Snapshots of The Research Projects

Arvind K. Srivastava

Overview of Arvind’sResearch Interests & Current Projects

200nm200nm

Scan and Data AcquisitionScan and Data Acquisition

MFC SettingsMFC SettingsMFC DiagnosticMFC Diagnostic

Sensor Selection & DiagnosticsSensor Selection & DiagnosticsTemperature SettingsTemperature Settings

PCA1

PCA2

CH3OH

C2H5OH

C2H5CO

CHCl3

C6H6

PCA1

PCA2

CH3OH

C2H5OH

C2H5CO

CHCl3

C6H6

Bio-inspired Computation

Cd-SnO2

Zn-SnO2

5 µm+

+

Cd-SnO2

Zn-SnO2

5 µm+

+

Microsensors & MEMS Arrays

Grain Neck200nm30nm 200nm30nm

200nm95nm 200nm95nm

Nanopatterning & Nanometrology Signal Processing & Embedded SystemsF u n c tio n 1

86420-2-4-6- 8

Fun

ctio

n 2

6

4

2

0

-2

-4

-6

C lu s t

G r

6

5

4

3

2

1

6

5

4

3

2

1

C280.01uF

CS2_FG

F0CAP_

+

C21

1 uf

5V

IME0

(0 - 5V)

0V

F4A

IME7

74LS279

U1712356

1011121415

4

7

9

13

1R1S11S22R2S3R3S13S24R4S

1Q

2Q

3Q

4Q

F4CAP_

5V

12V

F3B

DIO_7

DIO5_

74LS138

U1615141312111097

123

546

Y0Y1Y2Y3Y4Y5Y6Y7

ABC

G2BG2AG1

F2CAP_

(0 - 5V)

0V

74LS279

U1912356

1011121415

4

7

9

13

1R1S11S22R2S3R3S13S24R4S

1Q

2Q

3Q

4Q

MFC2

F2A

IME5

DIO7_

-5V

5V

+

C23

1 uf

+

C24

1 uf

DIO11_

IME3

-5V

MFC0

CS5_FG

F1CAP_

+C22

1 uf

5V

74LS138

U20123

645

15141312111097

ABC

G1G2AG2B

Y0Y1Y2Y3Y4Y5Y6Y7

F5A

DIO0_

5V

F6CAP_

(+/-2.4V)

0V

F2B DIO12_

MFC4

IME4

F3CAP_

5V

5V

DIO2_

12V

F3A

CS6_FG

0V

F0A

0V

Center for Bioelectronics, Biosensors and Biochips (C3B), VCU 1.6

Interface Circuit-A

A

1 26Monday, March 29, 2004

Title

Size Document Number Rev

Date: Sheet of

C310.01uF

F4B

-2.4V

(0 - 5V)

CS0_FG

DAC8800

U15

45

10

123

20191817161514138

76

9 1211

VOUTBVOUTC

CLK

VREFL1VREFH1VOUTA

VREFL2VREFH2VOUTHVOUTGVOUTFVOUTE

VSSLDSDI

VDDVOUTD

CLK CLRGND1

C250.01uF

DIO3_

PELTDAC8800

U13

45

10

123

20191817161514138

76

9 1211

VOUTBVOUTC

CLK

VREFL1VREFH1VOUTA


VSSLDSDI

VDDVOUTD

CLK CLRGND1

(+/-2.4V)

74LS139

U2123

1

4567

AB

G

Y0Y1Y2Y3

DIO8_

C300.01uF

F7CAP_

IME1

0V

F5CAP_IME6

C32

0.01uF

0V

F0B

F5B

2.4V

+C20

1 uf

MFC3

12V

-2.4V

CS3_FG(0 - 5V)

F7A

CS7_FG

F1A

F6B

-2.4V

DIO10_

0V

F6A

DIO_9

DIO6_

C270.01uF

DIO1_0V

C29

0.01uF

GND

CS1_FG

MFC1

(0 - 5V)

+2.4V

DAC8800

U18

45

10

123

20191817161514138

76

9 1211

VOUTBVOUTC

CLK

VREFL1VREFH1VOUTA


VSSLDSDI

VDDVOUTD

CLK CLRGND1

+2.4V

(0 - 5V)

