magnetic Nanotags...............................................................................................28
Chapter 3. Fluidic Biochip Development ..........................................................................32
3.1. Rationale for Microfluidics ..................................................................................33
3.2. The Need for a New Microfluidic Fabrication Technology.................................34
3.3. Thin PDMS on a Rigid Support ...........................................................................36
3.4. Dry-Etching and Bonding of PDMS ....................................................................39
3.5. Alignment Tolerant Two-Layer Fluidics..............................................................42
3.6. Packaging and Fluidic Connections .....................................................................44
viii
3.6.1. Snap-Off Edge Fluidic Connections............................................................46
3.6.2. Backside Port Fluidic Connections .............................................................47
3.7. Microfluidic Measurements..................................................................................51
3.8. Microfluidics Conclusion and Suggested Future Work .......................................54
Chapter 4. Assay Results...................................................................................................57
4.1. Direct-Binding Assay for Interferon-Gamma ......................................................60
4.2. Sandwich Assay for Interferon-Gamma...............................................................62
4.3. Sandwich Assay for Interferon-Gamma in 50% Serum.......................................66
4.4. Standard Curve for hCG in 50% Serum...............................................................67
4.5. hCG Assay Signal Scaling and Dynamic Range..................................................70
4.6. Magnetic Biochip Assay Conclusion ...................................................................72
Chapter 5. Optimization And Characterization .................................................................73
5.1. Development of a Simple 64-Sensor Signal Preamplifier....................................73
5.2. Sensor-to-Sensor Reproducibility ........................................................................77
5.3. Chip-to-Chip Reproducibility...............................................................................79
5.4. Reducing the Impact of Sensor Drift....................................................................80
5.5. Signal Dependence on Nanoparticle Distance .....................................................83
5.6. Signal Dependence on Tickling and Bias Fields..................................................86
5.7. Signal Dependence on Sensor Segment Width ....................................................89
Chapter 6. Mathematical Modeling...................................................................................92
6.1. The Resistance of a Spin-Valve Biosensor ..........................................................93
6.2. The Magnetization of Superparamagnetic Nanoparticles ....................................95
6.3. Effect of Nanoparticle on Sensor Resistance .......................................................96
6.4. Definition of Assay Signal ...................................................................................98
6.5. Model and Experiment – Optimal Tickling Field at Zero Bias............................99
6.6. Model and Experiment – Tickling and Bias Field Dependence.........................100
6.7. Model and Experiment – Sensor Segment Width Dependence..........................101
6.8. Insight Derived from Mathematical Modeling...................................................102
6.9. Mathematical Modeling Conclusion ..................................................................104
ix
Appendix A – Biochip Fabrication Process at SNF ........................................................105
Appendix B – Temperature Correction ...........................................................................111
Appendix C – Mathematica Code ...................................................................................114
Bibliography ....................................................................................................................115
x
LIST OF FIGURES
Number Page
Figure 1: Magnetoresistive sensor principle of operation. ...............................................................8
Figure 2: Nanoparticle detection principle of operation.................................................................10
Figure 3: Confocal-like label detection in magnetic assays. ..........................................................13
Figure 4: List of representative research groups developing magnetic biochips. ..........................18
Figure 5: Schematic outline of the biochip fabrication process. ....................................................20
Figure 6: Effectiveness of global-, lead-, and sidewall-passivation. ..............................................23
Figure 7: Sensor geometry evolution. ............................................................................................25
Figure 8: Comparison of water response of 2 kΩ and 40 kΩ biochips. .........................................26
Figure 9: Two actual spin valve sensors with different degrees of segmentation. .........................27
Figure 10: Illustration of three classes of magnetic nanotags. .......................................................28
Figure 11: Comparison of Miltenyi MACS and Immunicon magnetic nanoparticles. ..................30
Figure 12: Three generations of spin-valve sensor fluidic biochips...............................................33
Figure 13: Channel collapse in a PDMS section under compression.............................................36
Figure 14: Thickness dependence of spin-cast PDMS on solvent addition. ..................................37
Figure 15: Example of dry-etched PDMS pattern fidelity. ............................................................40
Figure 16: Microfluidic fabrication procedure. ..............................................................................41
Figure 17: Two-layer fluidics.........................................................................................................43
Figure 18: Three generations of fluidic interconnect technology...................................................44
Figure 19: Photo of wafer-level PDMS microfluidics fabrication. ................................................45
Figure 20: Schematic illustration of “snap-off edge” fluidic interconnects. ..................................46
Figure 21: Schematic illustration of backside port fluidic interconnects. ......................................47
xi
Figure 22: Fluidic spin-valve sensor biochips with backside port connections. ............................49
Figure 23: Fluidic layout of the 8-fluidic-channel biochip. ...........................................................50
Figure 24: Face-down microfluidic biochip with backside ports during measurement. ................51
Figure 25: First microfluidic measurements. .................................................................................52
Figure 26: Photo of open-well biochip and signal generation schematic.......................................58
Figure 27: Nanoparticle coverage image from scanning electron microscope...............................59
Figure 28: Direct binding interferon-gamma assay........................................................................60
Figure 29: Schematic illustration of magnetic label sandwich immunoassay................................62
Figure 30: Example of real-time data from IFN-γ sandwich assay quantification.........................64
Figure 31: Analyte concentration determines the nanoparticle binding curves. ............................65
Figure 32: IFN-γ sandwich assay in PBS buffer and in 50% serum compared (June 2006)..........66
Figure 33: Signal as a function of hCG concentration in 50% serum. ...........................................67
Figure 34: Offline sensor quantification example. .........................................................................68
Figure 35: Example of nanoparticle amplification.........................................................................69
Figure 36: Effect of nanoparticle amplification on standard curve. ...............................................70
Figure 37: Standard curve for hCG in 50% serum. ........................................................................71
Figure 38: New 64-channel signal preamplifier architecture from late 2007.................................74
Figure 39: Example of dynamic range and channel separation......................................................76
Figure 40: Example of sensor-to-sensor signal reproducibility in multiplex assay. ......................77
Figure 41: Example of chip-to-chip assay reproducibility. ............................................................79
Figure 42: Nanoparticle adsorption followed by nanoparticle release. ..........................................80
Figure 43: Quantification from nanoparticle adsorption vs. nanoparticle release..........................81
Figure 44: Signal vs. sensor-to-nanoparticle distance....................................................................83
Figure 45: Continuous measurement of the average nanoparticle distance. ..................................84
Figure 46: Determination of the optimal tickling and bias field for 1.5 µm sensors......................87
xii
Figure 47: Schematic illustration of sensor segment width evaluation. .........................................89
Figure 48: Signal and noise dependence on spin valve sensor segment width. .............................90
Figure 49: Experimental observations were explained with a mathematical model. .....................92
Figure 50: The resistance of a spin valve sensor segment..............................................................93
Figure 51: Example of calculated spin valve sensor MR transfer curves. .....................................94
Figure 52: Measured magnetization curve and model for MACS nanoparticles. ..........................95
Figure 53: Mathematical description of the sensor-nanoparticle interaction. ................................96
Figure 54: Model and experiment of two different types of sensors at zero bias field. .................99
Figure 55: Model and experiment of signal dependence on fields...............................................100
Figure 56: Model and experiment of signal dependence on sensor segment width. ....................101
Figure 57: Example of temperature-induced drift in the magnetoresistive sideband signal ........111
Figure 58: The centertone (sense current) drift can indeed be used to correct the signal ............112
1
CHAPTER 1. INTRODUCTION
1.1. RESEARCH AIM
The research efforts described in this thesis aim to significantly expand upon
the results of earlier students, which demonstrated the technical feasibility and
theoretical bioassay potential of magnetic biochips1,2. Specifically, this research work
attempts to analyze the shortcomings that existed before, and to develop appropriate
solutions that improve the ruggedness, manufacturability, performance, and ease of
use of magnetic biochips, to a point where actual analytic bioassays can be carried
out, reproducibly and under realistic conditions, with ease, routine protocol,
acceptable costs, and excellent assay results. Another important aim of this thesis is
the development of a microfluidic magnetic biochip, which is robust and suitable for
mass-production. The effort to develop such a microfluidic magnetic biochip is in
many ways inseparable from the overall optimization effort. Manufacturability and
practicality were important guiding principles at all stages of this work, even if it
meant eschewing solutions which permit good results with a lot of manual work in the
lab, but which are difficult to scale up to mass production.
1.2. BIOASSAYS
Molecular bioassays, which are used to quantify the concentration of specific
biological molecules in a sample, are an important analytical tool in many fields, such
as basic research, medicine, pharmacology, and forensics. While there are plenty of
simple bioassays which measure the concentration of small organic molecules such as
glucose, urea, and creatinine, the term “bioassay” more typically implies measuring
the concentration of complex biological macromolecules such as proteins or particular
segments of DNA. Such advanced bioassays are challenging for several reasons:
These macromolecular analytes in question are often present at very low
concentrations, and furthermore usually not distinguishable from similar molecules by
2
macroscopic physical measurements like mass spectral analysis or spectroscopy. The
reason, of course, is that the same large number of atoms and molecular bonds in a
biological macromolecule can give rise to many different conformations. Protein
folding is such an example: The final protein obtains its function not primarily from its
constituent atoms and molecular bonds, but from its overall shape and functional
domains.
This means that the goal of a bioassay is to identify and quantify, with a very
high degree of specificity, biological macromolecules on the basis of their shape and
structure. In theory, this could be accomplished with a sufficiently high resolution
microscopic technique, such as electron microscopy. However, aside from the fact that
these techniques would probably denature proteins before they could be identified,
such microscopic techniques suffer from their low throughput: The rate at which
macromolecules could be identified with today’s technology would be so low that it
would be extremely difficult to achieve a representative count at acceptable cost.
For these reasons, the vast majority of today’s bioassays hand off the task of
identifying the macromolecular analyte in question (the target) to other, highly
specialized complementary macromolecules (the probe). A particular probe binds to a
particular target analyte, typically with a very high degree of specificity and affinity.
As a rule of thumb, the specificity and affinity of the target-probe interaction increases
with the size of both macromolecules, and as a result the probes used in bioassays are
usually rather large – ranging from a few tens to more than a hundred kilodaltons, and
usually around 10 – 20 nanometers in size.
The two most common molecular probes are short segments of single-stranded
DNA and a class of proteins called immunoglobulins. While these are very different
types of molecules with very different properties – for example, DNA probes tend to
be very robust and durable, while immunoglobulins tend to be perishable in the open
air – they both serve a single purpose: To recognize a very specific complementary
macromolecule.
3
1.2.1. DNA ASSAYS
DNA probes, naturally, will reliably bind to complementary DNA strands.
DNA probes of commonly used lengths (25 – 100 base pairs) are also relatively easy
to synthesize and handle, and are predictable in the sense that the ideal probe for a
particular DNA target can be readily inferred, and that the affinity and specificity of
the probe can be reasonably well calculated in advance.
DNA bioassays are commonly used to test if a particular DNA sequence is
present in a sample or not, and occasionally also at what concentration. Because the
genome of an organism is relatively static, gene tests are primarily used for
identification and classification purposes of individuals, species, bacteria, and viruses.
Gene tests can also be used to assess someone’s risk for certain diseases such as breast
cancer3. However, exactly because of the static nature of the genome of most living
tissues not including tumors, DNA assays can usually not determine the current state
of health or disease progression/regression of an organism.
1.2.2. PROTEIN ASSAYS
Protein assays can be more challenging to set up and reproduce than DNA
assays, in part because there can be a multitude of different immunoglobulins (also
called antibodies), all of which can bind to a particular antigen, i.e., a specific
polypeptide, protein, or glycolipid4, with various affinities and specificities. A mixture
of such immunoglobulins for one particular antigen is called a “polyclonal antibody”,
which is what one typically obtains from batch fabrications with variable success. It is
possible to isolate and clone a particular immunoglobulin to obtain a “monoclonal
antibody” which raises costs but which provides a more reproducible performance.
The concentration of certain proteins, for example the blood level of interferon,
can change significantly and quickly with someone’s state of health. When a particular
protein has been positively linked to a certain condition, it is called a disease
biomarker and assumed to have significant diagnostic value. Testing for known
4
disease markers can be a useful tool in early diagnosis and medical treatment
monitoring, especially when done repeatedly over a period of time. Such proteomic
profiling of perhaps 4-20 biomarkers is expected to be a key to improving the
survival rate of patients with complex diseases such as cancers, autoimmune
disorders, infectious diseases, and cardiovascular diseases5,6,7.
1.2.3. LABEL-FREE BIOASSAY
One concept for utilizing these macromolecular probes (DNA or antibodies) in
an assay is as follows: First, locate the probes and measure a suitable, macroscopically
accessible property of the probes, such as their mass, conductivity, or refractive index.
Second, let the target molecules bind, and re-measure said property of the probes.
Now the mass of the probes should have increased, and their optical and electric
properties should also be different.
When such intrinsic properties of the probe are measured, one typically speaks
of a label-free assay. The challenge lies in the fact that these intrinsic property changes
are difficult to pick up because they are “diluted” by the surroundings of the probes –
the macroscopically measurable parameter is often mostly determined by the support
structure, the surrounding container, liquid, and other molecules, and only to a very
small percentage by the probes themselves. This means, first of all, that a large
number of potential phantom signal sources exist, and secondly that an extremely
sensitive method is needed to detect, for example, the change in mass of the probe –
nevertheless, this has been demonstrated to work, for example with the use of
microscopic tuning forks, or microcantilevers, onto which the probes are
immobilized8. Another technique of label-free detection in bioassays is based on
surface plasmon resonance9, in which the binding of the target to the probe cause a
change in the optical properties of the surface onto which the probes are immobilized.
The two significant advantages of label-free detection are the fact that the
analyte remains unaltered, and that the signal is generated in real-time as the analyte
5
binds. This allows one to measure the kinetics of the probe-target interaction, such as
the rates of binding and release10.
1.2.4. LABEL-BASED BIOASSAY
Instead of looking for changes in the intrinsic properties of the molecular
probes, as is done in label-free assays, it is also possible to attach a reporter molecule
(also called tag or label) to the target analyte of interest. The benefit of this method is
that the signal obtained from the reporter molecule can often be stronger and more
clearly distinguishable from the background than the intrinsic changes in the probe.
This is especially the case when the reporter molecule has some macroscopically
measurable property that is not typically found in the sample, such as radioactivity.
Using a radioactive reporter molecule would be called radiolabeling. More commonly,
however, an optical dye or fluorescent reporter molecule is attached to the target in
optical labeling. The change in emission or adsorption spectra can then be quantified
with optical systems. A third method, on which the work in this thesis is based, is
magnetic labeling, in which a tiny quantity of magnetic material is attached to the
analyte, which can then be detected with magnetic field sensors.
1.2.5. HOMOGENEOUS VS. HETEROGENEOUS BIOASSAY
One disadvantage of label-based bioassays is the need to distinguish between
labels that are bound to the target, and excess labels that are just floating around. Two
readily apparent solutions to this problem of excess labels are: washing, in which the
bound labels are held in place via the probes, while the excess labels are rinsed away;
or appropriately restricting the volume of observation to just the area of interest, i.e.,
looking only for labels at the surface onto which the probes are immobilized. The
former method requires multiple assay steps and rinsing away of the excess reagents,
which is called a heterogeneous assay. The latter method of restricting the observation
volume to the probes can allow measurements without removing excess reagents,
which is called a homogeneous assay.
6
A homogeneous assay is potentially much simpler to carry out, since in theory
reagents can just be added one by one. However, the larger number of concurrent
reagents present in label-based homogeneous assays can increase the chance of
unexpected cross-reactions, which could lower the overall specificity of the assay.
1.2.6. MULTIPLEX BIOASSAY
A “singleplex” bioassay employs only one type of molecular probe, which can
specifically recognize just one particular target molecule. If one wants to test for
several different targets, then several singleplex bioassays would need to be carried
out, each isolated in its own reaction volume and needing its own supply of reagents.
This is commonly done in microtiter well plates for example in Enzyme Linked
Immunoassays (ELISA), where each well has one probe.
Multiplex assays, on the other hand, have multiple probes in the same reaction
volume. The probes are usually in different locations, but all are in contact with the
same sample, and sharing all reagents. This results in a tremendous reduction of
reagent consumption and work effort, since a single 20-probe bioassay can
theoretically provide the same data as twenty individual singleplex assays.
This works particularly well with DNA assays, where up to 10,000 different
probes can be used in a single test tube11. In protein assays, the consensus is that a
high level of multiplexity is much more difficult to achieve12, because unexpected
cross-reactions tend to appear in protein assays which degrade the assay results. To
avoid such cross-reactions, multiplex protein assays need to be thoroughly tested,
theoretically with every conceivable analyte combination, which soon becomes
impractical. In general, there seems to be a tradeoff between the multiplexity and
specificity of protein assays, which as a result are usually limited to around a hundred
different probes13,14.
7
1.3. MAGNETIC BIOCHIPS
Magnetic biochips use magnetic sensors to measure the concentration of
specific analytes in bioassays quickly and inexpensively. The analytes, which often are
DNA segments or proteins, become visible to the magnetic sensors after they have
been tagged with small magnetic labels.
While magnetic biochips are in several ways similar to fluorescent label-based
bioassay chips, the use of magnetic labels can lead to many distinct advantages, such
as better background rejection, no signal fading due to bleaching, simpler and less
expensive hardware, higher sensitivity, real time signal monitoring, and seamless
integration with magnetic separation techniques.
1.3.1. PRINCIPLE OF OPERATION – MAGNETORESISTIVE SENSORS
At the heart of the magnetoresistive (MR) sensor technology stands an
elaborate multilayer thin film. This MR film is deposited layer-by-layer with utmost
care onto a non-conducting substrate wafer, and later divided into individual sensors
by photolithography and ion beam etching. The MR response of these sensors to
magnetic fields is very fast (nanoseconds or less) and is a static function of the
magnetic field strength and orientation. This is an important distinction from inductive
sensors such as pick-up coils, which respond only to changing magnetic fields.
The three types of magnetoresistive elements commonly used in magnetic
biochips are giant magnetoresistive (GMR) multilayer stacks, spin valves (SV), and
magnetic tunnel junctions (MTJ), all of which are examples of spintronic
(magnetoelectronic) sensors, meaning that spin interactions are used to modulate the
electronic properties of the structure15.
8
R1
Parallel
a.) Low Resistance
H
eR2
Perpendicular
b.) Intermediate Resistance
H
e Reference Layer
Free Layer
Resistive LayerR3
Antiparallel
c.) High Resistance
H
e
Figure 1: Magnetoresistive sensor principle of operation. The overall resistance of a magnetoresistive sensor varies with the degree of alignment of the two magnetic layers that sandwich a nonmagnetic layer. While the magnetization of the reference layer is fixed, the free layer will easily rotate and align itself with the applied magnetic field H. The actual current path depends on the electrical contact points and relative resistance of each layer.
There are many good books available which describe magnetoresistance in
great detail16, so it should suffice to use the spin valve as an example to illustrate the
general concept as shown in Figure 1. As electrons travel through a magnetized
material, they tend to align their spin with the magnetization of the material
surrounding them. If such spin polarized electrons cross an interface and enter a
differently magnetized region, they tend to be scattered, which causes an increase in
the apparent electrical resistance of the overall structure. In Figure 1 electrons emerge
from a magnetic reference layer with a fixed (pinned) magnetization, cross a non-
magnetic layer, and enter a soft magnetic layer with variable magnetization. The
magnetization of this so-called free layer closely follows the direction and magnitude
of the surrounding magnetic field H, while the magnetization of the pinned layer is
largely independent of H. The resistance of the magnetoresistive sensor therefore
depends on the orientation of the applied field, as illustrated in Figure 1.
The nonmagnetic layer helps to decouple the free layer from the pinned layer,
and it is also typically the primary determining factor of the base resistance of a spin
valve device. In spin valves the decoupling layer is usually a noble metal such as
copper or gold, and it transports the bulk of the electrons. As a result, SV films have
low sheet resistances on the order of 20 Ohms per square, which makes them suitable
for in-plane current transport, such as along a simple linear segment. Multiple SV
segments can be connected end-to-end in series to cover a large sensing area. A single
9
spin valve sensor can thus easily cover an area of about 100 µm in diameter, a size that
is comparable to a typical spot in DNA or protein arrays.
In contrast, MTJs utilize spin-dependent tunneling across a very thin insulating
oxide barrier, and accordingly have a much higher resistivities. As a result, they need
to be patterned into sensor elements with much larger electrical cross-sections, and the
measuring current is run perpendicular to the plane of the film, while spin valves can
be operated either current-in-plane (CIP) or current-perpendicular-to-plane (CPP).
Creating an MTJ sensor that covers a large area is challenging because a single small
defect in the thin but highly resistive tunneling layer can create a pinhole short, which
disables the entire MTJ segment, and the probability of such a defect increases with
the total sensor area. Electrostatic discharge is also a greater risk for MTJs than it is
for SV sensors, where a small defect would have minimal consequences for the
performance of the final device. Additionally, the relatively thick top lead on MTJs,
which is needed to minimize current crowding and the resultant highly localized
tunneling, is a potential complication which might decrease the effective sensitivity of
an MTJ in nanoparticle-sensing experiments, but with a careful design of the top
electrode shape it is possible to detect 10 nm sized particles 17.
For the work in this thesis, the first concern was to have an MR sensor that is
able to cover a large measuring area, because the resulting larger number of sampled
sites will reduce the stochastic noise in low concentration measurements, where
binding events are widely scattered and sporadic. Furthermore, for development work
it is desirable to select an MR sensor which is defect-tolerant, has low noise, and
which exhibits good baseline signal stability over time, which is important for
quantitative analytic assays which typically take several minutes. Considering these
requirements for large area coverage, defect tolerance, low noise, and signal stability,
a spin valve sensor with synthetic antiferromagnetic pinning was chosen for the
magnetic biochip on which this thesis is based.
10
Externally Applied Magnetic Field Ht
Particle Stray Field
Particle Magnetization M
SN
SN
SN
Pinned Layer
Free Layer
Ht
Hb
SN SN
A B
Figure 2: Nanoparticle detection principle of operation. To generate a detectable magnetic moment, superparamagnetic nanoparticles require the application of an external magnetic field Ht (A). On actual sensors, up to two magnetic fields are externally applied, a time-varying tickling field Ht and a static free layer stabilizing bias field Hb. For details see Chapter 6.
1.3.2. PRINCIPLE OF OPERATION – NANOPARTICLE DETECTION
The MR thin film on which the work in this thesis is based is a spin valve (SV)
structure which at room temperature can achieve a magnetoresistance of ∆R/Rmin =
12%. A simple linear stripe of this SV thin film can be used as the actual sensing
element on a magnetic biochip. In a very simplistic thought experiment, a miniature
permanent magnet could be used to label a biological molecule of interest. If this
molecule then attaches to the sensor, for example due to a specific binding reaction, a
small change in sensor resistance could be registered.
In reality, using miniature permanent magnets as labels would not be feasible,
since the labels would tend to steadily attract each other, just like real magnets would,
until they eventually would cluster and precipitate, largely canceling out each other’s
field in the process. Stabilizing surfactants would probably be insufficient to prevent
such magnetic aggregation of permanently magnetized labels.
To prevent aggregation of the magnetic nanoparticles, the magnetic labels
which are actually used are so small that their individual magnetization is weak and
continuously randomized by the thermal energy at room temperature. The resulting
time-average of zero net magnetic moment is called superparamagnetism. Materials
11
which are ferromagnetic in bulk are generally superparamagnetic in particle form with
diameters below ca. 10 nanometers.
