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Proceedings of the 7th International Workshop on Biosignal Interpretation (BSI2012) 129
Signal Classification Algorithm for the Detection of Abrupt Jumps in CMOS
Nanoelectrode Biosensor Applications
Q Zhu1,2
, T Merelle3,4
, F Widdershoven3, S Van Huffel
1,2,
1 Department of Electrical Engineering - ESAT, SCD - SISTA, Katholieke Universiteit Leuven, Leuven,
Belgium; 2 IBBT Future Health Department, Leuven, Belgium;
3 NXP Semiconductors, Leuven, Belgium; 4 Now at Global Foundries, Dresden, Germany.
Abstract
This paper presents a signal classification algorithm, in order to detect three types of abrupt jumps observed in the signal streams of CMOS nanoelectrode biosensors: an upward event, a dropping event and a random telegraph noise (RTN) event. It is a computationally efficient algorithm, suited for fast signal processing. Although the original purpose of the algorithm was to help improving the nanoelectrode manufacturing process, and to aid the interpretation of the experimental data, it suits various other engineering applications. It is tested on a number of biosensor data sets, acquired with different nanoelectrode materials under different experimental conditions. Good sensitivity and specificity of the algorithm for automatic detection and classification of the 3 different event types is verified by the experimental results.
Keywords Abrupt Jumps, Capacitance, Classification
Algorithm, CMOS Biosensor, Event Detection, FemtoFarad (fF), Frame Rate (FR), Nanoelectrode, Upward/Dropping/Random Telegraph Noise (RTN) Event
1 Introduction
CMOS nanoelectrode biosensors [1] are very
promising for healthcare applications, e.g., personalized
medicine, point-of-care diagnostics, etc. This is because
of a number of advantages of the on-chip electronics,
such as portability, real-time, label-free, miniaturization,
ease of use, and low-cost. The corresponding signal
processing algorithms need to be computationally very
efficient.
One of the major challenges for the CMOS biosensor
data processing algorithms is the automatic detection
and classification of features in a stream of signals, e.g.,
abrupt jumps. When reference liquid (i.e., not
containing biomolecules) is injected, a stationary signal
with constant variability/spread, and without abrupt
jumps and drifts, is expected. In practice, three types of
noise events often occur: an upward (jumping) event, a
dropping event, and a random telegraph noise (RTN)
event (see Fig. 1). The three event types are expected to
have different physical causes, and thus provide
potential feedback for improving the manufacturing
process and the data interpretation.
Existing methods of abrupt jump detection are
usually based on more advanced approaches, such as
wavelet transform and kernel density [2, 3], which
typically are computationally intensive. This paper
presents an efficient algorithm for detection and
classification of upward, dropping, and RTN events in
signal streams from the nanoelectrodes of CMOS
biosensors. Other information like the time stamp and
jump amplitude of an event can also be extracted.
2 Methods
The signal classification algorithm is a two-stage
(a) Drop (b) RTN (c) Upward
Figure 1: Examples of the three event types. (Horizontal axis: time in frame numbers (FN); Vertical axis: capacitance
signal in femtoFarad (fF). The frame rate was 5 Hz.)
Proceedings of the 7th International Workshop on Biosignal Interpretation (BSI2012) 130
analysis. In the first stage, a cut-off value of capacitance,
at which point jumps are most likely to occur, is derived
based on the histogram of the capacitances of the
selected signal stream. In the second stage, an
automated algorithm is implemented, based on the cut-
off value, to detect the existence of jumping events.
The detailed steps of the signal classification
algorithm are described as follows:
At stage one:
1. Classification between noisy and normal signals
– if the histogram of the capacitance values of a
signal stream is unimodal, classify as a normal
event; otherwise (if the histogram is bimodal or
multimodal), classify as a noise event and go to 2;
2. Find the cut-off value of capacitance, based on
the histogram: after ignoring the extreme values
(in capacitance) on both the left and right tails of
the histogram, find the interval Eid (represented
by a bar of the histogram) that shows the least
number of observations (Fig. 2). Define the
middle point c1 (Fig. 3) of the interval Eid as the
cut-off value;
At stage two:
3. Split the signal stream into two groups through
value c1 (Fig. 3);
4. Drift correction: as drift can hinder the detection
of jumping events, it needs to be corrected. Drift
correction is done separately for the two groups.