12V

F7B

C260.01uF

DAC8800

U14

45

10

123

20191817161514138

76

9 1211

VOUTBVOUTC

CLK

VREFL1VREFH1VOUTA

VREFL2VREFH2

VOUTHVOUTGVOUTFVOUTE

VSSLDSDI

VDDVOUTD

CLK CLRGND1

GND

DIO4_

5V

12V

F1B

CS4_FG

IME2

-5V

Institute for NanotechnologyMaterials Science and Engineering Northwestern UniversityWebsite: http://vpd.ms.northwestern.edu/memberpages.asp?url=members/arvind/arvind.htm

PARC ENGINE

KNOWLEDGE BASE

(k classes)

OUTPUT PREDICTOR Class (j)

TRAIN TEST

Xj

ARRAY PROCESSOR

X1j

X2j

X3j

X4j

SENSOR PROCESSOR

SENSOR PROCESSOR

SENSOR PROCESSOR

SENSOR PROCESSOR

ACTIVE MATERIAL

ACTIVE MATERIAL

ACTIVE MATERIAL

ACTIVE MATERIAL

V1j(t)

V2j(t)

V3j(t)

V4j(t)

TRANSDUCER

TRANSDUCER

TRANSDUCER

TRANSDUCER

Mimicking Sense of Olfaction – Basis of my research

Olfactory receptors

Our nose has millions of tiny sensory elements (called olfactory receptors). When we inhale odor molecules, they get attached to the receptors and activate olfactory neurons through which odor signals are transmitted to the brain for identification.

Our nose has millions of tiny sensory elements (called olfactory receptors). When we inhale odor molecules, they get attached to the receptors and activate olfactory neurons through which odor signals are transmitted to the brain for identification.

Human being can distinguish over 10,000 different odor molecules at the concentration down to one part per million (1 in 106), whereas dogs have sensing capability nearly 100 million times lower than ours.

Human being can distinguish over 10,000 different odor molecules at the concentration down to one part per million (1 in 106), whereas dogs have sensing capability nearly 100 million times lower than ours.

To mimic biological olfaction into electronics, so called Electronic Nose (ENOSE), what we essentially need is an array of sensors with partial overlapping sensitivity followed by suitable data processing and pattern recognition tool.

To mimic biological olfaction into electronics, so called Electronic Nose (ENOSE), what we essentially need is an array of sensors with partial overlapping sensitivity followed by suitable data processing and pattern recognition tool.

Interesting thing about these olfactory cells is that none of these cells is specific to any gas, even though we have remarkable sensing capability.

Interesting thing about these olfactory cells is that none of these cells is specific to any gas, even though we have remarkable sensing capability.

Signal ProcessingSignal ProcessingDevice PhysicsDevice PhysicsMaterials ScienceMaterials Science

Gas Sensitive MaterialGas Sensitive Materiale- Exchange

Active Site

Data Analysis and Pattern RecognitionData Analysis and Pattern Recognition

?Class 2

Class 1

Class 3

ENOSE – An Interdisciplinary Research Subject

INPUT ODOUR

(j)

My Research Agenda – Four Broad Fronts

Overall goal is to integrate these toolsets into a single

framework to build neuromorphic chemical detection system for the

applications where humans have limitations.

Microsensors & MEMS

MOS Capacitor Gas Sensor

Cd-SnO2

Zn-SnO2

5 µm+

+

Cd-SnO2

Zn-SnO2

5 µm+

+

New

Signal Transduction M

ethod For T

racking Gas-Solid Interaction Phenom

ena

p-Si

Al

SiO2

Pt

Substrate Contact

VG

p-Si

Al

SiO2

Pt

Substrate Contact

VG

MEMS Sensors

Chemoresistive Gas Sensors

Organic Thin Film Transistor (OTFT) Gas Sensor

Feature Extraction, D

ata Transform

ation, Identification and Q

uantification, Statistical Data Analysis (PCA, Regression)

F unction 1

86420-2-4-6-8

Fun

ctio

n 2

6

4

2

0

-2

-4

-6

C

6

5

4

3

2

1

Bio-inspired Computation

Artificial Neural Networks (Pattern Recognition, Forecasting, Control, & Modeling)

Genetic Algorithm (Optimization, Data Mining)