Unlike a permanent magnet, superparamagnetic nanoparticles would be very
difficult if not impossible to detect directly with a spin valve sensor due to their lack
of a discernible magnetic field. So to generate a detectable magnetic signal from a
collection of superparamagnetic labels, a magnetic polarizing field, or “tickling field”
Ht, is externally applied to the nanoparticles as shown in Figure 2a. The tickling field
stabilizes the magnetic moment of the superparamagnetic labels, making them act like
permanently magnetized labels, which then are easy to detect. The magnetic tickling
field Ht can be alternated at a particular frequency ωHt, which also allows for the
possibility of frequency-based detection schemes such as narrowband detection.
To distinguish induced currents (electromotive forces, EMF) from the sensor’s
MR signal, it is furthermore possible to use an alternating sense current at a frequency
ωisense, which is amplitude modulated by the MR sensor at frequency ωHt. The
resulting modulated sense current contains two AM sidebands at frequencies ωisense ±
ωHt with amplitudes which are primarily a function of the MR effect and sense
current, but not of the EMF. In a typical setup (see also Figure 26d), the sense current
would have a frequency of ωisense = 500 Hz, while the tickling field would have an
amplitude of 80 Oe (rms) and a frequency of ωHt = 208 Hz. This would mean that the
EMF signal is contained in the 208 Hz band, while the actual sensor signal can be
found at 292 Hz and 708 Hz. This makes high signal to noise ratios possible in this
magnetic nanoparticle detection scheme18. On the other hand, if a DC sense current
had been used, i.e., ωisense = 0 Hz, then both the EMF and the sensor signal would be
found at 150 Hz, and the measurement would be less precise.
As shown in Figure 2b, an external magnetic bias field Hb is also applied to
the sensor and nanoparticles. Hb is a static field of ca. 50 Oe, and its purpose is to
reduce the sensor noise to acceptable levels by providing a default orientation for the
sensor’s free layer when Ht transitions through zero.
12
One complication in this scheme is that the AC tickling Ht field creates a very
strong signal in the MR sensors, which can be regarded as the signal baseline. The
tickling field Ht is minimally altered in the immediate vicinity of a magnetic particle,
which on binding to the MR sensor induces a small deviation from the MR sensor’s
signal baseline. It is this small deviation from the signal baseline which constitutes the
magnetic label signal. In relative terms, the nanoparticle signal in actual experiments
is equivalent to a sensor resistance change of a few tens to a few hundred parts per
million, while the baseline signal is roughly equivalent to a 5% - 10% resistance
change.
With a reasonable degree of circuit complexity (see Chapter 5), an array of
such sensors can be read out by successively polling individual sensors for the relevant
frequency components at ωisense ± ωHt. A typical sensor polling durations is 1 second,
which results in a frequency resolution bandwidth of 1 Hz. An 8-channel ADC card
(NI PCI-6281) makes it possible to have an aggregate polling rate of 8 sensors per
second. Additionally, ωisense could be different for various sensors (frequency
multiplexing), which permits simultaneous measurement of multiple sensors with one
acquisition channel. This increases the polling rate further. For example, towards the
end of this work, a 2-frequency, 8-channel data acquisition system had been
established with a cumulative polling rate of 16 sensors per second and 1 Hz
bandwidth.
13
Magnetoresistive Sensor
dNS
dNoise Floor
Observation
Volume
Observation Volume
Out of Range
Signal
d min d max
(1/d)3
Figure 3: Confocal-like label detection in magnetic assays. The signal from a magnetic label drops off significantly as the separation d between the label and sensor increases beyond a few hundred nanometers. This results in an observation volume which encompasses primarily surface-bound magnetic labels, while the background signal from distant magnetic labels beyond is relatively small. In this work, the closest practically attainable separation dmin is ca. 100 nm, and labels beyond dmax of ca. 500 nm tend to be indistinguishable from the noise floor.
1.3.3. BENEFITS OF MAGNETIC LABELING
Magnetic biochips are expected to have several technological advantages when
compared to more traditional fluorescence-based biochips. One of the most important
advantages may be the very small background signal, which stems from the fact that
ordinary assay ingredients and biological samples have usually no magnetic signal
sources. Another important advantage is the extreme simplicity of the hardware and
signal transduction pathway: With just a small, simple, inexpensive stripe of spin-
valve film, the surface concentration of magnetic labels is directly translated into a
linearly proportional19 electrical signal. The signal transduction pathway is also
immune from other common sources of measurement error, such as signal fading due
to label degradation (photobleaching in optical systems), chemical changes such as pH
or osmolarity, or changes in opacity and level of autofluorescence. The effect of
temperature drift is also easily accounted for, as shown in Appendix B – Temperature
Correction.
The required instrumentation (chip reader) is also simple, inexpensive, and
very suitable for miniaturization – reducing it to the size of a USB memory stick
14
seems entirely feasible. Furthermore, magnetically labeled molecules can also be
manipulated and extracted with magnetic fields, for example to pre-concentrate certain
analytes, which might work particularly well in combination with microfluidics.
Another important benefit of the magnetic labeling scheme is the dependence
of the signal on distance. To a first order approximation, the signal induced in an MR
sensor by a properly oriented magnetic label would be approximately proportional to
1/d3 (where d is the sensor-to-label distance), i.e., the signal attenuation with distance
would be that of a simple dipole field. Theoretical calculations20 predict that in some
circumstances there is an optimal sensor-to-label distance of ca. 70nm, however in
actual bioassays in this work (see Chapter 5, Optimization and Characterization), the
closest attainable distances were 120 nm or more, so that this prediction could not be
tested.
The rapid signal attenuation with distance leads to a very limited observation
volume around the magnetic sensors, as shown in Figure 3. Because of the finite
observation volume, properly designed MR sensors are ideal for detecting surface-
bound labels. Unbound magnetic labels, if they are adequately stable in suspension,
will remain largely outside the observation volume, and will therefore not interfere
with the detection of surface-bound labels, which are very close to the sensor. Simply
put, the rejection of the background signal from excess labels is very high – so high
that excess labels may not need to be removed, which means that homogeneous assays
can be performed. This is an important advantage of magnetic labeling over optical
labeling methods, where much more complex equipment would be needed to achieve a
similar effect, for example with confocal microscopy.
15
1.3.4. PRIOR DEVELOPMENTS IN THE FIELD OF MAGNETIC BIOSENSORS
It appears that the use of magnetic nanoparticles as labels in immunoassays
was first reported in 1997 by Kötitz et al. who used a superconducting quantum
interference device (SQUID) to detect the binding of antibodies21. While their
experiment was successful, it was performed in a magnetically shielded room, and the
SQUID magnetometer required cooling with liquid helium.
At around the same time, giant magnetoresistive (GMR) stacks22, and spin
valves (SV), which had been introduced in hard disk drives as read head sensors23 in
1995, were reaching sufficiently high performance levels at room temperature to
become suitable for magnetic biochips. Modern spin valve read heads are sensitive
and stable enough to detect magnetic data bits from a hard disk at temperatures up to
about 100 °C. Each magnetic bit typically contains a few hundred cobalt alloy
magnetic nanoparticles, but the spin valve sensors in hard disk drives operate at very
high frequencies (up to ~500 MHz) and benefit from the high signal modulation rate
which is beyond the 1/f noise range of the detection process. This advantage is absent
in biological detection assays, where the magnetic fluctuations that need to be detected
occur much more slowly. On one hand, slow changes permit longer sampling times
and correspondingly a better resolution of the absolute signal level, but on the other
hand, this also means that the requirements with respect to 1/f noise, interference,
drift, and long-term measurement stability are much more stringent when GMR and
spin valve sensors are used on biochips.
One of the earliest papers on biomagnetic detection assays using GMR sensors
was published in 1998 by Baselt et al. with a research group at the US Naval Research
Laboratory (NRL). Their bead array counter (BARC) chip was able to detect a single
2.8 µm diameter polystyrene bead containing dispersed maghemite24, albeit in a dry
state. Their data showed that the signal to noise ratio improved significantly as the
sensor width was decreased from 20 µm to 5 µm. Due to its potential for
miniaturization, Edelstein et al. later proposed the BARC sensor for use in a portable
16
detector for biological warfare agents25. In this paper, the NRL group also
demonstrated the application of a magnetic force to manipulate the magnetic beads
and improve the assay outcome. In 2003 the same group, using a multi-segment GMR
sensor, measured a signal change resulting from biologically bound 2.8 µm beads in
an aqueous solution. However, the binding event could not be recorded in real-time,
apparently because the application of the tickling field that magnetizes the beads
would also lead to clustering of the particles and hence obscure the natural binding
process26.
Graham et al. and a group based in Lisbon, Portugal, were probably the first to
publish real-time magnetic label capture curves, using a short single-segment SV
sensor, and a magnetic gradient to concentrate the particles in the vicinity of the
sensor. The biological signal was obtained by comparing the GMR signals before and
after washing off the nonspecifically bound magnetic particles. They also reported
particle clustering problems with 400 nm high magnetic content particles, which were
however resolved through the use of 2 µm lower magnetic content microspheres27.
A direct performance comparison of magnetic biochips with a fluorescent
detection method for DNA hybridization was first carried out by Schotter et al. with a
research group in Bielefeld, Germany, who defined the relative sensitivity of each
assay as the signal ratio between positive probes and negative probes, the latter of
which generate only the signal from nonspecific adsorption. The conclusion of this
group was that the performance of the magnetic detection method was superior to the
fluorescent method, primarily because at low concentrations the fluorescent method
had a higher background signal level28, which may stem from autofluorescence of the
negative probes.
Our research group at Stanford University, California, is one of the first to
focus on truly nanometer-sized magnetic labels. Unlike other groups which mostly
used particles that ranged from 200 nm to 3 µm, at Stanford the original aim had been
to develop a biochip based on high-moment monodisperse 11 nm diameter Co
nanoparticles29 and 16 nm diameter Fe3O4 nanoparticles19. To advance this approach
17
of using very small nanoparticles, the feasibility of using very thin passivation layers
was first evaluated using 4 nm of tantalum oxide29. Another distinction is the early
adaptation of spin valve sensors with line widths below two micrometers. In an earlier
implementation, such sensors with widths of 0.2 µm have already been shown to
detect a few tens of said 16 nm particles in a dry-environment before-and-after capture
experiment30.
GMR spin valve sensors have remained the dominant read head technology in
hard disk drives until roughly 2005, when magnetic tunnel junctions (MTJ) began
replacing GMR spin valve sensors in hard disk drives. However, whether MTJs will
also become the primary sensors in magnetic biochips remains to be seen – after all,
the different requirements in biological applications such as the need for low drift and
data collection over a large binding surface may well favor GMR spin valve sensors
over MTJs, which are better suited for highly localized measurements. On the other
hand, MTJs have significantly larger magnetoresistances and may have better
corrosion resistances than the all-metallic spin valves. In an early example, MTJs were
being used for real-time detection of 2.8 µm beads in an aqueous solution, albeit
without biological binding events31.
Several originally academic research efforts in magnetic biochips have
attracted commercial interests. The NRL group joined forces with NVE Corporation
and more recently Seahawk Biosystems Corporation to advance the development of
the BARC sensor. The IST group in Portugal collaborates with Micro Magnetics Inc.
Similarly, the good results of the research group at Stanford has led to the
formation of MagArray Inc., which pursues commercialization of magnetic biochips
for medical and research uses. On the side of corporate research, Philips Research in
the Netherlands has published research articles about their development of magnetic
biochips for use in point-of-care diagnostic medical devices.
18
Institutionand Site
Principal
Investigators
Magnetic
Particles
Sensor
Technology
Sensor
Passivation
NRL, Washington
NVE, Eden Prairie
Whitman, LJ
Tondra, M
Dynal M280
2.8 µm
GMR, Multi-Segment
1.6 x 8000 µm, 42 kΩ
Si3N4
250 nm
IST,
Lisbon, Portugal
Ferreira, HA
Freitas, PP
Nanomag-D
250 nm
SV, Single-Segment
2.5 x 100 µm, 1 kΩ
Al2O3/SiO2
100/200 nm
University of Bielefeld, Germany
Reiss, G Brueckl, H
Bangs CM01N 350 nm
GMR, Spiral 1 x 1800 µm, 12 kΩ
SiO2
100 nm
Stanford University,Stanford
Wang, SX
Pourmand, N
Miltenyi MACS
40 nm
SV, Multi-Segment
1.5 x 2800 µm, 45 kΩ
SiO2/Si3N4/SiO2
20/20/20 nm
Brown University,Providence
Xiao, G
Dynal M280 2.8 µm
MTJ, Ellipse Patch 2 x 6 µm, 142 Ω
Au/SiO2 200/200 nm
Philips Research,
Netherlands
Prins, M Ademtech
300 nm
GMR, Gradiometer
3 x 100 µm, 250 Ω est.
Unknown
>1000 nm est.
GMR
SV
MTJ
= GMR Stack
= Spin Valve
= Magnetic Tunnel Junction
Figure 4: List of representative research groups developing magnetic biochips. Basic design parameters are listed which can be used to estimate the theoretical performance limit of each platform. However, in practice the performance of a magnetic biochip may depend on additional factors such as the choice of binding chemistry, modulation, and signal processing.
Some representative research groups which are actively developing magnetic
biochips are listed in Figure 4. This list is necessarily not complete, but it can serve as
a starting point for a further literature search. Some of the basic parameters of each
group’s platform are also included, but it should be noted that each group typically
evaluates several different designs at any given time. The great variety of sensors and
nanoparticles under investigation is a reflection of the ongoing active development in
the field of magnetic biochips, in which definite design guidelines had not yet been
established.
19
CHAPTER 2. MAGNETIC BIOCHIP DEVELOPMENT
At the beginning of this work, the magnetic biochip development at Stanford
had not yet produced actual data from real bioassays. The sensors had to be measured
before and after the magnetic nanoparticles had been captured, which made it more
difficult to identify and eliminate data from malfunctioning sensors. Therefore, the
first task was to achieve a reproducible functionality of the magnetic biochip in actual
assays.
2.1. BIOCHIP FABRICATION PROCESS
As part of this thesis, the magnetic biochip fabrication process was refined
multiple times, and the final process, dimensions, and material choices will be
described and reasoned in this chapter. For better communication, the general process
is outlined immediately in Figure 5. Starting with a magnetoresistive spin valve film
composed of nanometer-scale metallic layers (e.g., Ta 5 / seed layer 2 / IrMn 8 / CoFe
2 / Ru 0.9 / CoFe 2 / Cu 2.3 / CoFe 1.5 / Ta 3, all thicknesses in nm) on a non-
conductive or passivated silicon support wafer (1), large portions of the MR film are
removed by ion beam milling, leaving only the patterned sensors remaining (2). A
typical sensor consists of several linear MR segments, each of which is ca. 0.75 x 100
micrometers in size. The removed portions of the MR film are then replaced with an
insulating oxide. This backfill step (3) planarizes the wafer surface and passivates the
easily corroded sides of the MR sensor. Corrosion-resistant leads (e.g., Ta 5 / Au 300 /
Ta 5, all thicknesses in nm), which electrically connect the sensor with the external
world, are then put in place (4). A thin global oxide passivation (e.g., SiO2 15 / Si3N4
15 / SiO2 15, all thicknesses in nm) is then applied to the entire wafer (5), but even
though it is globally applied, its primary purpose is the encapsulation of the sensor,
which it protects from the corrosive assay reagents.
20
Insulating Substrate
1. Substrate 2. Pattern sensor from film
3. Backfill / sidewall passivation 4. Apply conductive leads
5. Apply thin global passivation 6. Apply thick lead passivation
Magnetoresistive Film
critical dimension ca. 750 nm
ActiveArea
Figure 5: Schematic outline of the biochip fabrication process. Proportions are not to scale. Note especially the three different types of passivation: Sidewall passivation, global passivation, and lead passivation.
In a final step (6), a significantly thicker oxide passivation (e.g., SiO2 100 /
Si3N4 100 / SiO2 100, all thicknesses in nm) is applied over the conductive leads while
sparing the area of the sensor. This defines the active area of the sensor, i.e., the
roughly 100 x 100 micrometer central area of the sensor which is only protected by the
thin global passivation. The remaining area of the biochip is non-sensing and protected
by a much thicker and hence more durable passivation.
A detailed description of the fabrication procedure is given in Appendix A –
Biochip Fabrication Process at SNF. However, it should be noted that the exact spin
valve structure and fabrication procedure vary significantly according to what
fabrication equipment and capabilities are available at a given time. For example,
much of the fabrication work in this thesis was carried out at the Stanford
21
Nanofabrication Facility, which at this time had a certain range of processing
capabilities aimed at 100 mm wafers. As a result, the fabrication process as described
has been developed around the equipment at hand at the SNF, and will probably not be
directly transferable to another wafer foundry.
2.1.1. SENSOR PASSIVATION
In a bioassay it is highly desirable to be able to monitor the sensor signal
continuously. Such continuous real-time measurements can show the dynamics of
label and/or analyte adsorption, from which analyte binding kinetics can be inferred.
More importantly, however, a real-time signal is a very useful troubleshooting tool,
because it can help distinguish between relevant, assay-induced signals, and unwanted
extraneous signal changes from temperature drift, sensor malfunction, and various
other unexpected signal sources. This is not trivial: the real-time signal monitoring
ability revealed that the earliest magnetic biochips generated signal errors in response
to vibration (bad contacts), water exposure (dielectric losses), corrosion, temperature
changes, magnetic pre-conditioning (removal of irregular magnetic domains). Even
light sensitivity (unexplained but limited to a particular wafer) was observed at one
point. These and many other signal errors were identified and eventually removed with
the help of the real-time signal monitoring ability.
However, to achieve real-time measurements it is necessary to passivate the
sensors adequately to withstand the conditions of electrolytic corrosion that are created
when measurement currents and assay liquids are applied at the same time. At the
beginning of this work, the risk of electrolytic corrosion was considered to be
significant enough that the spin valve sensors were only measured before and after the
assay.
Magnetic biochips also face a particular challenge with regards to the thickness
of the sensor passivation. On one hand, the magnetoresistive sensor passivation needs
to be durable enough to minimize leakage currents and prevent sensor corrosion, but
on the other hand, the passivation needs to be as thin as possible. The thinnest possible
22
passivation will allow the closest possible proximity of the magnetic nanoparticles to
the sensor’s free layer, which will maximize the sensitivity of the finished chip. The
importance of the passivation thickness for chip sensitivity is illustrated schematically
in Chapter 1, Figure 3, where it is shown that the theoretical signal falls off with the
sensor-to-nanoparticle separation cubed.
For that reason, several experiments were conducted to determine how the spin
valve biochips needed to be passivated to permit real-time measurements. Different
passivation materials, passivation thicknesses, and passivation architectures were
tested.
Eventually, three types of passivation (sidewall, global, and leads) were
applied to the spin valve sensors as shown in Figure 5. To maximize the sensitivity,
the initial approach was to use the sensors without the thin global passivation, and to
instead rely on the tantalum capping layer of the spin valve film to provide sensor
topside passivation via the native tantalum oxide. To test the feasibility of this
approach, three wafers with test devices were fabricated, each with a different
combination of passivations: Wafer RA01 had only the sidewall passivation, wafer
RA06 had sidewall and lead passivations, while wafer RA04 further added a global
passivation of 30 nm SiO2.
To simulate the conditions of a simple bioassay, chips from each of these three
wafers were exposed to 1x phosphate buffered saline for an extended period of time,
and the change in resistance recorded as shown in Figure 6. Wafer RA01 fared worst,
showing resistance increases of more than 100%. The addition of the lead passivation
on RA06 reduced the corrosion to only 10% resistance increase, which is a hint that
unpassivated gold leads are probably forming a corrosion-accelerating galvanic couple
with the sensor in the PBS solution (this was later corroborated). Wafer RA04, which
finally adds the global passivation, fared best in this static PBS exposure experiment.
This showed that the global passivation could not be omitted.
23
% Increase in DC Resistance of Various MagArrayIV S ensorsAfter Immersion in Buffer (1xPBS + 0.01% Tween, pH 7.4)
0.1
1
10
100
1000
0 20 40 60 80 100 120
Sensor Number
Incr
ease
, %
of O
rigin
al V
alue
After 1hr
After 3hrs
Wafer RA01+ Sidewall Pass.- No Lead Pass.- No Global Pass.
Wafer RA06+ Sidewall Pass.+ Lead Pass.- No Global Pass.
Wafer RA04+ Sidewall Pass.+ Lead Pass.+ Global Pass.
Figure 6: Effectiveness of global-, lead-, and sidewall-passivation. All three types of passivation are required for adequate corrosion protection of the magnetic biosensor during prolonged exposure to phosphate buffered saline.
Subsequently, an additional improvement to the corrosion resistance of the
biochip was made by adopting a tri-layer silicon oxide-nitride-oxide (ONO) film
instead of plain SiO2 of the same thickness to passivate the sensors. This ONO film
was reported as a promising passivation structure in other people’s work32,33. In
general, it was found that the spin valve sensors are adequately protected against
corrosion with the addition of 30-50 nanometers this ONO film, meaning that this
passivation permitted continuous (live, real-time) readout of the sensors throughout
the assay, at a sense voltage of 0.5 V, with individual sensor failure rates of roughly
1% in simple actual assays, which is low enough to be inconsequential if redundant
sensors are used in conjunction with continuous sensor quality monitoring
implemented in software.
2.1.2. SENSOR GEOMETRY DEVELOPMENT
When designing an MR sensor for magnetic biochips, the first consideration
should be to find a shape which facilitates orderly magnetic domain formation. Edges
24
create a local demagnetizing field which favors alignment of the magnetization
parallel to any edges that the sensor has, therefore the default magnetic free layer
orientation would tend to fall along the axis of a linear segment. This shape anisotropy
effectively is a magnetization bias, which stabilizes the free layer and lowers the noise
of the sensor (see also Chapter 6 – Mathematical Modeling). On the other hand, rough
sensor edges, sharp corners, curved sensor segments, or a lack of shape anisotropy can
result in complex magnetic domain structures which reduce the linearity and
reproducibility of the sensor. For that reason, simple straight segments with a high
length/width ratio and smooth edges (good photolithography) are the basis of the
magnetic biosensor design.
At the beginning of this thesis, the shape of the magnetic sensor was chosen to
be similar to the designs of earlier students30, which had worked with sensors
consisting of a single linear spin valve segment (e.g., 1 x 2 µm, and 0.3 x 4 µm). To
eliminate the need for Electron-Beam Lithography and to permit the use of the much
faster contact mask optical photolithography equipment at the Stanford
Nanofabrication Facility (resolution limited to around 1 µm), the initial sensor
geometry was scaled up to a single line, 3 x 14 µm in size, as shown in Figure 7a. This
type of sensor also featured a small “gold patch” in its center, which was meant to
provide a means of anchoring capture probes via a special thiol-based binding
chemistry exclusively to the middle of the sensor, where theory predicts the highest
sensitivity17. However, in practice it turned out to be challenging to achieve a reliable
and exclusive localization of the capture probes only on the gold patch. Nanoparticle
binding occurred wherever the capture probe solution had contacted the chip, with
little distinction between the gold patch and the passivation. Furthermore, the gold
patch needed to be even narrower than the sensor, and well aligned, which meant that
the sensor itself needed to be wider than it could have been without a gold patch.
Another problem was the rather low resistance of this type of sensor: The sensor had
100 Ω, while the leads on the chip had ca. 20 Ω, which meant that the effective
magnetoresistance was noticeably reduced in practice.
25
Figure 7: Sensor geometry evolution. The blue areas indicate the spin valve film, the brown areas indicate the leads, which make electrical connections to the sensor. Sensor design A features a thin gold stripe in its center, intended to be a preferred anchoring site for the capture probes.