For each group, use the Frame Number (FN)
(shown as the horizontal axis in Fig. 3) as the
covariate, fit a linear regression to the
capacitance (vertical axis in Fig. 3) values of the
signal stream. The fitted regression line captures
the drift pattern of the signal stream. After
subtracting the fitted regression line from the
signal, the drift is corrected;
5. Check the Grouped Mean Condition: if the
Grouped Mean Condition is satisfied (Fig. 3),
i.e.: 1 1 2 2-2 +2 , go to 6; else classify as a
normal event;
6. Check the Frame Number (FN) Condition:
compare absolute value of the difference in the
means of frame numbers for the two groups
(after rescaling with respect to their standard
deviation), with the student T statistic, i.e, if
2 2
( 1) ( 2)
1) ( 2)0.95, # 2
FN FN
FN FNt FN
(
, go to 7;
else classify as an RTN event;
7. Compare the grouped means: if
( 1) ( 2)FN FN , then classify as an upward
event; otherwise, classify as a dropping event.
0.018 0.02 0.022 0.024 0.026 0.028 0.03 0.032 0.034 0.0360
200
400
600
800
1000
1200
Eid
Figure 2: Graphical representation of Stage 1, of the
signal classification Algorithm.
3 Results
The biosensor used to test the algorithm consists of 256
rows by 256 columns of nanoelectrodes, making up 1
frame of 65,536 electrodes. Each electrode generates a
signal stream to be classified. Applying the algorithm to
each signal stream of one minute length takes less than
0.005s on a state of the art setup of a desktop computer,
which leads to less than 0.1 hour to process these
signals for a certain biosensor chip.
3.1 Corrosion resistance comparison of
two different electrode surface coating
methods
Au2 Au1
DIW
drops 6 227
upward 12 123
RTN 1583 1897
PBS
drops 44 937
upward 4 1153
RTN 380 1460
Table 1: Number of electrodes with the event types, for
the four measurements, detected by the signal
classification algorithm.
Gold coating of the sensor’s copper nanoelectrodes
is done with 2 different procedures, referred to as Au1
and Au2. Au2 protects the underlying copper better
(a) Drop (b) RTN (c) Upward
Figure 3: Steps 3 and 5 of the algorithm, concerning the three event types.
0 500 1000 1500 2000 2500 30000
0.2
0.4
0.6
0.8
1
1.2
1.4
c1
2,
2
1,
1
0 500 1000 1500 2000 2500 30000
0.2
0.4
0.6
0.8
1
c1
2,
2
1,
1
0 500 1000 1500 2000 2500 30000.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
c1
1,
1
2,
2
Proceedings of the 7th International Workshop on Biosignal Interpretation (BSI2012) 131
against corrosion from the injected liquid than the
previously used Au1. Hence, it is expected that less
noise events are detected when using the Au2 coating
technique.
To compare the corrosion resistance of the two
coating methods, their performance was tested in two
different liquids: de-ionized water (DIW) and phosphate
buffered saline (PBS). PBS, which contains 0.15
mol/liter (150mM) chloride ions, is known to be far
more corrosive to copper than DIW. Therefore, more
noise events are expected for PBS than for DIW,
especially in combination with the worse coating
method Au1 (which is more porous than Au2). For this
reason, the signal classification algorithm can be tested,
based on four types of measurements: Au2 DIW, Au2
PBS (150mM), Au1 DIW, and Au1 PBS (150mM). Ten
minutes of stable signals for each of the four
measurements were considered.
Table 1 shows the number of events detected by the
algorithm. From Table 1, the number of noise events is
clearly less when using Au2 compared with Au1,
especially the upward and dropping events. The upward
events can be explained by pinholes in electrodes that
have an inhomogeneous surface composition. Such
electrodes are susceptible to sudden corrosion at weak
spots at boundaries between different crystallographic
phases (refer to Figure 4). This leads to a sudden
increase in the effective surface area, and consequently,
in the electrode capacitance (which increases with
increasing surface area). Dropping events can be
explained by electrodes that are completely dissolved
during a major corrosion event, while RTN may be
generated by the transistors in the sensor chip.
Figure 4: SEM (scanning electron microscope) picture
taken with a backscattered electron detector (for optimal
chemical contrast). The two electrodes on the left have
inhomogeneous surfaces. Brighter regions are gold-rich;
darker regions are copper-rich.
The finding is in line with the expectation that Au2
coating is much more robust against corrosion than Au1
coating. This is not only proved by the much fewer
events for the Au2, compared with the Au1
measurements, but also by the relatively constant
upward/dropping events for PBS and DIW
measurements with Au2. In other words, when the new
Au2 is used, the corrosion effect is no longer sensitive
to the compositions of the injected liquid. On the other
hand, when using Au1, a significantly larger amount of
upward/dropping events was detected for PBS than for
DIW, since PBS is more corrosive.