Nanopatterning & Nanometrology

Patt

erni

ng/ A

ssem

blin

g &

Pro

bing

(N

ew S

ensi

ng P

rinc

iple

)

DPN PatterningEBL Patterning

100x

Electrode gap 1μm

Polypyrrole

300nm200nm30nm 200nm30nm

200nm95nm 200nm95nm

200nm200nm

AFM tip

“Ink”

Scan Direction

Substrate

Water Meniscus

AFM tip

“Ink”

Scan Direction

Substrate

Water Meniscus

AFM tip

“Ink”

Scan Direction

Substrate

Water Meniscus

AFM tip

“Ink”

Scan Direction

Substrate

Water Meniscus

(Nanocale site and shape specificity and high reproducibility)

Controlled Nanostructures

Grain Neck

Assembling nano-colloids with tailored interfaces

(100nm)

G asU V (365nm )

+ _

ZnO

Insu la to r

G asU V (365nm )

+ _

ZnO

Insu la to r

Instrumentation Interfacing (Multi-Sensor Array Test-Bed)

Signal Processing & Embedded Systems

Adv

ance

d Se

nsor

Exc

itatio

n, P

aram

eter

E

xtra

ctio

n an

d M

easu

rem

ent

Readout and Interface Electronics

Optical excitation

GP

IB

M K S M F C 14 79

N 2

2 ,

10 0s cc m10 sc c m

10 sc c m10 sc c m

F lo w M e te r

MK S 6 4 7C F low C o n t r o lle r

In le tO u tle t

GP

IB

GP

IB

RS223

K e ith le y 24 2 0 S o u rc e M ete r

A g ilen t 3 49 80 A 4 0 C h a n n e l S w tch in gS y ste m / D M M

K e ith le yS C S 4 20 0

G as C h am b erC on s tan t Tem p . W ate r B a th

E xh au s t

G U I D ev e lo p e d in L A B V IE W

E lec tro d e g a p 1 μm

10 0 x

2 0 0 xM ic ro C en tile ve rA rray

L A B V IE W C o d in g

S c a n a n d D a t a A c q u i s i t i o nS c a n a n d D a t a A c q u i s i t io n

M F C S e t t in g sM F C S e tt in g sM F C D i a g n o s t i cM F C D ia g n o s ti c

S e n s o r S e le c t i o n & D i a g n o s t ic sS e n s o r S e le c tio n & D i a g n o s t i c sT e m p e r a t u re S e t tin g sT e m p e r a tu re S e tt in g s

GP

IB

M K S M F C 14 79

N 2

2 ,

10 0s cc m10 sc c m

10 sc c m10 sc c m

F lo w M e te r

MK S 6 4 7C F low C o n t r o lle r

In le tO u tle t

GP

IB

GP

IB

RS223

K e ith le y 24 2 0 S o u rc e M ete r

A g ilen t 3 49 80 A 4 0 C h a n n e l S w tch in gS y ste m / D M M

K e ith le yS C S 4 20 0

G as C h am b erC on s tan t Tem p . W ate r B a th

E xh au s t

G U I D ev e lo p e d in L A B V IE W

E lec tro d e g a p 1 μm

10 0 xE lec tro d e g a p 1 μm

10 0 x

2 0 0 x2 0 0 xM ic ro C en tile ve rA rray

L A B V IE W C o d in g

S c a n a n d D a t a A c q u i s i t i o nS c a n a n d D a t a A c q u i s i t io n

M F C S e t t in g sM F C S e tt in g sM F C D i a g n o s t i cM F C D ia g n o s ti c

S e n s o r S e le c t i o n & D i a g n o s t ic sS e n s o r S e le c tio n & D i a g n o s t i c sT e m p e r a t u re S e t tin g sT e m p e r a tu re S e tt in g s