Without the ability to chemically localize the capture probe only on the center
of the sensor, the alternative was to spot the entire sensor and its surroundings with a
capture probe. In this scenario, the small size of the first sensor didn’t match well with
the spot size that DNA array printers could readily achieve, which was ca. 100 µm in
diameter (~7854 µm^2). Most of the spotted capture probe would have been wasted,
possibly even depleting the samples of low-abundance analytes, which would only
have a small (42 µm^2 / 7854 µm^2 = 0.5%) probability of binding on the small
sensor’s area. For that reason, another sensor was soon designed, which had a total
footprint of ca. 100 x 100 µm, as shown in Figure 7b. This sensor was better matched
to the size of a DNA spot, and it was also thought that such a sensor would provide
more reproducible data due to observing and averaging a larger number of capture
events. Indeed, this sensor became the first to result in actual magnetic bioassay data,
and it greatly facilitated systematic technical optimizations and bioassay development.
However, two flaws of the design shown in Figure 7b soon became apparent:
First, the sensor’s high resistance of ca. 40 kΩ, combined with the sensor’s passivation
failure threshold of 2V when immersed in water, meant that the sense current was
necessarily very small. This meant that even very small leakage currents across the
passivation and into the water became noticeable, and signal shifts would occur when
the water was applied and removed from the sensor.
26
IFN-γ Detection Assay - 300ng/mL2kΩ Chip SJO7-WD1-9-4, May-18-2007 - Raw Data Minus In itial Value
-5
0
5
10
15
20
0 5 10 15 20 25 30 35 40
Time, Minutes
Sig
nal A
mpl
itude
, µV
Anti-IFN-γ
Anti-IFN-γ
BSA 10%
BSA 10%
Signal w/o Wash - Start
Signal w/o Wash - End
Signal with Wash - Start
Signal with Wash - End
VA
C MACS Nanoparticles in PBS
H2O
H2O
PB
S
PB
S
PB
S
PB
S
IFN-γ Detection Assay - 300ng/mL40kΩ Chip RB2-7-6, May-18-2007 - Raw Data Minus Initial Value
-5
0
5
10
15
20
0 5 10 15 20 25 30 35 40
Time, Minutes
Sig
nal A
mpl
itude
, µV
Anti-IFN-γ
Anti-IFN-γ
BSA 10%
BSA 10%
Signal w/o Wash - Start
Signal w/o Wash - End
Signal with Wash - Start
Signal with Wash - End
VA
C MACS Nanoparticles in PBS
H2O
H2O
PB
S
PB
S
PB
S
PB
S
Figure 8: Comparison of water response of 2 kΩ and 40 kΩ biochips. The 40 kΩ biochip, due to its very high impedance and sensitivity to leakage currents, picks up a lot of signal swing when water is applied and removed (left). The revised design with a lower resistance sensor is insensitive to the application of water (right).
These “water signals” were usually on the order of ca. 10 µVrms (implying a
total passivation impedance of ca. 70 MΩ at 500 Hz, see footnote1), comparable to the
actual assay signals. Another lesser design flaw was that the patches of lead material,
which shunt out the irregular magnetic domains where spin valve segments join, were
rather small and closely spaced (see Figure 7b), and difficult to manufacture reliably.
Both of these issues were resolved with the final sensor geometry revision,
which reduces the number of SV segment joints to just five, resulting in larger shunts
and a significantly simpler lead layer design shown in Figure 7c. More importantly, by
connecting the SV segments in parallel and in series, a total sensor resistance of ca.
2.4 kΩ was realized. The lower resistance of latest sensor design allows a higher sense
current, which in turn lessens the impact of current leakage through the passivation,
which takes place when the sensor is immersed. As a result, by changing the sensor
resistance from 40 kΩ to 2 kΩ, the “water signals” are reduced from formerly 10
µVrms to theoretically 0.6 µVrms – in practice, the water signals are no longer
noticeable, as can be seen in actual data in Figure 8.
1 Assume the sensor is operated at 7 %MR, sense voltage (centertone) is 1 Vrms at
500 Hz, and magnetic field frequency is 200 Hz. The sideband at 700 Hz (our signal) is then 1*0.07/4 = 17.5 mVrms. To cause a change in the sideband of 10 µVrms, the centertone needs to change by 10/0.07*4 = 571 µVrms. This is 571 PPM of 1 Vrms. A bypass impedance of 70 MΩ changes a 40 kΩ sensor by 571 PPM.
27
Figure 9: Two actual spin valve sensors with different degrees of segmentation. The latest sensor design allows changing of the spin valve segment width without altering any other parameters of the sensor – for example, the total area and electrical resistance of the two sensors shown are identical.
Another intended feature of the latest sensor design is that the spin valve
segment width can be easily changed, without changing the overall area, resistance, or
sense current density in the spin valve layer, and without having to re-design the
electrical leads. For example, two spin valve segments of 3 µm in width can be
replaced by four spin valve segments which each are 1.5 µm wide but occupy
essentially the same footprint. This is illustrated with an actual chip in Figure 9, where
the left sensor has 12 segments, each 3.0 µm wide, while the right sensor has 24
segments, each 1.5 µm wide. This flexibility in sensor design can be exploited to put
comparable sensors, which differ only in their segment width, on the same biochip.
This makes it possible to vary and study the spin valve segment width (see also
Chapter 5, Optimization and Characterization). By placing different but comparable
sensors in close proximity, one can be certain that the effects of assay chip-to-chip
variability, magnetic field non-uniformity, etc., will be minimized.
An unresolved question in the sensor design is what the impact of the gold
patch shown in Figure 7a would be, if such a fine degree of selective surface
functionalization could be reliably achieved in real assays. Would the sensors be more
sensitive if nanoparticles would bind just to the center of the spin valve segments, as
theoretical considerations suggest? Additional work is needed before this question can
be tested in actual experiments.
28
Organic polymer (dextran) Magnetic material (Fe3O4) Non-magnetic material (Ru)
A B C
50 nm 16 nm 100 nm
40 nm
14 nm
Figure 10: Illustration of three classes of magnetic nanotags. In this work, the most frequently used magnetic nanotags consisted of an irregular-shaped organic matrix with several small clusters of magnetic material embedded within (A). Monolithic spherical magnetic nanoparticles with an organic surface coating (B) and disc-shaped, lithographically produced multilayer antiferromagnetic nanoparticles (C) may offer better magnetic performance.
2.2. MAGNETIC NANOTAGS
As mentioned earlier, superparamagnetic nanoparticles (also called nanotags)
are used to avoid clustering and precipitation. Furthermore, using nanotags with a
small diameter enhances their rate of diffusion and helps limit the sensor’s observation
volume to surface-bound nanotags only. While nanotags should also have as high a
magnetic moment as possible, their most important performance-determining
characteristics are probably their surface functionalization and colloidal stability.
Particle precipitation must not occur at any rate, because it would lead to a continuous
rise in the signal baseline that could obscure the equilibration of the binding reactions,
especially at low concentrations. Similarly, it is important that the surface
functionalization of the particles leads to highly selective and strong binding reactions
so that the molecules of interest are labeled exclusively and irreversibly, while other
areas need to remain particle-free.
In practice, it is surprisingly difficult to create nanotags which simultaneously
fulfill the requirements of small size, high magnetic moment, suspension stability, and
excellent binding selectivity. Prior to this work, magnetic biosensors used large
magnetic particles for proof of concept which ranged in size from ~250 nm to
3 µm24,28,34,35.
29
These micron-sized labels are not optimal for biomolecular assays – they
diffuse slowly, are prone to magnetic interaction and subsequent precipitation, and are
very bulky compared to the analyte molecules. Truly nanometer-sized magnetic labels
where therefore evaluated soon after, for example 16 nm nanotags, which were nearly
completely metallic to ensure a strong magnetic signal17. Synthetic antiferromagnetic
nanotags36,37, which were produced by imprint lithography and which offer a tightly
controlled size and custom-tailored magnetic properties, were also tested. An
illustrative overview of the different classes of nanotags investigated in this thesis is
given in Figure 10.
One general observation was that magnetic nanoparticles, despite being stable
in suspension in storage, would often start to precipitate when used on the biochip.
The probable reason for this precipitation is that the nanoparticles, once exposed to the
magnetic tickling field, attract each other much more strongly and form clusters which
are no longer stable in suspension. The telltale signature of such nanoparticle
precipitation can only be observed if the sensor can be measured continuously (which
is one of the achievements of this thesis) while the reagents are applied: It is a steady,
linear increase in the signal, which starts and stops with the nanoparticle incubation,
and which is independent of the biochemistry. A second often observed shortcoming
of various nanoparticles was a lack of binding selectivity. This could be tested by
functionalizing a biochip with two surface functionalizations in close proximity: one
which should bind the nanoparticles via specific molecular interactions – such as
streptavidin on the nanoparticles and biotin on the sensors – while other nearby
sensors functionalized with a suitable non-particle-binding protein, such as bovine
serum albumin (BSA), would serve as a control. A high particle binding selectivity
would result in a large difference in particle coverage between the two types of
functionalizations.
30
Immunicon vs. MACSTested on 600 pM HCG Assay, Sept-15-2007
0
5
10
15
20
25
30
35
40
MACS MACS Control Immunicon Immunicon Control
Sig
nal,
µV
Median: 22.7 Median: 0.8 Median: 23.5 Median: 2.5
MACS SNR: 29 dB Immunicon SNR: 19 dB
Figure 11: Comparison of Miltenyi MACS and Immunicon magnetic nanoparticles. Both nanoparticles generated similar signal levels, but Immunicon particles had more non-specific binding to the biochip, as revealed by the control sensors.
In Figure 11, two types of nanoparticles are compared in such a binding
selectivity experiment. Two biochips were identically prepared, each with 16 hCG-
specific sensors and 16 control sensors blocked with BSA solution. An actual bioassay
(as described in Chapter 4 – Assay Results) was then carried out, using a 600 pM hCG
solution as the analyte. To quantify the signal, one chip was exposed to 45 nm
streptavidin-functionalized MACS nanoparticles from Miltenyi, which are irregular-
shaped and have a structure as illustrated in Figure 10a. The other chip was exposed to
70 nm streptavidin-functionalized nanoparticles from Immunicon, which have a
structure as illustrated in Figure 10b. The signal levels after five minutes of
nanoparticle exposure are shown.
Both nanoparticles worked well, generating comparable signal levels on the
hCG-specific sensors. However, the control signal, which ideally should be zero, was
significantly higher with the Immunicon nanoparticles, probably due to a higher level
of non-specific binding. As a result, the MACS particles resulted in a ca. 28:1 signal to
background ratio (29 dB), while Immunicon particles only achieved a 9.4:1 signal to
background ratio (19 dB).
31
At the beginning of this work, MACS nanoparticles, which are estimated to
have only a small amount of magnetic material embedded in a largely organic matrix,
were thought to be too weak for use as magnetic nanotags. Eventually it was found
that these particles did actually generate useful signals on the magnetic biochip, in part
probably due to the very thin sensor passivation used in this thesis. Furthermore,
MACS nanoparticles are small – 35 to 50 nm in overall diameter depending on which
method is used to measure their diameter – which is a reasonable size for labeling
biological molecules. Due to their reliable suspension stability in the presence of an
applied magnetic field, and due to their consistent binding selectivity, MACS particles
eventually became the nanotags of choice for the work in this thesis, despite having a
somewhat lower magnetic signal than several other available magnetic nanotags38.
An important but unexpected side benefit of using small and magnetically
weak nanotags is that the spin valve sensors will only detect them when they are very
close – such as when they are attached the sensor surface. Due to their small size and
good suspension stability, the signal contribution from unbound nanotags is negligible.
Washing steps, which are typically required to remove unbound but strongly signal-
generating labels, can be omitted.
From actual experiments it seems that the sensor’s detection range for MACS
nanoparticles is indeed small enough to encompass only surface-bound nanotags (the
sensor’s detection range will be estimated more precisely in Chapter 5, Optimization
and Modeling). In practice, this suppression of unbound labels means that the true
amount of currently bound nanotags can be observed in real-time, and that negative
control sensors experience no signal changes during nanotag application and removal.
Simple one-step homogeneous assays with no washing steps are thus a possibility.
32
CHAPTER 3. FLUIDIC BIOCHIP DEVELOPMENT
A simple question was raised in the early stages of the magnetic biochip
development: How can one ensure that the sample, which one wants to analyze, is
maximally interacting with the sensor elements? For example in the case of a limited
number of available analyte molecules, any adsorption of analyte molecules to the
reaction chamber walls would reduce the number of analyte molecules that can still
interact with the sensor elements. This loss of analyte would manifests itself as a loss
of analytical sensitivity. So the question is, if analyte depletion is a concern, how can
one minimize the loss of analyte to non-sensing surface areas?
One immediate idea would be to alter the surface chemistry of the non-sensing
areas so as to adsorb as little of the analyte molecules as possible, e.g., by coating
these areas with anti-biofouling molecules such as poly(ethylene glycol) chains39 or a
blocking protein like bovine serum albumin (BSA). However, in that case one would
have to ensure that only the non-sensing areas are modified to prevent analyte
adsorption, and that the actual sensor elements maintain their affinity. Achieving this
kind of locally differential functionalization is not trivial and often works less reliably
than expected, especially if the non-sensing area is large, and the sensing area
comparatively small.
Another approach might be to confine the sample physically to the location
where it is needed, i.e., right above the sensor. This could be achieved with a channel
that flows the sample across the sensor, which would have the additional benefit of
providing a means of forced sample agitation, which should further help to maximize
the sensor-sample interaction and hence the analytical sensitivity. Such “microfluidic”
sample handling systems are already widely established in other academic projects,
often even with significant complexity40, and a simple microfluidic sample handling
system might be very useful on a magnetic biochip.
33
Figure 12: Three generations of spin-valve sensor fluidic biochips. Left: The earliest fluidic biochips contained a single fluidic channel, accessible from the chip edge, and short-segment spin valve sensors. Center: The second generation fluidic biochips contained a single fluidic channel, also accessible from the chip edge, which branched out to cover 64 large multi-segment spin valve sensors. Right: The latest generation fluidic biochip contained eight separate fluidic channels, accessible from twelve backside ports, and 64 large multi-segment spin valve sensors. In all three chip designs, the bond pads to make electrical connections are on the left and right sides of the chip.
3.1. RATIONALE FOR MICROFLUIDICS
The original motivation behind the development of microfluidics for the
magnetic biochip was the notion that a microfluidic assay should have better
sensitivity than a well-based assay, for reasons of better sample confinement and
better sample agitation, both of which would reduce the analyte diffusion distances
and maximize the sensor-sample interaction.
However, there are several additional potential benefits of moving from a well-
based assay to a microfluidic assay. The laminar flow of reagents in a microfludic
channel is a potential benefit, since it might result in more predictable, reproducible
interactions between the liquid reagents and the solid surfaces. Washing steps, for
34
example, could be carried out with a prescribed and uniform shear force at the channel
walls, which should be an improvement over the turbulent and non-uniform manual
washing procedure in well-based assays. A fluidic biochip would also have the
additional advantage of permitting the sample and reagent handling to be automated
with relatively modest hardware. Automation of the bioassay should further increase
the reproducibility of the results over manually pipetted assays. The closed reaction
compartment of a microfluidic biochip would also better prevent the contamination or
uncontrolled evaporation of reagents in response to room humidity fluctuations, which
can be a source of variability in open-well chips. Lastly, microfluidics can also be
used to compartmentalize a biochip into several isolated reaction volumes, while still
retaining a very small chip size. Reaction compartmentalization is often an important
strategy in highly multiplexed protein bioassays, where cross-reactivity between the
various antibodies can severely degrade the sensitivity and specificity of an assay.
3.2. THE NEED FOR A NEW MICROFLUIDIC FABRICATION TECHNOLOGY
Several existing methods of fabricating microfluidic channels were evaluated
and deemed unsuitable for use on a magnetic biochip for various reasons. The most
durable and chemically inert microfluidic channels would probably consist of silicon
or silicon dioxide, and indeed such channels can be fabricated on a separate wafer and
fused to the sensor wafer with one of several wafer-to-wafer bonding methods41.
However, even so-called low-temperature bonding methods42 employ temperatures of
at least 200°C, and most wafer bonding methods use temperatures far higher than that.
The temperature requirements alone ruled out the majority of these methods, because
they would almost certainly destroy the spin valve sensors in the process.
Additionally, several wafer bonding methods require that the mating surfaces be
perfectly clean and flat, which would be very difficult to achieve on the magnetic
biosensor chip.
An interesting alternative was therefore the so-called “soft lithography”, where
channels are fabricated by casting a slab of polydimethyl siloxane (PDMS) on a mold
35
template43. PDMS has two distinct advantages, one being that it is an elastomer, which
will conform to surface irregularities without reflowing. The other advantage is that
the PDMS surface can be chemically activated with a brief oxygen plasma exposure,
after which it adheres spontaneously to clean oxide surfaces such as the spin valve
sensor wafer, even forming permanent bonds under the right conditions.
However, the traditional PDMS-based microfluidic fabrication method has
several drawbacks, the most serious one probably being that the PDMS slab is sloppy
– at larger sizes it does not maintain its dimensions very well, and it can not be
handled like a regular, rigid wafer. This means that an accurate registration of the
entire PDMS slab to the entire sensor wafer is nearly impossible. To get around this
flaw, the PDMS slab is typically made almost 1000x thicker than the channels that it
provides, and then cut up into many individual pieces, which are aligned one-by-one
to the finished biochips as needed. The obvious downside is that this approach is very
manual and is most likely not suitable for large-scale production of biochips in an
industrial setting.
Since PDMS microfluidics with their low-temperature elastic sealing abilities
seemed like a great fit for the spin valve biochip in principle, the plan was to try to
develop an improved type of PDMS microfluidics which would also permit wafer-like
handling and alignment accuracies. Not only would this eliminate the chip-by-chip
PDMS attachment, but it would open up the possibility for wafer-based, possibly
large-scale industrial PDMS microfluidic fabrication.
36
The PDMS (blue) deforms when compressed between two rigid wafers (grey). Qualitatively shown are Van Mises stresses, which indicate the overall shear force concentration. Blue indicates zero, and red indicates a large positive Van Mises stress.
255
0
Figure 13: Channel collapse in a PDMS section under compression. A thick section of PDMS (left) is more likely to buckle and collapse a fluidic channel than a thin section of PDMS (right).
3.3. THIN PDMS ON A RIGID SUPPORT
To make a wafer-like handling and alignment accuracy possible for the very
flaccid PDMS material, the first idea was to add a rigid support layer to the PDMS.
Instead of just curing the PDMS material on the channel mold template, the PDMS
was covered with a glass wafer, to which it remained attached after curing and
separation from the mold. This approach was initially pursued with Dow Corning
Sylgard 184 PDMS polymer, 10:1 volume mixing ratio of base to curing agent.
However, two problems became apparent as this approach was pursued. First, the
separation of the PDMS from the mold was rather difficult, since the rigid-backed
PDMS could no longer be “peeled” off the mold, as it was done in traditional PDMS-
based fluidic fabrication. Second, channels fabricated this way were observed to
collapse easily with the slightest application of pressure from above. This was
especially a problem with very shallow channels which were supposed to be roughly
half a micrometer in height and roughly five micrometers wide – such shallow, wide
channels would often spontaneously collapse, with the PDMS sticking to the substrate
where the channel was supposed to be.
To remedy this problem and to make even very shallow channels possible, we
set a goal to make the PDMS layer thinner, comparable in thickness to the height of
the intended fluidic channel – a channel collapse should then be impossible. A finite
element simulation, shown in Figure 13, supported this notion.
37
Sylgard 184 Spin Casting Thickness
at 2500 rpm, 60 seconds
0.1
1
10
100
0.1 1 10
Parts Solvent per Part PDMS (vol)
Thic
knes
s, µ
m
Xylene or IPA Chloroform
Figure 14: Thickness dependence of spin-cast PDMS on solvent addition.
Unfortunately this further complicated the issue of separating the PDMS from
the mold: The thinner the PDMS layer was cast in the mold, the more difficult it
became separate the cured and rigidly supported PDMS from the mold afterwards.
Additionally, the high viscosity of the uncured PDMS made it impossible to squeeze
the PDMS thinner than ca. 100 µm between the mold and supporting cover wafer.
Since it seemed impossible to pattern very thin PDMS layers by molding,
another method of PDMS patterning was sought. A literature search revealed that
other researchers had successfully patterned cured PDMS films with dry etching
methods44,45. However, initial attempts to implement the reported method weren’t
satisfactory: The spin-cast PDMS was still relatively thick, and attainable etch rates
did not realistically permit full-depth dry etching of PDMS above 50 µm in thickness.
To obtain thinner PDMS films by spin-casting, the uncured PDMS was thinned
with organic solvents which would later evaporate during the curing of the PDMS on a
hot plate. Several PDMS spin-casting tests with various solvent loading were
conducted with the results summarized in Figure 14. Chloroform evaporated too
quickly to be an effective thinning solvent during the spin-casting. Xylene worked
38
very well as a PDMS thinning agent, resulting in 20 µm thick PDMS films at ca. 10
vol-% solvent loading, and permitting thicknesses down to 300 nm with high solvent
loading. Eventually, xylene was replaced by isopropanol alcohol, which was an
equally effective PDMS thinner at the thicknesses of interest while being less
hazardous.
Another reason for the use of a solvent thinner was that spin-cast PDMS films
would often have several defects in the form of raised protrusions. This was a
significant problem in later attempts to bond the PDMS wafer to the sensor wafer,
because these PDMS protrusions would prevent the wafers from making full and
uniform contact during the bonding step. However, with the addition of the thinning
solvent, the PDMS viscosity was lowered just enough to permit filtration of the mixed
but uncured PDMS prior to spin-casting. The filtration step was very effective in
reducing the PDMS film irregularities and yielded visually perfect PDMS films most
of the time. Due to the still significant viscosity of the uncured thinned PDMS
polymer, most 1 µm pore size syringe filters would clog almost immediately. 10 µm
and larger pore size syringe filters would often not resolve or even worsen the PDMS
film defect issue. Eventually, the optimum filter appeared to be a 5 µm pore size,
32 mm diameter syringe filter with a Supor® membrane, which was strong enough to
permit filtration of ca. 5 mL per filter before clogging, which was the amount needed
to coat one 4” wafer. Centrifugation of the PDMS prior to spin-casting removed
trapped air bubbles and further enhanced the PDMS film quality.
39
3.4. DRY-ETCHING AND BONDING OF PDMS
Starting with low-defect 30 µm thick films of Sylgard 184 PDMS spin-cast and
cured on 4” Pyrex wafers, the patterning of PDMS by dry etching was optimized next.
A 150 nm aluminum film was sputtered onto the PDMS as a hard mask. The hard
mask itself was patterned with photolithography and ion beam milling, which yielded
a much improved pattern fidelity compared to wet etching of the hard mask.
Complications in this step were the fact that the soft PDMS film would yield if the
photoresist was baked with the normal parameters, which created defects in the hard
mask that would later be transferred to the PDMS. The solution was to bake the
photoresist only at low temperatures before exposure, and not at all after development.
This way, defect-free patterned hard masks with the required etch selectivity could be
obtained on PDMS. Frequently the aluminum hard mask would also develop
characteristic surface wrinkles, which however did not interfere with the PDMS
etching and patterning resolution.
With the hard mask in place, extended dry etching times which exceeded the
durability of the photoresist were then tested in Drytek 1 at the SNF. The originally
reported dry etching recipe with a mixed O2-CF4 plasma did not result in a satisfactory
etch profile, possibly due to equipment-specific issues. Instead, an SF6-CHClF2 plasma
etch gave much better anisotropy with near etch vertical profiles. After several
variations, it was determined that in Drytek 1 the best results were obtained with a 105
W plasma at 200 mTorr, using 100 sccm of SF6 as a source of fluorine and 150 sccm
of CHClF2 (Freon® 22) to passivate the sidewalls and obtain better anisotropy. The
PDMS etch rate was rather low at approximately 100 nm/min, but it was sufficient that
the 30 µm PDMS film could be etched cleanly all the way down to the supporting
glass wafer in a few hours. This optimized recipe resulted in very good pattern fidelity
and near vertical channel sidewalls, as shown in Figure 15.