Figure 5 shows the mappings of the (jumping)
events for the four measurements on the electrode array
of the biosensor chip. Strong column-dependent
(vertical) effects of the RTN events are observed,
caused by transistors in the peripheral read-out circuits.
All electrodes on the same column share the same read
out circuit. This proves the robustness of the algorithm.
From Figure 5d, which shows the events detected for
the Au1 PBS measurement, the upward and dropping
events scatter across the array. These events are very
likely to be corrosion related ones.
The false positive rates of event detection, for Au2
DIW, Au2 PBS, Au1 DIW, and Au1 PBS, are
respectively 1.06%, 0.23%, 0.45% and 0.45%, which
are quite negligible. These false positive events are all
detected as RTN events, the type of which is the most
difficult to detect among the three types. There is no
misclassification among the three event types.
3.2 Corrosion resistance for different
fluid compositions
In this section we study the corrosion resistance of
nanoelectrodes coated with the Au2 method to fluids of
different composition. The experiment consists of 2
sub-experiments, one with DRY (in air) and DIW, and
the other with DIW and three steps of increasing
concentrations of PBS: 10mM, 50mM and 150mM,
respectively. For each step, 8 minutes of relatively
stable signals were recorded for the application of the
electrode classification algorithm.
Table 2 shows the average amplitudes of jumping
events for different experimental steps. As expected, the
robustness against corrosion of the Au2 coating is not
only reflected by a low number of upward/dropping
events (as discussed in Section 3.1), but also by a
relatively constant amplitude of the jumping events for
different fluid compositions – Table 2 shows that the
amplitude is quite constant for all the steps, except for a
slight increase at the highest 2PBS concentrations.
DRY DIW
Exp1 0.0057 0.0053
DIW 10mM 50mM 150mM
Exp2 0.0055 0.0050 0.0067 0.0070
Table 2: Average amplitudes (in fF) of jumping events
for the six experimental steps.
Proceedings of the 7th International Workshop on Biosignal Interpretation (BSI2012) 132
50 100 150 200 250
50
100
150
200
250
Column
Row
AuCuDIW(180211)
50 100 150 200 250
50
100
150
200
250
Column
Row
AuelessDIW(070910)
(a) Au2 DIW (b) Au1 DIW
50 100 150 200 250
50
100
150
200
250
Column
Row
AuCuPBS(110411)
50 100 150 200 250
50
100
150
200
250
Column
Row
AuelessPBS(160910)
(c) Au2 PBS (d) Au1 PBS
Figure 5: Mapping of dropping events (blue), upward events (green), and RTN events (magenta) on the electrode array of
the biosensor chip, for the four measurements.
4 Conclusions
The signal classification algorithm gives precious
insights in the proportion of electrodes damaged by
corrosion during an experiment as well as on the
amplitude of these events. Apart from these factors,
dropping events which are spatially correlated, i.e.,
clustered on the biosensor array, and detected around
the same time instant, can point to, e.g., an air-bubble
during the experiment. The algorithm was developed as
a tool for the improvement of the nanoelectrode coating
process, by extracting corrosion-induced features from
noisy signals, in the presence of air-bubbles or dirt
particles. It suits fast signal processing applications,
with good sensitivity and specificity.
The algorithm is methodologically straightforward,
and can be immediately applied to various alternative
engineering tasks, for classifying different jumping
events in signal streams.
Acknowledgements
The research was supported by NXP Semiconductors;
Research Council KUL: GOA MaNet; Belgian Federal
Science Policy Office: IUAP P6/04 (DYSCO,
`Dynamical systems, control and optimization', 2007-
2011).
References
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Merelle, H. Suy, F. Jedema, R. Hoofman, C. Tak, A. Sedzin,
B. Cobelens, E. Sterckx, R. van der Werf, K. Verheyden, M.
Kengen, F. Swartjes, and F. Frederix. CMOS biosensor
platform. Technical Digest International Electron Devices
Meeting IEDM (2010), 816-819.
[2] M. Chabert, J. Y. Tourneret, and F. Castanie. Additive
and multiplicative abrupt jump detection using the continuous
wavelet transform. IEEE Trans. on Acoustics, Speech, and
Signal Processing (1996), 3002-3005.
[3] D. N. Nikovski, and A. Jain. Fast Adaptive Algorithms
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Address for correspondence:
Qi Zhu Department of Electrical Engineering - ESAT, SCD - SISTA, Katholieke Universiteit Leuven, Leuven, Belgium IBBT Future Health Department, Leuven, Belgium Address: Kasteelpark Arenberg 10, bus 2446
B-3001 Leuven, Belgium [email protected] or [email protected]