Center for Bioelectronics, Biosensors and Biochips (C3B), VCU 1.6

Feedback Resistor Logic Circuit - A

A

1 26Thursday, April 22, 2004

Title

Size Document Number Rev

Date: Sheet of

R77

500

FR7

U44 ICM7555

5 3

7

8

426

CONTV OUT

DISCH

V+

RESETTRIGTHOLD

D_C

+-

U55A

MAX414321

411

R81

500

+-

U55C

MAX4141098

411+-

U55B

MAX414567

411

C71 0.01uF

+-

U56C

MAX4141098

411

U50

D74

LS04

98

+-

U53D

MAX414121314

411

U51

C74

LS04

56

U50

F74

LS04

1312

D106

1N4001

FR2

FR13

U50

B74

LS04

34

R78

500

+-

U56A

MAX414321

411

R82

500

FR3

+-

U55D

MAX414121314

411

U51

D74

LS04

98

+-

U53B

MAX414567

411

FR8

U45A 74LS107

1

12

4

3

2

13

J

CLK

K

Q

QCL

P56 20K

D_B

R71

500

P572K

FR9

U52

B74

LS04

34

R80

500

-5V

R73

500

AI'''4

FR15FR14

EXT_TRIG_

D_A

U50

E74

LS04

1110

R79

500

+-

U53A

MAX414321

411

FR4

U50

A74

LS04

12

U52

D74

LS04

98

R19

61K

5V

+-

U54B

MAX414567

411

R69

500

+-

U53C

MAX4141098

411

U51

F74

LS04

1312

R76

500

R64

RB

U51

E74

LS04

1110

U50

C74

LS04

56

FR10

+-

U54A

MAX414321

411

+

-

U47AMAX412

3

21

84

U46MM74HC154

1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 17

2423 22 21 20 18 19

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

VCCA B C D G1

G2

+-

U54C

MAX4141098

411

U48A

74LS86

1

23

+

-

U47BMAX412

5

67

84

FR0

R74

500

P585K

FR5

FF-A

R63

RA

R70

500

GND

FR6

U49A

74LS04

1 2

R83

500

R72

500

C70 CD_D

+-

U54D

MAX414121314

411 +-

U56D

MAX414121314

411+-

U56B

MAX414567

411

U51

A74

LS04

12

U52

A74

LS04

12

FR11

R75

500

R68

500

U45B 74LS107

8

9

11

5

6

10

J

CLK

K

Q

QCL

FR1

FR12

U51

B74

LS04

34

U52

C74

LS04

56

R19

51K

FF-B

D107

1N4001

Temperature modulationAC excitation

Time (Sec.)

SiOx base

Heater

SiOx PtSnO2

+ _

RL

~

SiOx base

Heater

SiOx PtSnO2

+ _

RL

~

SiOx base

Heater

SiOx PtSnO2

+ _

RL

~

Gas

Insulator

Impedance analyzer

(f =…. Hz)

V~

I~,

Gas

Insulator

Impedance analyzer

(f =…. Hz)

V~

I~,V

~V~

I~I~,

Optical micrograph of DPN patterned polypyrrole (a) and an array of SnO2 (b) based chemoresistors. (c) Sensing response of polypyrrole to increasing concentrations of acetone and ethanol showing fast response (≈10s) and fast recovery (<20s). (d) Response of SnO2sensor array to different gas..

E le c t ro d e g a p 1 μm

1 0 0 x3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 1 0 0 0 1 1 0 0

0 . 0

2 0 0 . 0 f

4 0 0 . 0 f

6 0 0 . 0 f

8 0 0 . 0 f B ia s V o l t a g e = 5 VC a r r ie r G a s = N i t r o g e n

E t h a n o lA c e t o n e

P u r g eE x p o s u r e 1 0 0 s c c m

1 7 5 s c c m1 5 0 s c c m

1 2 5 s c c m

7 5 s c c m

5 0 s c c mCur

rent

(Am

p.)

T im e ( S e c . )

2 5 s c c m

12

34

56

78 0 . 0

0 . 1

0 . 2

0 . 3

0 . 4

Acetonitril

ChloroformToluene

Sensor

Response

12

34

56

78 0 . 0

0 . 1

0 . 2

0 . 3

0 . 4

Acetonitril

ChloroformToluene

Sensor

ResponseC d - S n O 2

Z n - S n O 2

5 µ m+

+1 ) S n O 22 ) T i - S n O 23 ) C o - S n O 24 ) N i - S n O 25 ) C u - S n O 26 ) Z n - S n O 27 ) C d - S n O 28 ) P t - S n O 2

V = 5 μ l

( a )

( b )

( c )

( d )

E le c t ro d e g a p 1 μm

1 0 0 x3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 1 0 0 0 1 1 0 0

0 . 0

2 0 0 . 0 f

4 0 0 . 0 f

6 0 0 . 0 f

8 0 0 . 0 f B ia s V o l t a g e = 5 VC a r r ie r G a s = N i t r o g e n

E t h a n o lA c e t o n e

P u r g eE x p o s u r e 1 0 0 s c c m

1 7 5 s c c m1 5 0 s c c m

1 2 5 s c c m

7 5 s c c m

5 0 s c c mCur

rent

(Am

p.)