40
Figure 15: Example of dry-etched PDMS pattern fidelity. The optimized and highly anisotropic PDMS etching recipe is used to create a fluidic filtration grate with pillars that are 2 µm in diameter and ca. 10 µm tall (left). The PDMS is etched all the way down to the supporting glass wafer, and is later covered with another glass wafer to create a 10 µm high fluidic channel (bottom right).
After the completion of the dry etch, the photoresist was usually no longer
present, but the 150 nm aluminum hard mask was still in place. It was conveniently
removed by exposing the PDMS wafer to TMAH-based developer for about two
minutes. The PDMS was then thoroughly cleaned with acetone, methanol,
isopropanol, and extended sonication in distilled water. Following that, the PDMS
surface was first baked to remove moisture and solvent traces and then activated by
exposure to an O2 plasma in Drytek 1 (500 mTorr, 100 sccm O2, 85 W, 30 seconds).
In this condition, the PDMS surface was hydrophilic and ready for bonding to
another oxide surface which had been similarly cleaned and activated with an O2
plasma. Accurate alignment of the PDMS wafer to the spin-valve wafer was then
achieved on the Electronic Visions 620 Aligner using the wafer-to-wafer alignment
chuck and viewing the mating surface through the transparent PDMS and support
wafer. Final bond strength was achieved by compression and, optionally, by heating of
the wafer sandwich to 100°C for 2 hours. The entire fluidic wafer fabrication
procedure is illustrated in Figure 16.
41
1.) Spin Cast PDMS onto Wafer
• Use Dow Corning Sylgard® 184 PDMS (Polydimethyl Siloxane)
• Add isopropanol to reduce the viscosity for filtering and spin casting
• Use 20:2:3 of Base : Curing Agent : IPAby volume, mix thoroughly
• De-bubble in centrifuge, filter through 5 µm Supor®32 mm syringe filter
• Spin cast at 2000 – 3000 rpm, 45 seconds, thickness ca. 20 – 30 µm
• Heat cure at 150°C for 30 minutes (longer is also o.k., 120-160°C also o.k.)
Glass (Pyrex, Quartz) Wafer, 6” diameter
PDMS, 30 µm
3.) Coat Photoresist over Hard Mask & Develop
• Standard Photoresist process can be used
• Expose features with sizes of ca. 10 µm critical dimension or larger
• Develop as usual, but do not use any post-develop bake
• A post-develop bake will cause cracks in the resist and hard mask!
Glass (Pyrex, Quartz) Wafer, 6” diameter
PDMS 30 µm
Aluminum 150 nm
Resist Resist
5.) Transfer Pattern into Silicone Elastomer
• Dry Etch: 100 sccm SF6, 150 sccm CHClF2 (Freon 22), 200 mTorr, 105 Watts
• Photoresist will usually disintegrate after ca. 1 hour (hence use hard mask)
• Etch rate ca. 100 nm / min (5 hours), sidewall angle ca. 90°
Glass (Pyrex, Quartz) Wafer, 6” diameter
PDMS
Aluminum Aluminum
PDMS
SF6 Plasma
7.) Clean and Activate PDMS
• Solvent clean with Acetone, Methanol, IPA
• Sonicate in DI water for extended time
• Finally, activate PDMS with 30-second 100W RF O2 plasma
Glass (Pyrex, Quartz) Wafer, 6” diameter
PDMS PDMS
O2 plasma O2 plasma
2.) Sputter Al Hard Mask onto PDMS
• Ca. 150 nm of Al recommended
• Any sputtering method is acceptable
• Temperature is not a problem up to 300°C• Interesting effect: Aluminum film will
often form surface wrinkles on PDMS
• Wrinkles as described in J. Mech. Phys. Solids, ZY Huang, W Hong, Z Suo, 2005
Glass (Pyrex, Quartz) Wafer, 6” diameter
PDMS, 30 µm
Aluminum 150 nm
4.) Transfer Pattern into Hard Mask
• Ideally done by Ion Beam Milling for best pattern fidelity
• Alternative: Wet Etch with Aluminum Etchant or TMAH developer
Glass (Pyrex, Quartz) Wafer, 6” diameter
PDMS 30 µm
Aluminum
Resist Resist
Aluminum
Aluminum Etch
6.) Remove Hard Mask
• Immerse wafer in Aluminum Etchant to strip Hard Mask
• Alternatively, TMAH-based developer will also strip the Al Hard Mask
Glass (Pyrex, Quartz) Wafer, 6” diameter
PDMS PDMS
Aluminum Aluminum
8.) Align and Bond PDMS to Sensor Wafer
• Align PDMS features to sensor wafer
• Lower PDMS wafer into contact with sensor wafer
• Gently apply pressure to seal wafers to each other
• Heat to 100°C for 2 hours to enhance bond strength
Glass (Pyrex, Quartz) Wafer, 6” diameter
PDMS PDMS
6” Silicon Wafer with Sensors
S
. O
ster
feld
, S
tan
ford
Uni
vers
ity,
200
7
Figure 16: Microfluidic fabrication procedure.
42
The microfluidic channels created in this way are very durable. Since the
microfluidic channels are no longer just a surface feature on a much thicker PDMS
slab, but instead cut across the entire thickness of the 30 µm PDMS film, they can no
longer experience “roof collapse” if external forces were applied to the final
microfluidic device. Similarly, they are much less likely to leak than traditional soft
lithography microfluidics when the channels were pressurized. The transparent glass
wafer on which the PDMS layer is supported becomes a glass top window on the final
biochip. This means that the exterior of the biochip is both transparent and also nearly
as impervious as all-solid fusion-bonded glass microfluidics would be. Yet at the same
time one has all the conveniences associated with traditional soft lithography
microfluidics: The cold-sealing ability, and the compliance of PDMS, which can
accommodate non-planar substrate topographies, and which minimizes the impact of
slight particle contamination on the mating surfaces.
In theory it is even feasible to encapsulate biological molecules in the channels
by applying them first to a properly prepared substrate wafer and then cold-sealing the
PDMS without heat application over this functionalization chemistry. Of course, some
experimentation would be needed to ensure that the substrate wafer chemistry is both
suitable for surface functionalization and PDMS adhesion at the same time.
3.5. ALIGNMENT TOLERANT TWO-LAYER FLUIDICS
A further refinement of the thin PDMS fluidics is the creation of a two-layer
fluidic structure as shown in Figure 17. The PDMS is patterned on the support wafer
as usual, but in addition, a layer of ca. 200 nm thick SiO2 is fabricated on the sensor
wafer by lift-off patterning. This SiO2 layer contains short trenches, for example at the
location of the spin valve sensor, which are later covered by a section of the PDMS
layer. If designed properly, this effectively forms a localized fluidic channel
constriction above the sensor. This is useful if the flow of the analyte needs to be
constricted above the sensor for sensitivity reasons.
43
Sensor Wafer (silicon)
PDMS Support Wafer (glass)
PDMS PDMS PDMS
SiO2 SiO2Sensor
Large Channelin PDMS
Small Channel in SiO2
Figure 17: Two-layer fluidics. Two large microfluidic channels are patterned in the 20 µm thick PDMS layer. They are joined through a much smaller channel which is patterned in a 300 nm thick SiO2 layer on the substrate wafer. This way a channel constriction is created above the sensor.
Such a structure failed to work with thick PDMS layers of traditional soft
lithography, which would sag and close the constriction, but with the rigid-supported
dry-etched thin PDMS it worked as designed. The benefit of fabricating only a small
local constriction with a 2-layer fluidic architecture is that the overall flow impedance
of the fluidic circuit is much lower, and the overall flow rate and clogging resistance
greater, than if the fluidic circuit had been implemented exclusively in the 200 nm
SiO2 layer.
Another benefit of the two-layer microfluidic structure is that the trenches in
the PDMS and SiO2 layers can be designed to have generous overlap in those areas
where the fluid switches from one layer to the other, thereby making the accurate
alignment of the PDMS to the SiO2 layer less critical. Lastly, the finest, most
alignment-critical channels can be defined exclusively in the SiO2 layer, which as a
standard process will have even better alignment to the substrate than the PDMS layer.
A provisional patent application for this method was filed on Dec. 10, 2004,
eventually resulting in the issuance of US Patent 11388223.
44
Figure 18: Three generations of fluidic interconnect technology. Left: A prototype fluidic-only single-channel chip with edge connections that interface with an aluminum manifold. Center: An early single-channel fluidic biochip with edge connections that interface with cast epoxy manifolds. Right: The latest design, an 8-fluidic-channel biochip which has fluidic inlet/outlet ports on the backside.
3.6. PACKAGING AND FLUIDIC CONNECTIONS
One of the most challenging aspects of the microfluidic chip development was
the fabrication of the fluidic connections, through which the microfluidic channels can
be connected to the macroscopic world. For a durable, rugged chip which could
potentially be mass-produced, such connections had to withstand rough handling, high
fluid pressures, high temperatures, and a variety of chemicals. This alone is a
challenging task, but the greatest challenge lies in making such a fluidic connection to
the chip in a very small form factor. After all, silicon real estate is expensive, and as
little as possible should be wasted on interconnects, be they electrical of fluidic in
nature. None of these important concerns are usually addressed in traditional soft
lithography microfluidics.
45
Figure 19: Photo of wafer-level PDMS microfluidics fabrication. The last step of the whole-wafer microfluidic fabrication procedure consists of accurately aligning the completed spin valve sensor wafer (left) to the PDMS-coated glass wafer. This can be done in a regular wafer alignment machine. The aligned wafers are then brought in contact to form a wafer sandwich (right) which then needs to be sawed into multiple individual devices.
Drilling holes in the PDMS support wafer, which would have been equivalent
to the traditional soft lithography approach where holes are simply punched into the
PDMS, was not possible. Such holes would have to be drilled prior to PDMS spin
casting, which would have rendered a good spin casting film nearly impossible.
Alternatively, drilling such holes into the support wafer after spin casting would most
likely ruin and contaminate the fine PDMS structures. A different approach had to be
found.
A further complication was the fact that the fluidic channels were completed
before the wafer had been diced into individual chips. As shown in Figure 19, the last
step of the fluidic wafer fabrication is the alignment and bonding of the sensor
substrate wafer to the PDMS supporting wafer. After this step, the wafer sandwich
needs to be diced into individual chips on a wafer saw, which is a dirty process that
could easily contaminate the chip and render it useless if the cooling water and
sawdust should accidentally enter the fluidic channels. For this reason the wafersaw
must not cut into or open any of the fluidic channels.
46
1.) 2.) 3.) 4.)
PDMS
PDMS Support Wafer (glass)
Sensor Wafer (silicon)
SiO2
Partial cut
PDMS
PDMS Support Wafer (glass)
Sensor Wafer (silicon)
SiO2
Insert blade and twist edge off
PDMS
PDMS Support Wafer (glass)
Sensor Wafer (silicon)
SiO2
Microfluidic opening
PDMS
PDMS Support Wafer (glass)
Sensor Wafer (silicon)
SiO2
Epoxy
Inlet / Outlet
Microfluidic Manifold
Figure 20: Schematic illustration of “snap-off edge” fluidic interconnects. This was the earliest method of making strong connections to the glass-topped PDMS fluidics. It only requires that a partial cut be made in the PDMS support wafer at the right location. This can be conveniently done prior to actual wafersawing.
3.6.1. SNAP-OFF EDGE FLUIDIC CONNECTIONS
On earliest generation fluidic biochips, the channels terminated near the edge
of the chip under a section of the PDMS support wafer which could later be removed
to reveal the channel entrance. This entirely eliminated the need for drilling or etching
holes into the wafers. To make a section of the PDMS support wafer removable, a
partial cut was made across the biochip with the wafersaw, deep enough to cut most of
the way through the PDMS support wafer, but not deep enough to cut into the sensor
wafer. This formed a designed breakpoint which could be opened when access to the
fluidic channel was needed. To make a permanent fluidic connection to the channel,
these partially removable chip edges were opened in a clean environment, and then
sealed to a matching manifold with a carefully metered amount of epoxy resin.
The “snap-off edge” fluidic connection concept is illustrated in Figure 20 and
shown in actual use in Figure 12 and Figure 18. Several fluidic chips were made with
such connections, which were sturdy, worked as intended, and held up well to high
pressures.
47
A
Chip Carrier
In Out
Contact Contact
Epoxy
PDMS Fluidics Rigid Cover (Glass)PDMS
Biochip
B
PDMS
Biochip
Chip Carrier
In Out
Contact Contact
PDMS Fluidics Rigid Cover (Glass)
Wirebond
Figure 21: Schematic illustration of backside port fluidic interconnects. This was the later method of making fluidic connections to the biochip. It requires a special chip carrier with drilled holes that line up with the etched backside holes in the biochip. The chip is glued to the carrier with adhesive epoxy. To make sure that the epoxy doesn’t enter the fluidic channels, it is usually best to apply the epoxy indirectly, i.e., by placing the chip in its correct position and letting the epoxy wick (by capillary forces) under the chip from the side.
It could be argued that such fluidic connections at the chip edge are potentially
mass-producible: With the appropriate amount of epoxy resin applied around the
microfluidic chip opening, assembly simply consists of placing the chip into the
microfluidic manifold. However, the most significant drawback to this method is that
despite the relatively small footprint – estimated at ca. 12 mm2 per fluidic connection
– there is only enough room for two of these connections on the 120 mm2 size biochip.
Unless the chip size was increased, this packaging method would yield just a single
microfluidic channel.
3.6.2. BACKSIDE PORT FLUIDIC CONNECTIONS
Another fluidic connection approach was therefore soon developed to achieve
a larger number of fluidic ports. It was based on backside fluidic connections etched
into the biochip, and required a chip carrier with pre-drilled holes that would line up
with the holes in the chip. The concept is relatively simple and illustrated in Figure 21.
This method requires that access holes are etched into the sensor wafer backside at the
end of its manufacturing cycle with deep reactive ion etching (see Figure 19 for a
wafer with such holes).
48
Care must be taken that the spin valve sensors are not exposed to the etching
gases in this step, however this is usually helped by the native oxide of the spin valve
wafer which, when properly timed, can serve as an etch stop just before the hole
breaks through to the sensitive frontside of the wafer. The remaining oxide film which
formed the etch stop is so thin that it is easily blown out with compressed air or a brief
sonication after the etch. Alternatively, the frontside of the sensor wafer can be
temporarily protected with another wafer during the port etch step.
During wafer sawing, the backside ports of the chips are sealed shut by the
wafer sawing tape. This prevents contamination with sawing debris. To assemble such
a chip, the wafer sawing tape is removed, and the chip is aligned to a chip carrier with
matching pre-drilled holes. The chip is then glued to this carrier, preferably by letting
the epoxy glue wick under the chip, i.e., letting it fill a tiny air gap between the chip
and the carrier by capillary forces, so that only the required amount of epoxy is drawn
in. If done properly, this technique results in no epoxy entering the fluidic pathways.
This worked actually quite well with a gold-coated ceramic chip carrier and fluidic
holes that were ca. 0.8 mm in diameter, and it is also feasible that this step could be
automated with precise epoxy metering.
To complete the biochip, stainless steel tubing segments (needle gauge 21)
were press-fitted into the holes in the ceramic chip carrier and potted with a generous
amount of epoxy resin. After this procedure, the chip can be wirebonded as usual, with
the wirebonds being encapsulated in epoxy afterwards (see Figure 21 and Figure 22).
The result is an extremely well-packaged fluidic biochip which can withstand a lot of
mechanical, thermal, chemical, and hydraulic abuse. This is a very significant
improvement over traditional soft lithography PDMS microfluidics, which often are
used with minimal or no mechanical reinforcement, and which therefore are extremely
sensitive to mechanical disturbances or slight fluidic overpressure, which in traditional
soft lithography fluidics quickly causes leaks or de-bonding of the PDMS from the
sensor chip surface.
49
Figure 22: Fluidic spin-valve sensor biochips with backside port connections. On the backside each chip has twelve stainless steel tubes, needle gauge 21, supported in a socket of clear epoxy, which connect to the fluidic channels of the biochip. These fluidic ports are extremely robust and yet occupy only minimal real estate on the chip. The biochip itself is also very robust, with the microfluidic channels being protected by a strong 0.5 mm thick glass cover (formerly the PDMS support wafer).
5 mm
30 mm
50
Air Flow Carries Fluid to DrainAir Flow Carries Fluid to DrainATM VACATM VACATM VAC
3 mm 500 µm
Figure 23: Fluidic layout of the 8-fluidic-channel biochip. Each of the eight channels has an individual inlet, but the outlet ends in a shared drain channel which is open to the atmosphere at one end and attached to a vacuum at the other end. The constant stream of air in this drain channel carries the reagents to a waste carboy.
In addition to their great mechanical strength, the fluidic backside ports are
also much more densely packed. Compared to the snap-of edge fluidic connections,
the backside ports have a 4x smaller footprint, and require ca. 3 mm2 of chip real
estate each. While it would have been possible to accommodate 16 fluidic backside
ports on the existing biochip, which would have been enough for eight completely
isolated fluidic channels, the actual design used only 12 ports as shown in Figure 23.
By using a shared drain line, the waste collection from the chip is simplified and the
required number of fluidic ports is slightly reduced. This design could also have
operated in reverse, flowing a reagent from one port to several of the fluidic channels.
This would have made it possible to functionalize (or program) each fluidic channel
with an individual chemistry first, after which an analyte could have been delivered to
all channels at once to run multiple analysis tasks in parallel.
A compartmentalization of separate analysis tasks makes sense if the analysis
chemistries would interfere with each other if they were in direct contact. This actually
is frequently the case with protein detection chemistries if multiple analytes are to be
detected simultaneously. Beyond a certain level of multiplexity, protein detection
assays will most likely benefit from compartmentalization.
51
200 µm
30 mm
Figure 24: Face-down microfluidic biochip with backside ports during measurement. Close-up of microfluidic channels made by dry etching PDMS, as seen through clear glass top of the spin valve biochip (right). The channels are approximately 120 µm wide and 30 µm high.
3.7. MICROFLUIDIC MEASUREMENTS
Several measurements were carried out with the microfluidic chips. In a typical
setup, the biochip was inserted face down into the measurement socket inside a
Helmholtz coil as shown in Figure 24. Just two tubes were connected to the chip: One
tube from the chip’s waste drain channel (see Figure 23) to a vacuum pump with a
waste carboy, and the other tube from a particular fluidic channel inlet to a syringe
pump which had been loaded with a few microliters of the desired reagent(s). This of
course meant that only one of the eight fluidic channels on the chip would be utilized,
while the other seven fluidic channels would remain empty until they were utilized in
a later experiment. This one-by-one measurement of fluidic channels was both a
demonstration of the chip compartmentalization – now eight independent experiments
could be run on one chip, even at different times – and it was also necessitated by the
fact that in 2007 the electronics were only able to record four (freely selectable)
sensors of the 64 sensor array.
To avoid switching valves and dead volumes, the reagents were sequentially
loaded into the syringe pump, so that a sequential flow assay could be performed.
Long air bubbles were used to separate the reagents and reduce the intermixing of
reagents as they traveled through the tubing.
52
First Microfluidic Signal from Magnetic Particles - Real Time DataCa. 5 microliters of Ferridex Iron Particles, May-5 -2007
-40
-20
0
20
40
60
80
100
120
10 15 20 25 30
Time, Minutes
Sig
nal A
mpl
itude
, µV
Sensor 1
Sensor 2
Sensor 3
Sensor 4
Ferridex FlowAir Air
BiotinBSA Test Assay - Real Time Data2kΩ Fluidic Chip SJO7-WD3-8-2, July-02-2007
-10
0
10
20
30
40
50
0 5 10 15 20 25 30 35 40
Time, Minutes
Sig
nal A
mpl
itude
, µV
Sensor 1
Sensor 2
Sensor 3
Sensor 4
Initial Value
Final Value - MACS
Final Value - Water
Flowing MACS Nanoparticles Flowing DI WaterAir
Figure 25: First microfluidic measurements. This experiment verified that a highly concentrated ferrofluid could be detected as it was pumped through the fluidic channel (left). Later experiments demonstrated specific binding of streptavidin-coated nanoparticles to a biotinylated channel (right).
The first fluidic measurements were designed to simply deliver a uniform, non-
specific stimulus to the spin valve sensors via the fluidic channels, which should
ideally elicit an identical response from all sensors that doesn’t depend on chemistry.
For this test, Ferridex® magnetic nanoparticles, which are highly concentrated iron
oxide particles and usually marketed as a non-gadolinium MRI contrast agent, were
pumped at a rate of 1 µL per second through a channel of the biochip. Four sensors,
which was all that the electronics could monitor concurrently at that time, were
recorded. An approximate step function in the signal level to 85 µV was observed,
coinciding with the Ferridex® nanoparticles entering and leaving the sensor area, as
shown in Figure 25a. The step function shape was an indication that no accumulation
was occurring, i.e., there was no settling and continuous addition of particles to the
sensor, instead the signal was mostly just caused by bulk of the Ferridex® particles.
This is in contrast to MACS particles, which are dilute enough that the bulk MACS
fluid does not cause a signal, rather, only the adsorbing and accumulating MACS
particles generate a signal. Also, as the Ferridex® fluid was pushed out of the fluidic
channel by air, a thin layer of the magnetic fluid remained on the channel walls, as can
be seen by the fact that the signal settles down at roughly 16 µV instead of returning to
its initial value (zero) after the bulk of the fluid has passed.
53
In Figure 25b, a specific nanoparticle binding experiment is shown. Prior to the
measurement, the fluidic channel had been functionalized by first flowing a 2%
polyethyleneamine through the channel, followed by a solution of biotinylated BSA,
which will adsorb onto the entire channel surface and the spin valve sensors.
Streptavidin-coated MACS nanoparticles are entering the fluidic channel at t = 7
minutes and start adsorbing and accumulating on the sensor and channel surfaces due
to the strong biotin-streptavidin affinity. The resulting signal curve shows a very
typical adsorption behavior, with a rapid signal rise in the beginning, which eventually
slows down and levels off as the number of available binding sites approaches an
equilibrium value. Such binding curves appear in many surface adsorption processes
and can be described as a Langmuir adsorption isotherm, or more precisely, as a two-
compartment adsorption process46,47,48.
The specificity of the binding reaction in Figure 25b is corroborated by the
high binding strength, which results in a stable signal steady even as the bulk of the
MACS magnetic nanoparticles is flushed out by air at t = 28 minutes, and furthermore
as the sensor is washed with distilled water beginning at t = 31 minutes. Also
encouraging is the relative similarity of the signals reported by the four recorded
sensors. At the time of this experiment, open-well measurements of multiple sensors
did not usually achieve the same level of uniformity as this microfluidic measurement,
which suggested that the use of microfluidics could indeed make measurements more
reproducible.
54
3.8. MICROFLUIDICS CONCLUSION AND SUGGESTED FUTURE WORK
The microfluidic fabrication method presented in this work has reached a level
of maturity which permits routine fabrication of very durable microfluidic structures
even on thermally and chemically sensitive devices such as magnetic spin-valve
biochips. The microfluidic fabrication method is entirely implemented on a wafer
level and could be scaled up for mass production. Sturdy high-density fluidic
interconnects, compatible in principle with automated assembly tools, were also
developed. The overall result is a highly compatible manufacturing route for fully
integrated microfluidics, which could be automated to mass-produce microfluidic
biochips which are robust enough to withstand rough handling e.g., outdoors in a
portable device.
However, it is interesting to note that the basic spin-valve biochip, which had
been developed and improved concurrently with the microfluidics, took on a much
larger role in actual assay experiments than the microfluidic version of the same chip.
In fact, assaying experiments carried out on an open-well version of the same basic
chip became so successful (see Chapter 4, Assay Results) that the microfluidic
development eventually slowed down. Why was this the case? The most important
reason might be ease of use and flexibility in handling. For example, several open-well
biochips chips can be quickly sealed with caps and placed in a refrigerator for several
hours. With a microfluidic chip, one would have to seal at least two connections
without accidentally purging the tiny fluidic channels, or one would have to place the
entire fluidic pump and the chip into the refrigerator. Similarly, every reagent
exchange on a microfluidic chip requires loading and programming the fluidic pump
and connecting the chip, while on the open-well chip the same step can be done
directly with a pipette. Lastly, the open-well chip permitted direct access to the
individual sensors. This is a great benefit for individual-sensor biofunctionalization
(e.g., with robotic contactless spotting) and sensor inspection by optical and electron
microscopy.
55
It appears that the success of the open-well chip is also partially due to the
demands of the ongoing development work, where many parameters are frequently
changed. In such an environment, direct manipulation and flexible handling are more
important than a high degree of automation. Automation requires a great deal of time
to be set up properly, and hence only becomes viable when all design parameters have
been thoroughly optimized and locked in. Additionally, while the microfluidic spin-
valve biochip was potentially suitable for automatic assaying, in actuality it was still
operated in a rather manual fashion: All reagents were manually and sequentially
loaded into a syringe pump – which clearly was far from an automatic process – which
was then connected to the biochip and slowly emptied. This way, the microfluidic
biochip resulted in added complexity without actually reducing the researcher’s
workload – yet.
To better utilize the assay automation potential of a microfluidic biochip, one
would need to develop a sufficiently capable reagent delivery system, which might,
for example, incorporate several multi-position valves to select and deliver various
reagents to the biochip(s) on demand. Similarly, it would be desirable to develop a
means of automatically making and breaking the fluidic connections between such a
reagent delivery system and various fluidic biochips under test. An alternative would
be to develop an on-chip miniaturized reagent handling system, which consisted of
microfluidic valves and on-chip pumps – no small task indeed, especially if true
autonomy from external machinery is the goal.
Furthermore, it seems highly desirable to combine individual-sensor
biofunctionalization with microfluidics. This is not easy: If the microfluidics are
completed first, then one no longer has direct physical access to individual sensor
surfaces. If, on the other hand, the sensors are coated with individual probes first and
then covered with microfluidics, then the additional limitations placed on
manufacturing and assembly are extremely restrictive. In such a case, mechanically
sealed macrofluidics, such as a small flow chamber sealed with an O-ring, are
probably more suitable than bonded mirofluidics. The downside is that mechanically
56
sealed macrofluidics would most likely be bulkier and less precisely aligned than
prefabricated microfluidics, so that high-density fluidic compartmentalization might
no longer be possible.
Lastly, it also appeared that a slow corrosion process was limiting the useful
shelf life of the fluidic biochips after fabrication, apparently due to a liquid which was
slowly precipitating inside the microfluidic channels. This liquid was probably either
formed or already present in the PDMS at the end of manufacturing and slowly
released over the course of several weeks. Possible candidates are traces of TMAH
developer, uncured PDMS precursors, water, or sulfur-containing residues formed in
the SF6-based dry etching process, which occasionally produced PDMS with a faintly
noticeable hydrogen sulfide smell. To resolve this issue, the microfluidic fabrication
method should be varied to evaluate a purely CF4-based dry etching chemistry. Other
possible solutions may be the elimination of direct contact between TMAH and
PDMS, and using other more fully cross-linked PDMS polymers such as h-PDMS49,50.
57
CHAPTER 4. ASSAY RESULTS
In the development of the magnetic biochip, an essential challenge was to
optimize the chip and the assay chemistry concurrently: Without a working chip the
assay chemistry can’t be tested, but without a proven chemistry the chip is difficult to
evaluate. To facilitate this interdependent optimization task without the added
complexity of microfluidics, many assay experiments were carried out on an open-
well magnetic biochip of the type shown in Figure 26. A low viscosity two-component
epoxy (EP5340, Eager Plastics, Chicago, IL) was chosen to encapsulate the wirebonds
and attach the reagent well (Tygon® tubing, 1/4” ID x 3/8” OD, 6 mm long) to the
chip.
The chip itself was supported on a ceramic 84-pin chip carrier (LCC08423,
Spectrum Semiconductor Materials, San Jose, CA). A ~0.5 mm layer of the same
epoxy was used to mask some of the sensors. The masked sensors, no longer able to
detect nanotag binding, would serve as electrical signal references. The electronic
signal generation method based on an AC current source was implemented by Shu-Jen
Han18 and capable of recording only four differential signals (sensors) in actual
experiments – a further complication when unproven assays need to be tested on
initially unreliable chips.
To accelerate the early assay development, simple specific binding
experiments were carried out on dummy chips, which were then inspected in a
scanning electron microscope as shown in Figure 27. The question was whether a
reliable correlation could be observed between varying the concentration of a dummy
analyte, such as a biotinylated antibody, and the final density of streptavidin-coated
nanoparticles on the chip surface. The MACS nanoparticles were always applied at the
same (stock) concentration and should, ideally, bind to the surface only as facilitated
by the analyte.
58
AC Current Source
500 Hz
MR
Sensor
208 Hz
AC Field
80 Oe
AM Modulated
Voltage
Ht
CoilRef.
Differential
Pre-Amplifier
FFT
208 292 708500Hz
µV
SB SB
CT
Figure 26: Photo of open-well biochip and signal generation schematic. The chip has a 200 µL reaction well and is supported by an 84-pin ceramic base (A). At the bottom of the reaction well are 64 sensors in an 8x8 array, some of which are covered with epoxy to provide a reference signal (B). Each sensor has an active area of roughly 90x90 µm2 and consists of 32 linear spin valve segments, each 1.5 µm wide, which are connected in series (C). The signal generation method is shown for a single sensor in (D), where the sensor modulates an alternating 500 Hz current at 208 Hz. A nanoparticle binding event will increase the AM sidebands at 292 Hz and 708 Hz, which are recorded with 1 Hz bandwidths. A reference sensor signal subtraction at the differential amplifier helps reduce drift artifacts.
59
1000 ng / mL 100 ng / mL
10 ng / mL 1 ng / mL
Figure 27: Nanoparticle coverage image from scanning electron microscope. Before the chip and the assay chemistry were working reliably, assays were sometimes evaluated from the magnetic nanoparticle coverage, which can be assessed in a scanning electron microscope (SEM). In this series of images, varying the concentration of biotinylated antibody did change the final nanoparticle coverage accordingly, which demonstrates that the nanoparticle binding is specific and mediated by the adsorbed analyte. However, due to the small field of view and the irregular, SEM-setting dependent appearance the nanoparticles, this method is only qualitative.
60
Probe Control Probe Control
c MNT-based Analyte Quantification
IFN-γ (Probe) Biotinylated Anti-IFN-γ (Analyte)IL6-sR (Control) MACS Nanotags
b Analyte Incubation
IFN-γ IL6-sR
Probe Control
a Sensor Functionalization
d Select Real-Time Data from 3 Chips Combined e Results
GMR Sensor
Concentration of Anti-IFN-γ in 1x PBS Buffer
67 pM 670 pM 6.7 nM
Probe Sensor
Control Sensor
0
10
20
30
Time, minutes
∆ S
ign
al, µ
Vrm
s
∆ S
ign
al, µ
Vrm
s
0 5 10 15 20-5
Magnetic Nanotags in PBSAirPBS
0
10
30
20
6.7 nM
670 pM
67 pM
6.7 nM control
10
5.4
25
0.4
10
5.4
25
0.4
Figure 28: Direct binding interferon-gamma assay. This was one of the earliest successful protein assays. Three biochips were used to generate this data, with four sensors being recorded on each chip, for a total of twelve datapoints as shown in the bar graph (May 2006).
4.1. DIRECT-BINDING ASSAY FOR INTERFERON-GAMMA
One of the earliest successful protein assays used a relatively simple assay
chemistry, where the analyte is specially prepared, biotinylated anti-IFN-γ, which is
captured by IFN-γ functionalized sensors and quantified with streptavidin-coated
magnetic nanotags as shown in Figure 28. Interferon gamma is an inflammatory
marker, which for example is released by lymphocytes during an immune system
response. This permits ex-vivo immune response tests, e.g., to reveal past exposure to
tuberculosis51.
Three spin valve biochips were first thoroughly cleaned with acetone,
methanol, and isopropanol, and then further cleaned with a brief exposure to oxygen
plasma. To establish an anchoring layer for the biological surface functionalization,
the chips were exposed to a 2% polyethyleneimine (PEI) solution in water for two
minutes, followed by rinsing with deionized (DI) water, blow-drying with compressed
61
nitrogen, and baking on a 100°C hotplate for ten minutes. On all three chips, the
exposed (not epoxy-coated) sensors were divided into two groups, a “probe” and
“control” group. The probe sensors were identically functionalized with a 1 µL droplet
of IFN-γ, 100 µg/mL in PBS buffer. Control sensors were functionalized with a 1 µL
droplet of IL6-sR, 100 µg/mL in PBS buffer (Figure 28a). After incubation for 30
minutes at 4 °C the chips were washed with a 1% BSA in 1x PBS buffer solution to
block any remaining non-specific adsorption sites.
The three chips were then incubated for 1.5 hours at 30°C with 100 µL of
different concentrations (0.067, 0.67, and 6.7 nM, i.e., 10, 100, and 1,000 ng/mL) of
analyte, consisting of biotinylated anti-IFN-γ in PBS buffer (Figure 28b). The chips
were then rinsed with 0.1% BSA in TPBS and transferred to the measuring station for
subsequent analyte quantification. To quantify the amount of analyte captured on the
spin valve sensors of a particular chip, the chip well was filled with 100 µL of
Miltenyi MACS nanoparticle stock solution, and the developing nanoparticle binding
signal was recorded (Figure 28c and Figure 28d). The nanoparticle binding signals
after 20 minutes were taken as a measure of analyte concentration (Figure 28e).
The probe sensors, functionalized with IFN-γ, developed strong signals, which
approximately doubled with every 10x increase in analyte concentration. Yet at the
same time, the control sensors had near zero signal, which was a strong indication that
both the binding of the analyte to the probe, and the binding of the nanoparticles to the
analyte, was highly selective. Achieving the required high cumulative specificity of
these two dependent adsorption steps together with proper chip functionality were
significant milestones.
The small error bars in Figure 28e indicate the electrical noise of the
measurement, which is much smaller than the variance of signals from identically
functionalized sensors in this experiment. The sensor-to-sensor variance, which can
stem from local irregularities in the surface chemistry and from physical sensor
variations, is examined in more detail in Chapter 5 - Optimization And
Characterization.
62
Probe Control
c Linker incubation
Probe Control
d Nanotag-based quantification
Analyte Biotinylated AntibodyBSA
Streptavidin-coated Magnetic Nanotag
Capture Antibody
Probe Control
b Analyte incubation
Capture Antibody BSA
Probe Control
a Sensor functionalization
Magnetoresistive Sensor
Figure 29: Schematic illustration of magnetic label sandwich immunoassay. This figure outlines the use of a sandwich immunoassay for highly sensitive protein quantification with magnetic nanoparticles. Probe sensors are functionalized with capture antibodies specific to the desired analyte, while control sensors are functionalized with a non-matching antibody or protein such as 1 wt% BSA solution (A). During analyte incubation, the probe sensors capture a fraction of the analyte molecules (B). A biotinylated linker antibody is subsequently incubated which binds to the captured analyte (C), and which provides binding sites for the streptavidin-coated magnetic nanoparticles. Streptavidin-coated magnetic nanoparticles are then incubated (D), and the binding signal, which quickly saturates at an analyte concentration-dependent level, is used to quantify the analyte concentration. Image source: Original work by S. Osterfeld. First published in Osterfeld, S.J. et al. “Multiplex protein assays based on real-time magnetic nanotag sensing.” Proceedings of the National Academy of Sciences 105, 20637-20640 (2008).
4.2. SANDWICH ASSAY FOR INTERFERON-GAMMA
One disadvantage of the direct-binding assay (described earlier) is that it
required an analyte which is already biotinylated. This is avoided in a sandwich assay
as shown in Figure 29 where an unmodified analyte is sandwiched between two
specific antibodies, one which anchors the analyte to the sensors, and the other of
which provides a biotin site for magnetic labeling. In addition to being able to quantify
analytes in their natural state, sandwich assays also usually outperform direct-binding
63
assays in terms of sensitivity and specificity52. This is probably due to the fact that the
analyte incubation, which due to the low concentration of analyte is often the
throughput-limiting step of the assay, can occur faster and more specifically with the
higher diffusion speed of a small, unlabeled analyte. For example, the IFN-γ analyte in
the sandwich assay (Figure 29b) has a molecular weight of ca. 17 kDa, whereas the
biotinylated antibody analyte in the direct assay (Figure 28b) has a molecular weight
of ca. 150 kDa.
After cleaning and PEI coating, several chips were prepared with two
functionalizations: 500 µg/mL Anti-IFN-γ for the probe sensors, and 1 wt% BSA
solution for the control sensors (Figure 29a). Different concentrations of analyte (0.64,
1.9, 6.4, and 64 nM IFN-γ in PBS) were incubated on different chips at room
temperature for one hour (Figure 29b). Following a rinse with 0.1 wt% BSA in PBS,
the chips were then incubated for one hour at room temperature with 100 µL of a
linker antibody solution (Figure 29c), consisting of biotinylated anti-IFN-γ, 2 µg/mL
in PBS. The chips were then rinsed again with 0.1 wt% BSA in PBS, and transferred
to the measuring station for quantification with magnetic nanoparticles (Figure 29d).
Analyte quantification with magnetic nanoparticles was then carried out
according to a precisely timed protocol as shown in Figure 30. After the chip has been
primed a few times with PBS buffer and the stability of the signal baseline has been
confirmed, at time t = 15 minutes, 100 µL of undiluted nanoparticle solution (Miltenyi
MACS, part # 130-048-102) are delivered to the reaction well of the chip and
incubated for 20 minutes at room temperature. At the end of the 20 minute
nanoparticle incubation period, excess particles are removed, and the well is twice
washed with deionized water for 1 minute each. This rinsing step with DI water
removes any salt residues and thereby enables a later assessment of the nanoparticle
coverage in the scanning electron microscope (SEM).
64
3.6939.2345.1449.19∆ Signal with Wash
4.1040.7542.6552.33∆ Signal w/o Wash
Sensor 4: BSA 1%Sensor 3: Anti-IFN-γSensor 2: Anti-IFN-γSensor 1: Anti-IFN-γ
3.6939.2345.1449.19∆ Signal with Wash
4.1040.7542.6552.33∆ Signal w/o Wash
Sensor 4: BSA 1%Sensor 3: Anti-IFN-γSensor 2: Anti-IFN-γSensor 1: Anti-IFN-γ
IFN-γ Detection Assay - 1 µg/mL (59 nM)Chip RB3-7-3, June-09-2006 - Raw Data Minus Initial Value
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BSA 1%
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Signal w/o Wash - End
Signal with Wash - Start
Signal with Wash - End
VA
C MACS Nanoparticles in PBS
H2O
H2O
PB
S
PB
S
PB
S
PB
S
Figure 30: Example of real-time data from IFN-γ sandwich assay quantification. In minutes 4 to 15 some fluid cycling is performed to wash the chip and verify that it doesn’t respond excessively to wet/dry cycling alone. Magnetic nanoparticles were incubated from t = 15 to 35 minutes.
As shown in Figure 30, the final signal can be assessed in two ways, either at t
= 35 minutes, i.e., before the nanoparticle removal and washing procedure, or at t = 42
minutes, i.e., after washing. Since the probe-to-control signal ratio with washing was
not consistently better than the signal without washing, and for matters of
convenience, most assays were soon quantified without a final washing step.
However, the signal levels after the final rinses with DI water are more appropriate for
correlation with nanoparticle coverage densities in SEM images, because such images
can only be obtained on DI-cleaned chips.
Overall the sandwich assay worked as expected, with signals that scaled
reasonably well with the analyte concentration as shown in Figure 31. Occasionally,
however, inexplicable variations were observed, such as for example in Figure 30,
where only two out of three identically functionalized sensors agree well, while a third
sensor reports a 52 / 42 = 24% higher signal.
65
Time, minutes
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50
10
20
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Magnetic Nanoparticle Labels in PBSAirPBS
59 nM Control
(adjacent to probe)
0.59 nM
1.8 nM
5.9 nM
59 nM
Figure 31: Analyte concentration determines the nanoparticle binding curves. This figure shows real-time magnetic nanoparticle binding curves from different IFN-γ sandwich assay chips combined into one figure. The rate of nanoparticle binding is initially rapid, but slows down after a few minutes as the available binding sites are being saturated. Binding site saturation occurs fastest when few binding sites exist to begin with, e.g., when the analyte was only present in a low concentration. Roughly 90% of the specific nanoparticle binding occurs in the first five minutes (June 2006).
Such variations could not always be explained, but were occasionally found to
be the result of experimental error, such as when accidental merging and cross-
contamination of the functionalizations occurred during their incubation step (Figure
29a). Another likely source of variability is uneven drying of these small
functionalization droplets. The resulting protein deposition may be uneven due to
evaporation and convection inside the droplet – this is akin to fluorescent arrays where
DNA spots often take the form of rings, rather than evenly filled circles.
The IFN-γ sandwich assays were the first successful detection of a native (non-
biotinylated) protein analyte on the spin-valve biochips, which is a significant
milestone in actual utility. The sandwich assay chemistry increased the complexity of
the assay over the direct-binding assay by adding another instance of specific
adsorption, for a total of three specific binding events which all need to preserve
specificity in order to obtain good results: First, the specific binding of the analyte
only to the correct capture antibody (Figure 29b). Secondly, the specific binding of the
biotinylated linker only to the captured analyte (Figure 29c). Lastly, the specific
binding of the magnetic nanoparticles only to the biotinylated linker (Figure 29d).
66
MagArray4 Sandwich Assay - Signal vs. IFN- γ ConcentrationHomogeneous Assay - Datapoints Taken Before Washing
20.3
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Chip & IFN- γ Concentration
Sig
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Anti-IFN-γAnti-IFN-γAnti-IFN-γBSA 10%
1.2 ± 0.3 µV
1.8 ± 0.8 µV
Negative, Average ± Std.Err.
~10.8 (~20.7 dB)13.4 ± 0.9 µVSERUM
~10.2 (~20.2 dB)18.9 ± 2.3 µVBUFFER
Pos. Sig. / Neg. Sig. (dB)Positive, Average ± Std.Err.
1.2 ± 0.3 µV
1.8 ± 0.8 µV
Negative, Average ± Std.Err.
~10.8 (~20.7 dB)13.4 ± 0.9 µVSERUM
~10.2 (~20.2 dB)18.9 ± 2.3 µVBUFFER
Pos. Sig. / Neg. Sig. (dB)Positive, Average ± Std.Err.
BUFFER SERUM
Figure 32: IFN-γ sandwich assay in PBS buffer and in 50% serum compared (June 2006).
4.3. SANDWICH ASSAY FOR INTERFERON-GAMMA IN 50% SERUM
One concern that was raised in results to the successful sandwich assay was
that the results had been obtained with a single analyte in plain PBS buffer. While
such an experiment is a good proof of concept, real assays would need to quantify
analytes in blood serum, which contains vast numbers of other proteins, many of them
in very high concentrations. A reasonable expectation is that these proteins interfere
with the sensor or the detection chemistry in a way that makes measurements in serum
perform much worse than measurements in PBS buffer.
The IFN-γ detecting sandwich assay was therefore duplicated in both PBS and
50% human blood serum (balance PBS). In all cases IFN-γ was added to the samples
to establish an analyte concentration of 1.8 nM (30 ng/mL). Two measurements were
carried out with PBS, and three measurements with 50% serum. The results are shown
in Figure 32. The serum-based assays, on average, resulted in IFN-γ signals which
were ca. 29% lower than in PBS buffer. However, the signal from the control sensors
was also lowered by 33%, so that the overall probe to control signal ratio was similar.
67
MagArray Standard Curve - 16 Sensor Median ± 1 StDe vHCG in Serum; Diluted to 1:1 with PBS buffer for me asurement
y = 11.453x0.1981
R2 = 0.9918
10
100
1 10 100 1000 10000 100000
HCG Concentration (in serum, before dilution), mIU/ mL
Sig
nal,
mic
rovo
ltsMACS + 2xAmp
Nonlinear Regression
S17
S18
Figure 33: Signal as a function of hCG concentration in 50% serum. The standard curve was obtained from samples provided by the National Cancer Institute. Two unknown samples S17 and S18 were also measured and interpreted as 8-12 and 500-700 mIU/mL, respectively. Note the exponent of the nonlinear regression (0.1981), which indicates that a ca. 10^5 increase in analyte concentration would result in a ca. 10x larger signal.
4.4. STANDARD CURVE FOR HCG IN 50% SERUM
One of the most carefully planned and executed magnetic biochip assays
performed in the scope of this thesis was the determination of a standard curve for
human chorionic gonadotropin (hCG) with samples which were provided by the
National Institute of Cancer.
It is relatively easy to find high quality antibodies and calibration standards for
hCG, which is typically quantified in international units (IU) per mL. While the
definition of one IU is occasionally readjusted, it appears that 1 mIU/mL is roughly
equal to an hCG concentration of 1.9 pM according to Sturgeon et al53,54. Five
calibration samples were individually measured on magnetic biochips functionalized
with anti-hCG to obtain the standard curve shown in Figure 33. Two unknown
samples S17 and S18 were also quantified and interpreted. This assay series resulted
in positive feedback from the NCI program managers.
68
Supplementing 4-Channel Real-Time Data with Before & After Off-Line Measurements2kΩ MagArray Chip WD1-3-2, Sept-15-2007
22.7
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35
0 5 Minutes 15 20 Real-Time Offline Median
Sig
nal,
µV
MACS nanoparticle incubation - 20 min
Figure 34: Offline sensor quantification example. To enhance the accuracy of the hCG measurements, off-line evaluation of additional sensors and nanoparticle amplification were employed. In addition to four real-time sensors, 12 hCG sensors were quantified with before-and-after measurements. This way a 16-sensor median signal value could be obtained, even though the electronics had only 4 independent data channels.
The hCG assay series is noteworthy for several new techniques that were
employed to get better data, and for the insights gained from the assay results. First of
all, the measurements were performed at a time when the readout electronics were still
limited to recording only four sensors at a time. However, since the choice of the
sensors could be manually changed during a live experiment without significantly
disturbing the measurement integrity, additional sensors could be evaluated with some
effort by recording their signals off-line, i.e., before and after nanoparticle incubation.
This of course required a stable measurement setup and a fair amount of certainty that
no unexpected signal artifacts would go unnoticed in the off-line sensors. However,
the before-and-after data obtained this way was indeed roughly comparable in mean
value and variance to the real-time data, as shown in Figure 34. For that reason it was
deemed safe to combine off-line and real-time measurement data into an overall
median signal value and an overall error estimation.
69
HCG Assay 12.5 mIU Oct-08-2007 - Example of Nanopar ticle Amplification Protocol
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MACS - 20 min MACS - 20 minLinker Wash Offline Wash
Figure 35: Example of nanoparticle amplification. To enhance the accuracy of the hCG measurements, after the initial nanoparticle adsorption , the incubation of a biotinylated nanoparticle-nanoparticle linker molecule allowed the chip to adsorb a second layer of nanoparticles onto existing nanoparticles, which roughly doubled the assay signal level.
Additionally, as shown in Figure 35, the hCG assays systematically applied a
technique for nanoparticle amplification which was introduced by Heng Yu: After the
initial 20-minute nanoparticle incubation, the chips were rinsed with clean PBS buffer
and then incubated for five minutes with a multiply biotinylated nanoparticle-
nanoparticle linker molecule. This linker molecule would attach to any nanoparticles
on the chip surface and, colloquially speaking, make them “sticky” again towards
additional streptavidin-coated nanoparticles. After the incubation of the biotinylated
linker molecules, a second round of magnetic nanoparticles could be adsorbed onto the
sensors. The resulting secondary binding curves saturated at a level that was ca. 1.5x
to 3.5x higher than the earlier signal levels. This method of amplification was applied
twice on each chip. The result is that the hCG standard curve was obtained in three
forms: One from the original (traditional, unamplified) nanoparticle adsorption, one
from the first round of amplification, and one from the second round of amplification,
as shown in Figure 35. An interesting trend is that the amplification is strongest for the
lowest signal levels, and weakest for the highest signal levels. This means that overall
slope of the signal vs. concentration curve is flattened with each amplification. This
enhances the dynamic range of the nanoparticle quantification, but may increase the
measurement error.
70
MagArray Standard CurveHCG in Serum; Diluted to 1:1 with PBS buffer for me asurement
y = 1.0641x0.3364
y = 4.4422x0.2554
y = 11.453x 0.1981
1.00
10.00
100.00
1 10 100 1000 10000 100000
HCG Concentration (in serum, before dilution), mIU/ mL
Sig
nal,
mic
rovo
lts
MACSMACS + 1xAmpMACS + 2xAmp#REF!
Figure 36: Effect of nanoparticle amplification on standard curve. The hCG quantifications systematically employed two rounds of nanoparticle amplification, which each increased the signal ca. 1.5x to 3.5x over the previous signal level.
4.5. HCG ASSAY SIGNAL SCALING AND DYNAMIC RANGE
The systematic hCG assay series was the first rigorous experiment to explore
the signal scaling trend and sensitivity limits of the spin-valve biochip. The generally
noticed signal scaling trend of “twice the signal for every 10x increase in analyte
concentration” is more precisely quantified by a non-linear regression fit in Figure 36.
There, the data from the original nanoparticle application fits a power law relationship
of Signal = [conc]^0.3364 which means that for every 10x increase in concentration
the signal increases, on average, by a factor of 10^0.3364 = 2.17x. The benefit of this
signal scaling trend is that a concentration increase by one million results in only a
10^(6*0.3364) = 104x larger electrical signal and nanoparticle coverage (the linear
dependence of the signal on nanoparticle coverage was already demonstrated by
former students, e.g., thesis G. Li30). This means that the magnetic biosensor has a
very large dynamic range of at least six orders of magnitude in theory. Good signal
scaling over four orders of magnitude is already shown in Figure 36.
71
∆ S
ign
al, µ
Vrm
s
hCG concentration in 50% serum
Standard curve for hCG in 50% serum
1
10
100
1 nM 1 µM1 pM1 fM
Control Signal Range in PBS: 2.5 - 3.5 µVrms
Figure 37: Standard curve for hCG in 50% serum. The detection threshold for the hCG assay was estimated from PBS control assays to be a few fM. However, in actual 50% serum samples, the lowest readings were consistent with a metabolic baseline concentration of 1 pM hCG, which could have been the result of using real serum in the preparation of the samples.
However, the Signal = [conc]^0.3364 scaling relationship is altered if
nanoparticle amplification is used. From Figure 36 we see that if one and two rounds
of nanoparticle amplification are added, then for each 10x increase in analyte
concentration the signal level increases only by 10^0.2554 = 1.80x and 10^0.1981 =
1.58x, respectively. This would, theoretically, increase the sensor’s dynamic range
even more, but at the cost of accuracy. For example, a 10% measurement uncertainty
would translate into a roughly 10^(Log(1.1) / 0.3364) = 33%, 45%, and 62% analyte
concentration uncertainty for zero, one, and two rounds of nanoparticle amplification.
Another interesting aspect of the hCG assay series was the evaluation of the
detection limits. On the upper end of the scale, no signal saturation was detected, and
the standard curve was in good agreement with the power law fit up to the highest
concentration hCG sample (ca. 50 nM). Going to lower concentrations, the signal
scaling was well behaved across four decades, down to a few pM, where the signal
levels seemed to stabilize. This was the case even for serum samples which had no
additional hCG added, and which were labeled as “zero hCG”. Yet when a zero-hCG
assay was performed with plain PBS buffer, a significantly lower background signal
level of only 3 µV (after two rounds of amplification) was obtained. As shown in
Figure 37, this signal background in PBS would suggest that the lower detection
72
threshold for the hCG assay should be in the femtomolar concentrations, rather than in
the observed pM concentrations. One possible reason for this discrepancy between the
serum and PBS control assays might have been that the “zero hCG” serum sample was
not actually devoid of hCG. In fact, the natural metabolic baseline level for hCG in
real serum is around 1 pM for non-pregnant women according to Korhonen et al55. It
is therefore feasible that in this assay series the lower detection threshold was limited
by the natural metabolic baseline concentration of hCG in human serum.
4.6. MAGNETIC BIOCHIP ASSAY CONCLUSION
The results shown in this chapter are some the earliest true assay experiments
performed on the spin-valve biochip. Previously unobtainable, these results were made
possible by countless hours of sensor and biochip fabrication development at the
Stanford Nanofabrication Facility, the right choice of nanoparticles, lots of electrical
troubleshooting, and by the ingenuity of Heng Yu and Nader Pourmand, who were
developing the biochemistry for these chips. Chip fabrication and assembly was done
by myself, while Heng Yu would usually carry out most of the assay steps on the chip,
with the exception of the nanoparticle-based analyte quantification measurement,
which also was done by myself. Many additional assaying experiments were carried
out successfully later on, with increasingly more people obtaining results on the spin-
valve chip independently. To date, the open-well chips developed in this thesis were
used for assaying experiments by Heng Yu, Richard Gaster, Andy Mak, Liang Xu,
Shu-Jen Han, Drew Hall, and Dokyoon Kim. Their work often pursues much more
advanced assays, which increase the number of concurrent analytes in multiplex
protein detection, which push the limits of sensitivity, and which outperform existing
assaying systems such as ELISA and hence produce truly new scientific biochemistry
data. As a result, several high-profile publications based on this open-well biochip
have been published (in PNAS56,, Nature Medicine57, Biosensors and Bioelectronics58)
or are about to be published59 by S. J. Osterfeld, H. Yu, R. S. Gaster, A. Mak, N.
Pourmand, Drew A. Hall, L. Xu, and thesis advisor S. X. Wang. Additional
publications are likely to follow.
73
CHAPTER 5. OPTIMIZATION AND CHARACTERIZATION
This chapter will present several additional original developments and
experiments which are significant for the performance and understanding of the spin
valve biochip, but which also occasionally needed time and testing to be fully
understood and adopted.
5.1. DEVELOPMENT OF A SIMPLE 64-SENSOR SIGNAL PREAMPLIFIER
The spin valve biochip, as designed, had 64 individual sensors. Yet the signal
preamplifier was limited to recording only four sensors concurrently, which however
were freely selectable from the array. This was enough to obtain many very
encouraging assay results as shown in Chapter 4, Assay Results, but it left much to be
desired. What was the sensor-to-sensor signal variation across the entire chip? How
many analytes could be detected simultaneously? Was the magnetic tickling field
uniform enough across the chip? And what types of optimizations could be performed
with the statistical power of 64 sensors combined?
As shown in Figure 38, scaling up the old signal generation architecture to 64
channels would have required the use of at least 128 operational amplifiers, one in the
AC current source and in the buffer of each signal pathway. This seemed excessive for
a discrete electronics design. The idea of connecting the 64 sensors in an 8x8 matrix,
with just one external connection per row and column, had been floated around before.
However, it had not been entirely clear (to me) how sufficient signal isolation between
the sensors could be achieved in such a setup. A common node shared among several
sensors would need to be a perfect current sink, such as a connection to ground, to
prevent the sensors from interacting. But how could one measure a signal at a ground
node? The crucial insight came in a December 2007 discussion where Drew Hall
explained the concept of a virtual ground, i.e., an operational amplifier set up in such a
way that it maintained a ground potential at its signal input.
74
Old signal preamplifier: Applies an AC sense current, and measures the voltage across the spin valve sensors. Requires ca. 128 opamps and at least 65 connections to a discrete 64 sensor biochip.
New signal preamplifier: Applies an AC sense voltage and measures the current in the spin valve sensor. Requires ca. 16 opamps and a total of 16 connections to a discrete 64 sensor biochip.
Demonstration of 64 Sensor Magnetic Biochip Measure ment
53 Sensors with Biotin-BSA, 8 Reference Sensors, 3 N/CS. Osterfeld and H. Yu, July-14-2008
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500 Hz1mApp
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SVSV
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~
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Vsig 3+4
Biochip with N sensors and 2*N0.5 pins
One transimpedance amplifier per colum
-+-+i1+i2
Rf
Vsig 1+2-+-+
Figure 38: New 64-channel signal preamplifier architecture from late 2007. A new preamplifier design was developed to permit a complete readout of all 64 sensors on the biochip with a realistic part count and significantly fewer connections per chip. This resulted in the first ever 64-sensor magnetic biochip measurement being performed in July of 2008.
75
Such an amplifier could perform what seemed previously inconceivable: It
would extract useful information from a “ground” node shared by multiple sensors,
effectively by performing a linear addition of the individual signals. Despite being
summed at this common node, the individual signals would remain recoverable if each
of the summed sensor signals had a unique frequency. This would amount to
frequency domain multiplexing, as shown in Figure 38 with two AC voltage sources at
540 Hz and 570 Hz. Alternatively or in conjunction, these voltage sources could be
turned on and off sequentially, which would amount to time domain multiplexing.
This suddenly permitted that all 64 sensors be connected into a passive array
with just 16 external connections. This would permit smaller, more economic
biochips, and it would make building a 64-sensor preamplifier simple enough that it
could be done without too much effort. Additionally, the new signal preamplifier
could be built with much fewer discrete components, in part because the passive
sensor array could now be driven with AC voltage sources instead of the more
complex AC current sources needed in the old design.
The new preamplifier circuit architecture was immediately promoted for actual
implementation, but nevertheless, it took some time to gain the attention it deserved. It
was not until July of 2008 that such an amplifier had been built (by myself), which
allowed the first 64-sensor magnetic biochip measurement (Figure 38) to be
performed. The 64-sensor signal preamplifier made many important experiments
possible, such as the detection of eight analytes at 1 pg/mL (ca. 50 fM) concentrations
in the presence of a much larger signal from adjacent biotinylated sensors. This is
shown in Figure 39, where the signal separation was well maintained in the new
amplifier design, as signal ratios of up to 100:1 were easily recorded without any
evidence of electronic cross-talk. Due to the signal scaling relationship (Sig. =
[conc.]^0.3364, see Chapter 4), the 100:1 signal ratio shows that analytes with
concentration differences of ca. one million to one could be measured on the same
chip.
76
Detection of Multiple Analytes, Each at 1 pg/mL in PBSS. Osterfeld and H. Yu, October-2-2008
000E+0
50E-6
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75:1
Biotinylated Sensors
1 pg/mL Analytes
Photo of Individually Functionalized Sensors:
Biotin / BSA
GC
SF
Biotin
Epoxy
TN
F-α
IL-1α
Eotaxin
INF
-γ
LFVE
GF
CE
A
Figure 39: Example of dynamic range and channel separation. The signals from individual sensors remain well separated despite being very different in magnitude. The good similarity of signals from identically functionalized sensors is also apparent. The onset of particle binding results in a very sudden deflection of the data, indicating that no temporal averaging filter was used to smooth the data. The three signal saturation plateaus stem from three successive magnetic nanoparticle adsorptions, i.e., nanoparticle amplification as previously explained in Figure 35: Example of nanoparticle amplification.
77
Sensor-to-Sensor Signal ReproducibilityMultiplex Sandwich Assay, 7 Analytes at 100 pg/mLS. Osterfeld and H. Yu, Chip 408-4-3, Nov-19-2008
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4.04%
98% (flawed probe)
BSA Control Range: 0.72 ± 0.42 µV
Epoxy Reference
5.48%
9.45%
4.57%
5.83%
Figure 40: Example of sensor-to-sensor signal reproducibility in multiplex assay. Data from a single chip with multiple analytes in an actual protein sandwich assay. Seven analytes were measured, each with eight identically functionalized sensors. The median sensor-to-sensor coefficient of variation is roughly 5%. One functionalization resulted in a 98% CV, but this was an unusual probe consisting of a mix of several antibodies.
5.2. SENSOR-TO-SENSOR REPRODUCIBILITY
How many sensors should be allocated to a single analyte to ensure good
reproducibility? Would the assay results be better if the data from multiple identically
functionalized sensors was averaged? To answer these questions, it is important to
have an estimate of the reproducibility of signals obtained from identically
functionalized sensors in real assays.
The data shown in Figure 40 was obtained in a regular multiplex sandwich
assay with seven protein analytes at 100 pg/mL concentrations (ca. 5 pM in most
cases). The resulting CV for each group of eight sensors was ca. 1% to 10%, with a
median CV of approximately 5%. One particular analyte could not be reliably
detected, with very poor agreement across the eight corresponding sensors resulting in
a 98% CV. However, this was an experimental probe, which consisted of a mix of
several antibodies. The rest of the better performing probes were all monoclonal
antibodies.
78
The initial question, i.e., how many sensors need to be allocated to a single
analyte, is not fully answerable with this data alone. However, a lower bound can be
recommended: It seems like a good idea to use at least three sensors for each analyte.
This redundancy would help to detect problems, such as the malfunctioning probe
with 98% CV, from the resulting sensor disagreement.
Additionally, one should expect that the CV increases at lower analyte
concentrations. Therefore, low concentration analytes can most likely be more reliably
quantified with a larger number of redundant sensors. On the other hand, if one only
needs a better average without an estimate of the sensor-to-sensor CV, then it would
be more economical to simply increase the sensor size to average more analyte
binding events. Doing so should have the same effect as averaging the data from
multiple sensors, without allocating too many of the limited data channels to a single
analyte.
In Figure 40, the BSA control sensors are expected to have an average signal
close to zero, and hence the BSA signal range is indicated rather than the CV.
Similarly, the average signal of the epoxy reference sensors is used to define the zero
signal reference electronically, which is why they fall almost exactly on the horizontal
axis.
Assays results with redundant sensors, such as shown in Figure 40, are often
simplified by first removing data from sensors which are electrically malfunctioning
(e.g., unstable baseline resistance, out of spec initial resistance, etc.) and then
estimating an average signal for each analyte.
79
Chip-to-Chip Signal ReproducibilityMultiplex Sandwich Assay, 7 Analytes at 100 pg/mL
S. Osterfeld and H. Yu, Nov-19-2008
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Analyte 1 Analyte 2 Analyte 3 Analyte 4 Analyte 5 Analyt e 6 Flawed Probe BSA
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Chip 408-4-4
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CV = 1.5%
CV = 11.3%
CV = 10.1%CV = 6.3%
CV = 13.3%
CV = 11.1%
CV = 29.8%
BSA Control0.15 ± 0.54 µV
Figure 41: Example of chip-to-chip assay reproducibility. A multiplex sandwich assay with analytes at concentrations of 100 pg/mL was repeated five times on separate chips. If the flawed probe is excluded, the average chip-to-chip CV in this test was 8.9%.
5.3. CHIP-TO-CHIP REPRODUCIBILITY
To estimate the chip-to-chip reproducibility of assay results, the measurement
shown in Figure 40 was repeated five times on separate chips. On each of the five
identically functionalized chips, mean analyte signals were determined by averaging
of the redundant sensors. Ideally, all five chips should return identical analyte signals,
and the actual signals are shown in Figure 41.
The average chip-to-chip coefficient of variation was 11.9% in this round of
measurements if the flawed probe with 29.8% chip-to-chip CV is included. Without
the flawed probe, the chip-to-chip CV is 8.9% on average. This degree of chip-to-chip
assay reproducibility is quite good, and in practice means that one can usually
compare results from different chips without the inclusion of on-chip calibration
(a.k.a. housekeeping) probes. Still, on-chip control sensors which provide a chemistry-
dependent reference signal for assay calibration and quality assurance could be very
helpful for identifying general chemistry problems on individual chips.
80
Recycled Chip WD1-2-3, Mar-16-2008Only HPV-18 analyte is present (1nM)
-5
0
5
10
15
20
0 5 10 15 20 25 30 35
Time, minutes
Sig
nal,
mic
rovo
lts
HPV-39
EPOXY
EPOXY
HPV-39
HPV-39
EPOXY
HPV-39
HPV-39
EMPTY
EMPTY
HPV-18
HPV-31
HPV-18
HPV-18
HPV-31Nanoparticles NanoparticlesLinker DI Water
Initial Signal AmplifiedSignal
RinseWith
DIWater
Figure 42: Nanoparticle adsorption followed by nanoparticle release. Adsorbing nanoparticles generates a signal within a few minutes. Releasing the adsorbed nanoparticles by denaturing the hybridized DNA with DI water generates a reverse signal almost instantaneously. Using the magnitude of the near instantaneous nanoparticle dissociation signal for assay quantification, rather than the slower nanoparticle association signal, can reduce the impact of sensor drift on the results.
5.4. REDUCING THE IMPACT OF SENSOR DRIFT
In the early days of the spin-valve biochip experiments it used to be standard
procedure to rinse the chip with deionized water at the end of the measurement to
remove surface salt residues, so that the nanoparticles, which otherwise would have
been obscured, could be inspected in the scanning electron microscope. In protein
sandwich assays the rinse with DI water did not change the signal levels very much.
However, several experiments were also performed where the capture probe and
analyte consisted of complementary DNA strands. In these DNA detection
experiments, the final DI water rinsing step would cause the previously established
nanoparticle binding signal to disappear almost instantaneously and completely, due to
the DNA dissociating and the nanoparticles being released, as shown in Figure 42.
81
Adsorption Measurement vs. Release MeasurementHPV DNA Detection Experiment
S. Osterfeld / Heng Yu March 19th, 2008
-10
-5
0
5
10
15
20
25
30
Known PositiveProbes
Known NegativeProbes
Known PositiveProbes
Known NegativeProbes
Sig
nal,
mic
rovo
lts
Nanoparticle Adsorption Data Nanoparticle Release Data
3 σ LOD
10 σ LOQ
3 σ LOD
10 σ LOQ
Figure 43: Quantification from nanoparticle adsorption vs. nanoparticle release. The nanoparticle release data is obtained faster and hence less affected by sensor drift, which improves the negative probe signals in particular. This in turn results in a lower limit of detection (LOD) and a lower limit of quantitation (LOQ), which are both determined by the standard deviation of the negative probes (σ).
The feasibility of using the nanoparticle release data for a more accurate assay
quantification is shown in Figure 43. This set of data was obtained in a DNA detection
experiment performed on a chip which exhibited a slightly elevated amount of sensor
drift, which could have been either of magnetic origin (domain noise) or due to slow
sensor corrosion during the measurement. The sensor drift was random from sensor to
sensor, rather than uniform, resulting in a spreading of signals over time which could
not be corrected by reference subtraction or similar means.
In Figure 43 the “nanoparticle adsorption data” corresponds to the regular
method of magnetic assay quantification, where the signal gain during the nanoparticle
incubation period is recorded. On the other hand, the “nanoparticle release data” is the
near instantaneous drop in signal (multiplied by -1 for better comparison) which
happened upon rinsing with DI water.
In Figure 43 the most obvious difference between the adsorption and the
release data can be found in the set of DNA probes which are known to be negative,
82
i.e., which should ideally have resulted in a zero signal. The nanoparticle release data
from the negative probes is closer to the ideal value and has a much smaller standard
deviation. This is significant, because the standard deviation of the known negative
probes, designated as σ in Figure 43, determines the limits of detection (LOD) and
quantitation (LOQ). 3 σ is a common definition for the LOD. Any signal above the
LOD is a positive result with a 99% confidence. Similarly, 10 σ is often assumed to be
a reasonable LOQ, which is the minimum signal required to obtain a reasonably
accurate measure of concentration, rather than a simple positive/negative result.
In Figure 43, only 63% of the positive probes are above the LOD in the
nanoparticle adsorption dataset. On the other hand, 88% to 100% of positive probes
are above the LOD in the nanoparticle release dataset. This improvement in detection
is solely the result of the shorter observation period needed to record the nanoparticle
release signal, which hence is less affected by sensor drift.
Some degree of sensor drift is inevitable in high sensitivity measurements. In
fact, in many cases slow and random sensor drift is one of the sensitivity-limiting
factors on the spin valve biochip, second in importance only to functionalization
uniformity. It therefore would be desirable to find a similar method of sudden
nanoparticle release for the protein assays. Maybe a suitable antibody dissociation
reagent could be found. A better solution might be to anchor the antibody probes to
the sensor surface with a double stranded DNA segment, which could be dissociated
with benign DI water on demand at the end of the sandwich assay. Another solution
might be the use of a protein in which a sizeable conformation change (contraction or
expansion) could be induced on demand. This also would also create an observable
and sudden signal, as will be shown in the next section, Signal Dependence on Height.
83
Sensor 1 Sensor 2 Sensor 3
20nm SiO220nm SiO2
Signal: 241 µV Signal: 162 µV Signal: 114 µV
(241/162)^(1/3) = 1.14
Sensor 2is 1.14x farther away
Z0 + 20 nm = 1.14 Z0
Z0 = 20/0.14 = 142 nm
(241/114)^(1/3) = 1.28
Sensor 3is 1.28x farther away
Z0 + 40 nm = 1.28 Z0
Z0 = 40/0.28 = 142 nm(confirmation)
Sensor 1unknown distance
Z0
Signal vs. Sensor-to-Nanoparticle DistanceS. Osterfeld, Nov-13-2008
y = 7E+08x -3.0016
R2 = 0.9991
0
50
100
150
200
250
140 180 220 260 300
Calculated Distance, nanometers
Obs
erve
d S
igna
l, m
icro
volts
A B
Figure 44: Signal vs. sensor-to-nanoparticle distance. By creating a staircase structure of additional oxide on a spin valve biochip, the actual distance of surface-bound nanoparticles Z0 could be measured.
5.5. SIGNAL DEPENDENCE ON NANOPARTICLE DISTANCE
One fundamental question that needed to be confirmed was the signal
dependence on sensor-to-nanoparticle distance. In general, the field of a dipole magnet
diminishes with the third power of the distance. However, former students had also
carried out calculations which suggested that this r3 relationship might not be valid
very close to the sensor, and that for a specific sensor geometry and particle size, there
might be an optimum distance and strongest signal at ca. 80 nanometers20.
The availability of the 64-sensor amplifier made it possible to investigate this
scaling relationship carefully in an actual experiment. A regular 64-sensor spin valve
chip was modified by coating groups of eight sensors with successive depositions of
SiO2 in an ion beam sputter system, resulting in a staircase of additional oxide on the
chip as shown in Figure 44a. In total, eight groups of sensors with increasingly thicker
SiO2 layers ranging from +20 nm to +160 nm were created. To obtain a signal, this
chip was uniformly biotinylated, and MACS nanoparticles from stock solution were
adsorbed for roughly eight minutes, at which point the signal was stabilized by
replacing the MACS solution with PBS. At this point the signal was averaged for each
group of eight identical sensors for better data accuracy. The unknown sensor-to-
MACS distance on the unmodified sensors Z0 was then calculated as shown in Figure
44a while assuming an r3 scaling relationship.
84
MACS Nanoparticle to Sensor Distance - Continuous M easurement S. Osterfeld, Nov-13-2008
-50
0
50
100
150
200
250
300
350
0 2 4 6 8 10 12 14 16 18
Minutes
Sig
nal,
mic
rovo
lts
120
130
140
150
160
170
180
MA
CS
Dis
tanc
e, n
anom
eter
s
Original Sensors
+20 nm SiO2
+40 nm SiO2
+60 nm SiO2
+100 nm SiO2
+120 nm SiO2
+140 nm SiO2
+160 nm SiO2
Distance Calc.MACS Nanoparticle Incubation PBS Air Dried ChipDI
142 nm
129 nm
Figure 45: Continuous measurement of the average nanoparticle distance. The organic surface chemistry layer contracts by 13 nanometers as the chip surface is dried.
The sensor-to-MACS distances on the modified sensors was then estimated by
adding the known thickness of the deposited oxide layers to the initial distance Z0. The
resulting signal vs. distance data is shown in Figure 44b, and fitted with a power law
by nonlinear regression. The resulting fit is excellent and no deviation from a simple r3
scaling relationship is evident even if slightly different values for Z0 are tested.
Of greater interest than the scaling law confirmation is the fact that this method
can yield a seven-fold independently calculated estimate of the sensor-to-nanoparticle
distance, which is averaged over several million nanoparticles. The so calculated
distance should be a rather precise measurement of Z0 = 142 nm.
The most interesting result is that the spin-valve biochip can provide a real-
time measurement of the average nanoparticle distance. By performing the Z0
calculation for every data interval, and not just at the end of the assay, a continuous
distance measurement can be obtained as shown in Figure 45. This capability could
potentially be used to measure conformation changes in surface-bound proteins in
real-time, for example in response to a signaling molecule which is added to the
solution.
85
To demonstrate a measurement of a simple protein conformation change, the
biotin assay in Figure 45 was finished with a brief rinse with deionized water,
followed by air drying of the sensors. This caused the biotin surface chemistry, and
probably also the streptavidin and/or dextran matrix of the MACS nanoparticles, to
contract by a combined amount of 13 nanometers due to dehydration. This resulted in
a corresponding sudden signal increase on all sensors, with the largest signal increase
of +38% occurring, as one would expect, on the unmodified sensors.
In conclusion, Z0 = 142 nm is somewhat larger than expected. The spin valve’s
free layer is ca. 10 nm below the sensor’s surface, and the sensor passivation accounts
for another 40 nm, for an approximate total of 50 nm. This would suggest that the
relatively simple biotin surface functionalization chemistry, plus one MACS particle
radius, must have accounted for roughly 90 nm. Possible explanations include that
hydrated MACS particles may be significantly larger than they appear in the electron
microscope, and that the r3 scaling relationship diminishes in the near-field20, which
would tend to inflate Z0 calculations done according to Figure 44a.
These results also highlight the importance of minimizing and controlling the
thickness not just of the sensor passivation, but also of the surface functionalization
chemistry, which may have an even greater thickness in real sandwich assays – this
should be measured in a future experiment. So far, the data suggests that even just a
13 nm change in functionalization thickness can result in a 38% difference in
sensitivity and signal levels. This result also reinforces the notion that the surface
functionalization needs to be applied in a way that prevents uncontrolled evaporation
and uneven drying, which almost certainly would result in functionalization thickness
variations, and binding site density variations as well. This means, for example, that
the initial PEI layer in sandwich assays should not be slowly air dried, but instead
quickly and uniformly dried with compressed nitrogen. Lastly, the hypothesized
optimum nanoparticle distance of roughly 80 nm could be neither verified nor refuted,
since it lies outside the experimentally accessible range of distances, but a diminished
r3 scaling relationship might be observable in a meticulous follow-up experiment.
86
5.6. SIGNAL DEPENDENCE ON TICKLING AND BIAS FIELDS
As described in Chapter 1.3.2 – Principle of Operation, the superparamagnetic
nanoparticles need to be polarized with an external magnetic tickling field Ht, before
they can be readily detected by the spin valve sensors. A magnetic bias field Hb is also
applied to bias the sensor and lower the measurement noise. Hb is a static field applied
along the spin valve segments’ geometric axis, while Ht is an alternating field applied
perpendicular to the spin valve sensors’ geometric axis, with a frequency of 200 Hz to
facilitate narrowband detection. Details of this signal generation scheme have been
previously described in the works of S. X. Wang, G. X. Li, and SJ. Han.
Without a tickling field, there is no signal to measure, and without a bias field
the sensor does not respond linearly and is too noisy to be useful. On the other hand, if
either of these fields is too strong, then the sensor is saturated and can no longer detect
the small field changes due to the nanoparticles. However, the appropriate strength of
these externally applied tickling and bias fields had never been systematically
evaluated. Instead, default settings of 50 Oersteds for the bias field and 80 Oersteds
(rms) for the tickling field were routinely used, which had been roughly estimated
from the spin valve sensor’s MR transfer curve, which in a 50 Oe bias field appears to
be pretty linear to roughly ± 100 Oersteds before gradually saturating.
The obvious method for testing the bias and tickling field dependence would
have been to run several identical measurements with different Ht and Hb settings.
However, this would have been exceedingly laborious, in part because at the time the
signals varied somewhat from chip to chip even if nothing is changed. Thankfully, it
was determined that the sensor response is highly reproducible when alterations and
restorations of Ht and Hb are performed while the nanoparticle coverage is held
constant. This made it possible to determine the signal dependence on Ht and Hb on a
single biotinylated chip, by measuring the initial signal baseline level for various Ht
and Hb settings before the experiment, and again after the nanoparticle binding had
been positively stopped with a wash, rinse, and drying step, as shown in Figure 46a.
87
Magnetic Biochip Signal vs. Bias Field (Hb) and Tic kling Field (Ht)First Experimental Data, July-22-2007, S. Osterfeld
-250
0
250
0 10 20 30 40 50 60 70 80 90 100Minutes
Sig
nal,
mic
rovo
lts
25 Alternate Settings for Ht, Hb Recreate 25 Alternate Settings for Ht, Hb Particle Adsorption,
Default Settings
Example of Alternate Setting(Hb 132, Ht 25 Oe), Signal = 44 µV
Default Settings(Hb 54, Ht 78 Oe)Signal = 125 µV
A
Figure 46: Determination of the optimal tickling and bias field for 1.5 µm sensors.
88
Five different settings for the tickling field (25, 38, 51, 63, and 78 Oe rms) and
five different settings for the bias field (1, 14, 27, 50, and 54 Oe) were initially
evaluated in this way, resulting in twenty-five combinations of settings that had to be
dialed in and held for ca. one minute each, both before and after the actual
nanoparticle binding. The before-and-after difference in signal level was determined
for each setting, together with the sensor noise at each setting, which was determined
from the amount of signal variation during the one-minute steady hold for each
setting. Such data was obtained from a total of four sensors (the limit at the time of
this experiment), but only one such sensor is shown in Figure 46a for visual clarity.
The results revealed a surprising trend of increasing signals with decreasing
fields, so much in fact that both the first experiment (Figure 46b) and a second
experiment (Figure 46c) failed to reveal a clear signal maximum, which was
apparently closer to zero than the range of fields investigated. Similarly surprising was
the strength of the scaling dependence, as signals between 473 µV (Ht = 25, Hb = 1)
and 41 µV (Ht = 131, Hb = 27) could be obtained, depending on the choice of fields.
The underlying reasons for this signal scaling dependence remained somewhat of a
mystery until a mathematical model of the sensor and nanoparticle magnetization was
developed almost a year later, which showed that this signal scaling relationship was,
in fact, in good agreement with theory (see Chapter 6, Mathematical Modeling).
On the other hand, the noise dependence on fields was as expected, with the
sensor noise increasing in the absence of a bias field as shown in Figure 46d. The
noise measurements were combined with the signal levels to obtain the signal to noise
ratio at each field setting as shown in Figure 46e. The results suggested an optimum
SNR at ca. Ht = 38 Oe rms and Hb = 27 Oe. Fields were both ca. 50% lower than the
former default settings, resulting in 2x larger signals and ca. 6 dB better SNR. These
results were used for any subsequent assays on this type of chip. However, as later
results and mathematical modeling revealed (see next sections), these specific settings
were only optimal for sensors with 1.5 µm wide, 90 µm long spin valve segments.
89
Magnetic Nanoparticle Labels in PBSAirPBS
0
Time, minutes
∆ S
ign
al,
µV
rms
0 10 20 30
100
20
40
60
80
3 µm sensors
1.5 µm sensors
a Wide sensor vs. narrow sensor geometry b Signal development vs. sensor size
i i
Three segments,
each 3.0 µm wide
Six segments,
each 1.5 µm wide
Figure 47: Schematic illustration of sensor segment width evaluation. This figure illustrates how the effect of sensor segment width can be evaluated while keeping the current density and total resistance constant (A). Initial tests revealed that varying the sensor width can indeed affect sensitivity (B).
5.7. SIGNAL DEPENDENCE ON SENSOR SEGMENT WIDTH
Several models exist which predict that a spin valve sensor will be more
sensitive in nanoparticle detection assays if its aspect ratio (length/width) is
increased60,61. To investigate this prediction experimentally, several biochips chips
were made which featured multiple sensors in close proximity which were differently
patterned in such a way that only the sensor segment width was varied, while all other
operating parameters (current density, overall resistance, total area, etc.) were kept
constant. This is shown schematically in Figure 47a. Initially, such experiments were
limited by the electronics and the use of the available optical photolithography to
measuring only four sensors and segment widths of more than 1 µm respectively. Such
initial results revealed a promising trend which seemed to agree with predictions of
better sensitivity for more finely segmented sensors, as shown in Figure 47b. An
interesting and yet unexplained aspect of these early results is that the more finely
patterned sensors did not just simply generate a stronger signal, but also a signal with
a disproportionally faster initial response. In other words, while the signal of the
1.5 µm sensors is ca. 1.4x larger than that of the 3.0 µm sensors at t > 15 minutes, it is
up to 2.6x larger in the first 15 – 45 seconds of nanoparticle application.
90
Figure 48: Signal and noise dependence on spin valve sensor segment width. The optimal width for 90 µm long spin valve segments seems to be between 600 to 800 nanometers if noise is considered too. In this experiment no bias field was applied.
Later, with the availability of the 64-sensor amplifier it became feasible to
measure a single biochip which had a much larger number of different sensor on the
same chip. Additionally, due to a fortuitous collaboration with Hitachi Global Storage
researchers Robert Fontana, Thomas Boone, Stefan Maat, and Jordan Katine, it
became possible to fabricate a spin valve biochip with test sensors patterned by
electron beam photolithography. This easily permitted sensor segment widths of down
to 300 nanometers.
A biochip was fabricated with eight different spin valve sensor types, each
having segments that were 90 µm long, but of various widths. This chip was uniformly
biotinylated and the nanoparticle adsorption signal was measured for all eight sensor
types under a range of different tickling fields. No bias field was applied. The results
are shown in Figure 48a, which reveal that starting with a 1.5 µm sensor, increasingly
finer sensor segmentation will initially result in increased sensitivity. However, after
reaching a maximum sensitivity at ca. 0.75 µm segment width, reducing the sensor
width further actually decreases the sensitivity. This is contrary to most theoretical
models, which predict no limit to the benefits of decreasing the sensor width.
However, the diminishing returns of increasingly finer sensor segmentation
were partially anticipated. Spin valve sensors typically have a “dead zone” or “inverse
91
zone” at their edges where the magnetoresistance effect is suppressed or inverted. This
edge effect is thought to have a fixed width extending maybe 50 – 100 nm from the
edge into the segment. For increasingly narrower segments, this would leave an
increasingly smaller intact magnetoresistive region available for nanoparticle
detection, which would explain the diminishing returns of narrower sensors.
Another interesting result of this experiment is the sensor noise, which is
shown in Figure 48b. Unfortunately, the chip which was investigated had a few
defects, and as such the noise data is not deemed to be accurate enough for a reliable
signal to noise ratio calculation. However, the general trend of increasing noise with
increasing sensor width is accurate and has been confirmed in several other
experiments. In fact, it was eventually found that an external bias field was no longer
beneficial for sensors with segment widths of less than 750 nm, because such sensors
already had low noise due to their internal magnetic bias, or shape bias field, which
stems from their high aspect ratio of more than 120 (length/width). The resulting
simplification of the external magnetic field setup – a reduction of the Helmholtz coil
from two to one axis – is another engineering accomplishment of this thesis work.
Taking into account the experimentally observed noise and signal trends of the
spin valve sensors, it was determined that the optimal spin valve sensor segment width
lies between 600 nm and 800 nm, with corresponding optimal tickling fields of 40 to
20 Oe (rms), respectively. Narrower segments would give lower signals without
significantly improving noise, while wider segments would produce lower signals and
increasingly more noise.
It is anticipated that these results should be more general in terms of sensor
segment aspect ratio – i.e., optimal sensors probably have a segment aspect ratio
ranging from 110 to 150 (length/width), regardless of actual size. Additional
experiments would be needed to clarify these results and the exact noise scaling.
92
CHAPTER 6. MATHEMATICAL MODELING
In Chapter 5 the dependence of the nanoparticle signal on various parameters,
such as tickling field strength, bias field strength, and sensor width was determined
experimentally. The signal scaling with the applied fields was particularly puzzling at
first, in part because significantly higher signals were obtained for increasingly
smaller tickling and bias fields Ht and Hb, as shown in Figure 49a.
Almost a year after these results were obtained, it seemed like a good idea to
investigate whether these results were actually in agreement with theory or not. Simple
equations for the resistance of a spin valve sensor and for the magnetization of
superparamagnetic nanoparticles existed, but they had not yet been combined in an
intuitive fashion, and hence a new and simple modeling approach was pursued. In the
end, summing up the magnetic field x- and y-components acting on the sensor, and
then defining what an assay signal is in this context, yielded a surprisingly
straightforward model which is entirely analytical and which showed that the results
were actually in almost perfect agreement with theory, as is qualitatively highlighted
in Figure 49b.
Figure 49: Experimental observations were explained with a mathematical model.
93
( )θSinR
RR Avg *2max
.
∆+=
Sensor θ
Hy
Hx
x
y
x
y
HkHb
Ht
++∆+=
22
max.
)(*
2 HkHbHt
HtRRR Avg
Figure 50: The resistance of a spin valve sensor segment is determined by the externally applied tickling field Ht, the externally applied bias field Hb, and the segment’s geometric aspect ratio, which manifests itself as an apparent anisotropy field Hk.
6.1. THE RESISTANCE OF A SPIN-VALVE BIOSENSOR
In Figure 50, according to Li et al., the resistance of a spin valve sensor which
has its reference (pinned) layer oriented along the y-direction can be described as
(1)
where θ is the angle between the free layer and the x-axis. Rather than using
the hyperbolic tangent approximation by Li et al., it is assumed that the angle θ can be
calculated exactly from Hy (the y-component of all magnetic forces acting upon the
free) and Hx (the x-component of all magnetic forces acting upon the free) as
(2)
Central to the upcoming model is the idea that Hy and Hx are similar to, but
not exactly the same as the applied tickling and bias fields Ht and Hb. For example,
the sensor’s shape introduces an apparent anisotropy field Hk, which supplements the
external bias field in terms of free layer stabilization. Thus, for the sensor’s free layer,
(3)
( )θSinR
RR Avg *2max
.
∆+=
( )22 HxHy
Hy
Hx
HyArcTanSinSin
+=
=θ
HkHbHx +=
94
Example of Calculated MR Transfer Curves in Zero Bi as FieldFor Spin Valve Sensors with 10% MR and 100 µm Long Segments
1880
1920
1960
2000
2040
2080
2120
-200 -150 -100 -50 0 50 100 150 200
Applied Transverse (Tickling) Field, Oe
Res
ista
nce,
Ohm
s
3.0 µm Sensor, Hk ~ 17 Oe
1.5 µm Sensor, Hk ~ 33 Oe
0.75 µm Sensor, Hk ~ 67 Oe
0.5 µm Sensor, Hk ~ 100 Oe
Figure 51: Example of calculated spin valve sensor MR transfer curves.
This leads to the final equation of the sensor’s resistance
(4)
In equation (4), Ht and Hb are controlled with external electromagnets, while
RAvg. and ∆Rmax are material- and geometry-dependent properties. For example, a
typical spin valve film has a sheet resistance of ca. 20 Ω/sq and maybe 10%
magnetoresistance, meaning that a single sensor segment of 100 µm length and 1 µm
width would have RAvg. = 2,000 Ω and ∆Rmax ~ 200 Ω.
The apparent shape anisotropy field Hk is determined by the sensor’s
geometric aspect ratio (width/height), and can be calculated from first principles if
desired. Very approximately, from actual experiments, it seems that
(in Oersteds) (5)
However, in practice Hk should be measured directly from the finished
sensor’s magnetoresistance transfer curve to guard against manufacturing variations.
++∆+=
22
max.
)(*
2 HkHbHt
HtRRR Avg
width
lengthHk *4.0≈
95
MACS Nanoparticle Magnetization CurveCourtesy of Wei Hu
-100%
-50%
0%
50%
100%
-200 -150 -100 -50 0 50 100 150 200
Applied Field, Oe
Mag
netiz
atio
n, P
erce
nt
AGM Measurement
Langevin Fit, α = 0.025 / Oe
Figure 52: Measured magnetization curve and model for MACS nanoparticles.
6.2. THE MAGNETIZATION OF SUPERPARAMAGNETIC NANOPARTICLES
Superparamagnetic nanoparticles develop a magnetization M in the presence of
an externally applied magnetic field H. The degree of nanoparticle magnetization,
normalized by the saturation magnetization MSat, is commonly described by the
Langevin Function as
(6)
where α is a proportionality constant which depends on the temperature, the
material, and the diameter of the nanoparticles. In practice, α is often experimentally
determined from a magnetization measurement of specific particles. For the MACS
nanoparticles, α ~ 0.025 * Oe-1 is a reasonably accurate value at room temperature.
However, an assembly of nanoparticles with a range of sizes will not follow equation
(6) exactly. Such a size distribution is the likely reason why the Langevin Function fit
in Figure 52 is not perfect. In fact, sometimes the underlying nanoparticle size
distribution can be inferred by weighing an assembly of Langevin Functions with
different values for α until an optimal fit with the measured data has been achieved.
This method has been termed Langevin Granulometry62.
( )H
HCothM
M
Sat *
1*
αα −=
96
( )βCosMpcHpx **3=
( )βSinMpcHpy **3=β
P
Nanoparticle
MP
Sensor θ
Hy
Hx
x
y
x
y
HbHk
Ht
Ht
HbHpx
Hpy
( )θSinR
RR Avg *2max
.
∆+=
Figure 53: Mathematical description of the sensor-nanoparticle interaction. The proportionality factor c3 depends on the size and location of the nanoparticle relative to the sensor.
6.3. EFFECT OF NANOPARTICLE ON SENSOR RESISTANCE
The essence of the mathematical model is captured in Figure 53. The
nanoparticle magnetization Mp is determined solely by the externally applied fields
Hb and Ht. Specifically, we can now rewrite equation (6) and specify the angle β
(7)
(8)
The nanoparticle magnetization Mp generates additional field components Hpx
and Hpy, which act on the sensor’s free layer. How large these x- and y-components
are depends on several factors, such as the distance and position of the nanoparticle
relative to the sensor, as well as the nanoparticle size. These factors could be
accurately calculated for an individual nanoparticle.
However, on a biosensor which is interacting with millions of nanoparticles, it
is assumed that these factors can be folded into a single, constant, proportionality
factor c3, which represents the average sensor-nanoparticle interaction coefficient for
surface-bound nanoparticles.
( )22
22
*
1*
HtHbHtHbCothMp
+−+=
αα
( )22 HtHb
HtSin
+=β ( )
22 HtHb
HbCos
+=β
97
With these assumptions it is now possible to rewrite equation (4) for a single
nanoparticle as
(9)
To account for multiple nanoparticles, we introduce the variable n, which
simply is the number of nanoparticles that act on the sensor. Adding this variable to
equation (9) gives
(10)
where Mp and β are given by equations (7) and (8).
Equation (10) is already a complete description of the biosensor’s
instantaneous response to fields and magnetic nanoparticles. It could be further
modified to reflect the time-varying tickling field by changing the term Ht
accordingly. However, it is easier to consider Ht to be the effective (rms) value.
Also, it is not the absolute resistance R which is important, rather the change
∆R in response to a change in the number of nanoparticles ∆n is important. The goal
therefore is to maximize the sensor’s signal ∆R/∆n. Rather than calculating discrete
differences, the derivative δR/δn is therefore defined on the following page.
( )( )( ) ( )
++++
+∆+=22
max.
)**3(**3
**3*
2 HkCosMpcHbSinMpcHt
SinMpcHtRRR Avg
βββ
( )( )( ) ( )
++++
+∆+=22
max.
)**3*(**3*
**3**
2 HkCosMpcnHbSinMpcnHt
SinMpcnHtRRR Avg
βββ
98
6.4. DEFINITION OF ASSAY SIGNAL
As stated, the goal is to maximize the sensor’s signal ∆R/∆n in equation (10).
For this reason it is convenient to look at the derivative δR/δn. Furthermore, since the
goal is to detect the smallest possible number of nanoparticles n, it is sufficient to look
only at the initial response, i.e., the derivative in the limit of n 0, which simplifies
the resulting derivative further:
(11)
Calculating (11) in Mathematica (see Appendix C) yields the relatively
compact result
(12)
Note that Mp has been factored out but is still given by equation (7).
With equation (12) it is now possible to calculate the magnetic biochips’
response to nanoparticle adsorption as a function of tickling field Ht and bias field Hb.
Additionally, equation (12) makes it possible to calculate the effect of varying the
sensor width or aspect ratio, which will be reflected in a corresponding change of the
sensor shape bias field Hk. The results may need to be scaled by a constant multiplier
due to uncertainty about c3 and n. However, the experimentally observed signal
scaling trends and local maxima, which are very important for sensor optimization, are
independent of n and c3 and strongly corroborated by this simple theoretical model.
∂∂≡
→ n
RLimitnSignal
n 0*
( )( )
+++
+∆=2
32222
max *)(***3*
2*
HtHkHbHtHb
HtHkHbHkMpc
RnSignal
99
Sensitivity vs. Tickling Fields: Model and Experime ntS. Osterfeld, Apr-14-2008
0
50
100
150
200
250
300
0 10 20 30 40 50 60 70 80 90 100
Tickling Field, Oe (rms)
Sig
nal,
mic
rovo
lts
500nm Data500nm Model750nm Data750nm Model
Figure 54: Model and experiment of two different types of sensors at zero bias field.
6.5. MODEL AND EXPERIMENT – OPTIMAL TICKLING FIELD AT ZERO BIAS
As mentioned before, spin valve sensors with an aspect ratio (length/width) of
more than 100:1 have sufficiently low noise even without a bias field Hb. Two such
sensor types were evaluated for optimal signal generation in a biotin-BSA dummy
assay in a range of different tickling fields Ht. The results of these experiments,
together with the model according to equation (12), are shown in Figure 54. The
following four parameters were used to obtain a good fit:
500 nm Sensor 750 nm Sensor
∆Rmax 200 Ω
n*c3 1 mV * Oe / Ω 0.67 mV * Oe / Ω
α 0.025 / Oe
Hk 91 Oe 45 Oe
∆Rmax and n*c3 are simply vertical scaling coefficients. Parameter Hk is the
primary determinant of the optimal tickling field strength, which was found to be ca.
27 Oe (rms) for the 750 nm sensors and ca. 55 Oe (rms) for the 500 nm sensors.
100
Signal Dependence on Fields – Model S. Osterfeld, Apr -14-2008
Signal Dependence on Fields – Experiment S. Osterfeld, July -22-2007
Figure 55: Model and experiment of signal dependence on fields. See also Figure 46c for the original experiment.
6.6. MODEL AND EXPERIMENT – TICKLING AND BIAS FIELD DEPENDENCE
One of the first applications of equation (12) was to check if the dependence of
the signal on bias and tickling field could be explained mathematically. Indeed, the
mathematical model was in very good agreement with the experimentally observed
signal scaling trends. This is shown in a 3D graph in Figure 49 for larger fields, and
again for lower fields in a 2D surface contour plot in Figure 55.
The sensor on which the experimental data in Figure 55 was obtained was
designed to have 1.5 µm wide segments, 100 µm long. It was modeled with equation
(12) using the following parameters:
1.5 µm Sensor
∆Rmax 200 Ω
n*c3 2.62 mV * Oe / Ω
α 0.025 / Oe
Hk 15 Oe
101
Signal Dependence on Sensor Width – Model S. Osterfeld, September 2009
Signal Dependence on Sensor Width – Experiment Osterfeld, Fontana, Boone, et. al, Oct-21-2008
Figure 56: Model and experiment of signal dependence on sensor segment width.
6.7. MODEL AND EXPERIMENT – SENSOR SEGMENT WIDTH DEPENDENCE
The model given by equation (12) can also be used to help interpret the signal
vs. sensor width results which were obtained in collaboration with Hitachi researchers
(see Chapter 5). However, in this application the agreement between model and
experiment is not as good as in the field dependence tests. The reason may be that
different sensors are combined into one experiment, which will introduce fabrication
accuracy and sensor-to-sensor biochemistry uniformity as sources of error.
Nevertheless, the model and experiment agree qualitatively as shown in Figure 56. In
these calculations, the shape bias Hk was allowed to deviate somewhat from the
scaling relationship given in equation (5) to reflect likely deviations of the actual
width from the designed sensor width. Also, the existence of a ca. 100 nm “dead zone”
at each sensor edge (see Chapter 5) was corroborated by the model, and was simulated
by decreasing the ∆Rmax parameter accordingly for successively smaller sensors.
Width, nm 1500 1000 750 600 500 400 375 300
∆Rmax, Ω 87 80 73 67 60 50 47 33
n*c3 0.65 mV * Oe / Ω
α 0.025 / Oe
Hk, Oe 18 26 34 50 70 102 109 136
102
6.8. INSIGHT DERIVED FROM MATHEMATICAL MODELING
Developing the mathematical model was a very helpful exercise, because it
deepened the understanding of the spin valve biosensor – nanoparticle interaction. For
example, initially the expectation existed that a more strongly magnetized nanoparticle
would generate a stronger signal. However, the mathematical set-up shown in Figure
53, by virtue of its leading to a mathematical model which agrees so well with the
experiments, makes it clear that it is not the maximum nanoparticle magnetization that
we seek, but instead the maximum magnetic field rotation in the vicinity of the
particle. In essence, the mathematical model reminds us that the spin valve sensor is
primarily a magnetic field orientation sensor, rather than a field strength sensor. It is
only by appropriately setting up the spin valve sensor that we can measure a magnetic
field strength, i.e., if the external field provides a tangential component (a torque, so to
say), which disturbs the spin valve’s equilibrium orientation somewhat proportionally,
then it becomes possible to infer the magnetic field strength indirectly from its effect
on the free layer orientation.
By recognizing that the spin valve sensor is primarily a field orientation sensor,
it becomes clear that the optimal strategy is to induce a magnetization in the
nanoparticles which is oriented differently from the magnetization of the sensor. This
way, the nanoparticle can exert a torque on the spin valve’s free layer, and the
resulting free layer rotation is the actual source of the signal.
The logical conclusion is that there needs to be a magnetic force component
which acts on the sensor but not on the nanoparticle – if the free layer and particle
were to experience identical forces, they would both be magnetized along the same
direction, and no signal would occur.
The only such magnetic force component which acts on the sensor’s free layer,
but apparently not on the magnetic nanoparticle, is the sensor’s shape anisotropy,
which is modeled by the fictional field Hk in Figure 53. Whether this field is actually
fictional or simply locally constrained so that doesn’t significantly affect the
103
nanoparticle may need to be elaborated in more detail. However, it is clear that the
assumption that Hk affects only the free layer, and never the nanoparticle, leads to a
good mathematical model.
The utility of this conclusion is that Hk can be custom-tailored by choosing the
spin valve segment width appropriately during the sensor design. With increasing Hk,
the spin valve sensor initially becomes more sensitive to nanoparticles due to
increasingly larger differences in the orientation of the magnetizations of the free layer
and the nanoparticle. Eventually, Hk begins to limit the sensitivity of the spin valve
due to excessive free layer stabilization – an excessively stabilized free layer will not
rotate optimally despite the presence of the magnetic torque from nanoparticle.
In summary, the mathematical model helped clarify that it is not the
nanoparticle magnetization which needs to be maximized, but rather the free layer
rotation induced by the nanoparticle is what needs to be maximized. The free layer
rotation was recognized to be a function of the rotational torque exerted by the
nanoparticle (proportional to Hk) times the rotational sensitivity of the free layer
(inversely proportional to Hk). This insight made the experimentally observed signal
scaling trends much more intuitive.
104
6.9. MATHEMATICAL MODELING CONCLUSION
The mathematical model given in equation (12) is simple in the sense that it
only needs two physically measurable parameters Hk and α to describe the sensor-
nanoparticle interaction in relative terms, i.e., to identify optimal field settings. With
the addition of two more parameters c3 and ∆Rmax, the model can furthermore
achieve quantitative accuracy and calculate actual signal levels.
The model has shown that the experimentally obtained signal scaling results
presented in Chapter 5 might have been anticipated from theoretical considerations,
and at a minimum are not in contradiction with the existing models of spin valve
resistance and nanoparticle magnetization.
In fact, the mathematical model has shown very good agreement with
experimental data in several cases, to the extent that the model can itself become a tool
for experimentation. For example, the model shows that even an ideal spin-valve
sensor (without a dead zone at the edge) would not benefit from decreasing the sensor
width indefinitely, because at some point the shape anisotropy field Hk becomes so
large that the sensor’s response is being limited.
Another theoretical experiment that can be performed to some extent with
equation (12) is magnetic noise analysis. One can, for example, derive how strongly
the nanoparticle signal depends on noise, or fluctuations, in the applied and shape bias
fields Ht, Hb, and Hk. More precisely, the optimal choice for Ht, Hb, and Hk is
probably that which maximizes δSignal/δn, and which also achieves a near-zero
magnitude for the terms δSignal/δHt¸ δSignal/δHb¸ and δSignal/δHk. In the signal
vs. parameter landscape, such a location would correspond to a hilltop which is high,
yet relatively flat, and which doesn’t have steep cliffs nearby. With such
considerations it might be possible to define a theoretical magnetic signal to noise
ratio from the various derivatives of equation (12), which could then be used to
identify the optimal sensor parameters in terms of magnetic SNR.
105
APPENDIX A – BIOCHIP FABRICATION PROCESS AT SNF
• Refer to Figure 5 for visualizing the instructions given in this appendix.
• Starting Substrate: The starting substrate is a silicon wafer with 100 – 500 nm
thermal oxide (for electrical insulation) on which the ca. 35 nm thick GMR film
has been deposited. The GMR film should have at least 10% magnetoresistance, a
symmetric MR loop (centered around zero applied field), and the dynamic MR
loop (measured at ca. 100 – 300 Hz) should not differ too much from the static
(measured at ca. 0.1 Hz) MR loop. Additional details about the substrate structure
and procurement are given in several publications by former students (e.g.,
doctoral thesis and papers by Guanxiong Li).
• General Precautions: The metallic GMR film should never be exposed to dirt
(dust), greasy substances (fingerprints), salts (tap water), any temperature above
200° Celsius, and never to corrosive chemicals such as halogen gases or halogen
radicals, acids, or bases.
• Substrate Cleaning: Any protective photoresist should be removed with acetone,
followed by methanol and isopropanol. If dust contamination is suspected, it might
be possible to improve the wafer with a wash on the ultrasonic spin washer on the
EVBonder station. Finally, the wafer should be cleaned with DI water in one of the
automatic wafer washer/spin dryers.
106
• Mask M1 (Sensor): This is the highest resolution layer of the chip. The wafer
should be prepared with a surface silanization in the HMDS YES oven. A suitable
single-layer positive photolithography process (e.g., 1 µm 3612 resist, 2 second
exposure) should then be tested and optimized on several dummy wafers before
being applied to the real wafer. Actual recipes vary with equipment, but for M1 the
lithography should be easy (single layer resist, no alignment needed). If the correct
photoresist pattern has been achieved (verify with microscope), the resist pattern
can then be transferred into the GMR film with ion beam etching, e.g.,
o Veeco Etcher: Primary etch 200 mA, 500 V, 75° Angle, 90 seconds,
followed by edge clean-up etch 150 mA, 400 V, 15° Angle, 100 seconds.
o The transfer of the M1 resist pattern into the GMR film is a timed process,
timed for ca. 150% to 200% of the minimum time needed to perforate the
GMR film.
o Subsequently, the etch trenches are filled with an oxide deposition, e.g.,
transfer of the substrate into the Iontech Ion Beam Sputtering system. Pre-
etch for enhanced adhesion 50 mA, 500 V, 30 seconds. Deposition with
SiO2 target, 100 mA, 1000 V, 90° Angle, 7 minutes (ca. 55 nm of SiO2).
o Resist lift-off. Due to being overcoated with SiO2, the resist is difficult to
remove. 24 h soak in acetone, followed by 10 minutes of sonication in
acetone should remove the resist. If available, the sensor edges should then
be cleaned up (de-burred) with a CO2-snow blaster or sonication in a
suitable abrasive slurry. The wafer should then be cleaned with acetone,
methanol, and IPA.
107
• Mask M2 (Leads): The leads need to be fabricated with lift-off patterning. The
wafer should be prepared with HMDS, and a bi-layer photoresist should be used.
The recipe for applying and patterning a bi-layer resist is much more challenging
than for a single layer resist, so here are some pointers:
o A high quality HMDS prime is required, otherwise the LOL2000
underlayer resist will dissolve too rapidly during development.
o An LOL2000 underlayer is applied by spin coating in the headway spinner.
The technique should be practiced on dummy wafers. Dried LOL2000
residue on the supply bottle may ruin the coating, so pick up from center of
bottle with a pipette or syringe. DO NOT aspirate LOL2000 with a syringe
with a rubber plunger, because the plunger’s silicone oil lubrication will
interfere with the coating process and result in visibly non-uniform
LOL2000 films. Instead, an Eppendorf Repeater® should be used for
aspirating and dispensing LOL2000. Use at least 5 cc of LOL2000 per 4”
wafer (over-apply) to avoid dry streaks.
o The LOL2000 layer needs to be thoroughly baked to achieve a small and
consistent resist undercut profile. The recommended LOL2000 baking
temperature of 150°C is usually too low (undercut too large). Instead, a
bake of 30 minutes at ca. 180°C should be used to achieve a lateral
undercut of ca. 1 µm.
o The upper resist layer of the bi-layer resist can then be applied with a
standard recipe (1 µm 3612 resist). To achieve a good and consistent
undercut profile, it is important that the upper resist layer develops very
quickly. For this reason, the M2 mask should be generously overexposed –
by as much as possible without degrading the features (e.g., 3 seconds).
The resist development is equally critical, because the width of the lateral
undercut depends heavily on the LOL2000 bake temperature and developer
exposure duration. 2x 15 seconds in regular developer are recommended,
108
followed by an immediate and thorough clean in DI water and drying with
N2 (no bake). To remove traces of resist in the exposed areas, the wafer
should be briefly exposed to an O2 plasma (e.g., Drytek 1, descum recipe,
30 seconds).
o The lead metallization can now be applied by evaporation or sputtering.
The typical lead structure is 5 nm Ta, 300 nm Au, 5 nm Ta, but Cr could
also be used in place of Ta to promote Au adhesion. However, it is very
important that the Ta layer on the GMR sensor is broken or roughened
(with in-situ ion beam etching or pre-sputtering) right before deposition of
the lead metallization, otherwise the GMR sensor’s native tantalum oxide
surface will result in a very high resistance at the sensor-lead junction. The
best method for lead deposition is Ion Beam Sputter Deposition with in-
situ Ion Beam Etching, e.g., in Iontech:
Tantalum Base Layer: Pre-etch for enhanced adhesion and low
contact resistance 50 mA, 500 V, 30 seconds. Followed by tantalum
deposition 100 mA, 1000 V, 60 seconds (5 nm).
Gold Middle Layer: Pre-etch for enhanced adhesion and low
contact resistance 50 mA, 500 V, 30 seconds. Followed by gold
deposition 100 mA, 1000 V, 7 minutes (300 nm).
Tantalum Top Layer: Pre-etch for enhanced adhesion and low
contact resistance 50 mA, 500 V, 30 seconds. Followed by
Tantalum deposition 100 mA, 1000 V, 60 seconds (5 nm).
o Resist lift-off. Due to being overcoated with the lead metallization, the
resist will take some time to dissolve. A 24 h soak in acetone, followed by
10 minutes of sonication in acetone, should remove the resist. If available,
the sensor edges should then be cleaned up (de-burred) with a CO2-snow
blaster or sonication in a suitable abrasive slurry. The wafer should then be
cleaned with acetone, methanol, and IPA.
109
• Mask M3 (Sensor Passivation): Since the sensor passivation is thin and only
opened at the bondpads at the chip periphery, where some pattern roughness is
acceptable, a bi-layer photoresist is usually not required. Instead, a simple single
layer resist should be applied (e.g., HMDS treatment, followed by 1 µm 3612
resist, 2 second exposure).
o The sensor passivation is best applied by ion beam sputter deposition in
iontech, with an adequate ion beam cleaning etch prior to each deposition
to enhance adhesion. The typical recipe for a ca. 45 nm oxide-nitride-oxide
passivation in Iontech is as follows:
SiO2 Base Layer: Pre-etch for enhanced adhesion 50 mA, 500 V,
30 seconds. Followed by SiO2 deposition 100 mA, 1000 V, 120
seconds (15 nm).
Si3N4 Middle Layer: Pre-etch for enhanced adhesion 50 mA, 500
V, 30 seconds. Followed by Si3N4 deposition 100 mA, 1000 V,
120 seconds (15 nm).
SiO2 Top Layer: Pre-etch for enhanced adhesion 50 mA, 500 V, 30
seconds. Followed by SiO2 deposition 100 mA, 1000 V, 120
seconds (15 nm).
o Resist lift-off. Due to being overcoated with oxide, the resist may take
some time to dissolve. A 24 h soak in acetone, followed by 10 minutes of
sonication in acetone, should remove the resist. It is not necessary to clean
up the pattern with a CO2-snow blaster. The wafer should be cleaned with
acetone, methanol, and IPA.
110
• Mask M4 (Lead Passivation): Since the lead passivation is relatively thick, a bi-
layer resist is usually required to pattern it by lift-off. However, since the
passivation is only omitted at the large lead bondpads at the chip periphery, where
some pattern roughness is acceptable, the requirements for this bi-layer resist are
similar, but not as stringent as that for Mask M2 (Leads).
o Follow the bi-layer resist instructions given for Mask M2. Compared to the
M2 mask, the large feature size (bond pads) makes moderate resist defects
in the M4 layer more acceptable (e.g., LOL2000 underlayer defects,
varying or very large undercut, small other resist defects, etc.).
o The lead passivation is best applied by ion beam sputter deposition in
Iontech, with an adequate ion beam cleaning etch prior to each deposition
to enhance adhesion. The typical recipe for a ca. 250 nm oxide-nitride-
oxide passivation in Iontech is as follows:
SiO2 Base Layer: Pre-etch for enhanced adhesion 50 mA, 500 V,
30 seconds. Followed by SiO2 deposition 100 mA, 1000 V, 10
minutes (85 nm).
Si3N4 Middle Layer: Pre-etch for enhanced adhesion 50 mA, 500
V, 30 seconds. Followed by Si3N4 deposition 100 mA, 1000 V, 10
minutes (85 nm).
SiO2 Top Layer: Pre-etch for enhanced adhesion 50 mA, 500 V, 30
seconds. Followed by SiO2 deposition 100 mA, 1000 V, 10
minutes (85 nm).
o Resist lift-off. Due to being overcoated with oxide, the resist will take
some time to dissolve. A 24 h soak in acetone, followed by 10 minutes of
sonication in acetone, should remove the resist. The wafer should then be
cleaned with acetone, methanol, and IPA, and coated with a soft-baked
protective resist for subsequent dicing and storage.
111
APPENDIX B – TEMPERATURE CORRECTION
Spin Valve Biosensor Signal (MR) DriftS. Osterfeld, Jan-18-2008
-4
-3
-2
-1
0
1
0 100 200 300 400 500
Minutes
Mic
rovo
lts
Spin Valve Biosensor Centertone (Resistance) DriftS. Osterfeld, Jan-18-2008
0
50
100
150
200
0 100 200 300 400 500
Minutes
Mic
rovo
lts
A B
Resistors
Spin Valve Sensors
Figure 57: Example of temperature-induced drift in the magnetoresistive sideband signal (A) and the sense current (B).
Cyclical signal oscillations were observed on an idle spin valve biochip in an
ordinary lab setting (Figure 57a). These oscillations were found to coincide with the
heating/cooling cycles of the air conditioning system. It was also discovered that this
kind of temperature drift affected only real spin valve sensors, but not ordinary
resistors of comparable resistance, as shown in Figure 57a. This was evidence that the
signal drift originated in the sensor, and not in the signal preamplifier electronics.
It was also discovered that the centertone, or sense current applied to the
sensor, exhibited a very similar but much larger amount of drift as shown in Figure
57b. Due to the amplitude modulation scheme described in the principle of operation,
the centertone (500 Hz) is a measure of the average sense current and hence depends
only on the sensor’s average resistance, while the sideband (350 Hz) depends of both
the sense current and the magnetoresistive signal. Upon examination it was found that
the correlation of the sideband (Figure 57a) and centertone (Figure 57b) of spin valve
sensors on the biochip is very consistent. This observation led to the following
development of a temperature correction approach.
112
Signal (MR) Drift - Corrected with Centertone Signa lS. Osterfeld, Jan-18-2008
-2
-1
0
1
2
0 100 200 300 400 500Minutes
Mic
rovo
lts
Signal (MR) Drift - Corrected with Reference Sensor sS. Osterfeld, Jan-18-2008
-2
-1
0
1
2
0 100 200 300 400 500Minutes
Mic
rovo
lts
A B
Figure 58: The centertone (sense current) drift can indeed be used to correct the signal drift (A). An alternate approach to temperature correction is to subtract a signal from reference sensors (B). A combination of both is also feasible.
The sideband, which contains the magnetoresistance signal, is the primary
signal of interest. While the centertone had been recorded, it had so far largely been
disregarded. However, in light of the observed strong correlation between sideband
and centertone drift, an attempt was made to use the centertone (resistance)
temperature drift to correct the sideband (magnetoresistance) temperature drift. In
particular, if the centertone amplitude is given by CT, and the sideband amplitude by
SB, then temperature correction can be done on an individual sensor basis as follows:
where the exponent α is a correction coefficient which is experimentally
determined, i.e., by cooling the chip with cold spray and optimizing α for optimal
temperature correction. The result of this approach is shown in Figure 58a, which is a
great improvement over the raw data shown in Figure 57a. This approach was
frequently used in real experiments.
However, an alternative approach is to simply subtract a reference signal, i.e.,
from spin valve sensors which are coated with a ~ 300 nm thick oxide. Such sensors
α
=
)(
)(*)()(
0tCT
tCTtSBtSB RawCorrected
113
will behave exactly like real sensors, except that the thick oxide prevents them from
sensing any nanoparticles. The result of this approach is shown in Figure 58b.
Each approach to temperature correction has advantages and disadvantages.
Using the centertone amplitude to correct the sideband amplitude (Figure 58a) has the
advantage that it works with just a single sensor, and that each sensor can be
individually and separately temperature-corrected. The disadvantage of this approach
is that not only temperature can affect the centertone amplitude. For example, a
slightly unstable contact can also cause the sense current to drift. This would be
misinterpreted as a temperature change, and the corrective response would be
disproportionate. Basically, this type of centertone-based, individual-sensor
temperature correction corrects temperature drift, but it greatly exacerbates other types
of signal drift, e.g., drift which originates from contact resistance changes or shifts in
galvanic potential. If these types of drift occur even in small amounts, then the raw
data frequently looks better than the temperature-corrected data obtained with this
correction method. Another disadvantage of this method is that it only corrects for
temperature drift, but not for drift e.g., in the magnetic tickling field.
Using one or several reference sensors to obtain a correction signal (Figure
58b) has the advantage that it corrects for all sources of signal drift which affect the
entire chip fairly uniformly, such as temperature or magnetic field strength.
Furthermore, unlike the centertone-based method, the reference sensor method does
not further amplify signal errors which stem from unstable contacts or small amounts
of sensor corrosion. This improves the device’s fault tolerance in practice. The
disadvantage of this method is that some sensors need to be dedicated to obtain the
correction signal, which reduces the number of available sensors. Furthermore, if the
reference signal is obtained on a chip which is scanned (time domain multiplexed, one
bank of sensors at a time), then for optimal correction each sensor bank would need to
have its own set of reference sensors to ensure simultaneity of the measurement and
the correction signal.
114
APPENDIX C – MATHEMATICA CODE
Clear[R,Hx,Hy,Ravg,dRmax,Ht,Hb,Hk,c3,Mp,a,Hpx,Hpy,n,Sensitivity] Hy=Ht+n*Hpy; (* The y-components acting on the free layer *) Hx=Hb+Hk+n*Hpx; (* The x-components acting on the free layer *) R=Ravg+dRmax/2*Hy/Sqrt[Hy^2+Hx^2]; (*Sin[Theta]=Sin[ArcTan[Hy/Hx]=Hy/Sqrt[Hx^2+Hy^2]*) Hpy=c3*Mp[Hb,Ht]*Ht/Sqrt[Ht^2+Hb^2]; (* Particle y-component *) Hpx=c3*Mp[Hb,Ht]*Hb/Sqrt[Ht^2+Hb^2]; (* Particle x-component *) Signal[Hb_,Ht_,Hk_]=n*FullSimplify[Limit[D[R,n],n->0],Ht>0,Hb>0,a>0] Mp[Hb_,Ht_]=Coth[a*Sqrt[Hb^2+Ht^2]]-1/(a*Sqrt[Hb^2+Ht^2]); (* Langevin Function *) a = 0.025; (* Langevin Parameter *) c3 = 0.001; (* Proportionality Constant *) n = 1; (* Particle Coverage Fraction *) dRmax=200; (* Magnetoresistance in Ohms *) Plot[Signal[0,Ht,45],Signal[0,Ht,91],Ht,0,100,AxesLabel->"Tickling Field, Oe (rms)","Signal, microvolts",LabelStyle->Directive[Bold,12]] (* Signal vs. Ht (tickling field) for two different sensors (zero bias field) *)
20 40 60 80 100Tickling Field, Oe HrmsL
50
100
150
200
250
300
S ignal, microvolts
c3 dRmax Hk HHb+ HkL Ht n Mp@Hb, HtD
2 Hb2 +Ht2 IHHb +HkL2 + Ht2M3ê2
Plot3D[Signal[Hb,Ht,15],Hb,0,60,Ht,0,100,AxesLabel->"Hb","Ht","Signal", BoxRatios->1,1,1,MeshFunctions->#3&,#2&,#1&,Mesh->6,3,3, ColorFunction->"Rainbow",LabelStyle->Directive[Bold,12]] (* Signal vs. Hb (bias field) and Ht (tickling field), 3D Plot and Contour Plot *)
115
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