T im e ( S e c . )

2 5 s c c m

12

34

56

78 0 . 0

0 . 1

0 . 2

0 . 3

0 . 4

Acetonitril

ChloroformToluene

Sensor

Response

12

34

56

78 0 . 0

0 . 1

0 . 2

0 . 3

0 . 4

Acetonitril

ChloroformToluene

Sensor


Z n - S n O 2

5 µ m+


V = 5 μ l

12

34

56

78 0 . 0

0 . 1

0 . 2

0 . 3

0 . 4

Acetonitril

ChloroformToluene

Sensor

Response

12

34

56

78 0 . 0

0 . 1

0 . 2

0 . 3

0 . 4

Acetonitril

ChloroformToluene

Sensor


Z n - S n O 2

5 µ m+

+

C d - S n O 2

Z n - S n O 2

5 µ m+


V = 5 μ l

( a )

( b )

( c )

( d )

Sol-Gel Precursors

AFM tip“Ink”

Scan Direction

Substrate

Water Meniscus

AFM tip“Ink”

Scan Direction

Substrate

Water Meniscus

Novel light induced gas sensing at room temperature. Top row: Schematic of light induced gas sensing (left), Surface Potential Image of ZnO thin film showing UV modulation of surface adsorbed oxygen. Bottom row: SEM Image of soft-eBl fabricated polycrystalline ZnO nano sensor (left), Typical response of nano sensor to methanol under UV illumination (middle) and PCA of sensor response to different VOCs (right).

Sol-Gel Precursers Dip Pen NanolithographySoft E-Beam Lithography

200nm30nm 200nm30nm

200nm200nm200nm

Thin film

200nm200nm

Thin film

200nm95nm 200nm95nm

Towards Next Generation High Performance Chemical Sensors Based on Charge Transport Across Grains

Grain Neck

200nm200nm

Change of grain size with pattern dimension: Grain size decreases with characteristic pattern

Top-down fabrication of Au contacts

String of ZnO nano colloids with sharp interfaces

Nanopatterned Chemoresistors and Integrated Array(Team members: Suresh Donthu (Graduate Student), Arvind K. Srivastava (Res. Asso.) , Mohd. Aslam (Res. Asso.))

Lithography- Variable

Pressure eBL

Surface Treatment- O2 Plasma

- SAM treatment

Precursor Spin- Inorganic sols

- Polymeric solutions- Colloidal Solutions

Lift-off

Annealing

Resist

Substrate

Lithography- Variable

Pressure eBL


- SAM treatment


- SAM treatment





Lift-offLift-off

AnnealingAnnealing

Resist

Substrate

(100nm)

150nm5um

10um

150nm5um

10um

UV LED(λ=365nm, W = 1200-3400μW)

Au Electrodes

e-BL Patterned 150nmx10μm ZnO lines

150nm5um

10um

150nm5um

10um

UV LED(λ=365nm, W = 1200-3400μW)

Au Electrodes

e-BL Patterned 150nmx10μm ZnO lines

Extra Electron left after capture of Hole

Hole is strongly drawn to Chemisorbed Oxygen. Converts it to Physically Adsorbed due to its electronegativity.

Electrons tends to be trapped by Physically adsorbed Acceptor

Cond. band.

Filled band.

ZnO Oxygen Gas

Ener

gy

+

o o-hν




Cond. band.

Filled band.

ZnO Oxygen Gas

Ener

gy

++

o o-hν

)(2)(2

)(2)(2

gOadOh

ehh

adOegO

→−++

−++→

−→−+

ν (λUV ≥3.4eV )




Cond. band.

Filled band.

ZnO Oxygen Gas

Ener

gy

+

o o-hν




Cond. band.

Filled band.

ZnO Oxygen Gas

Ener

gy

++

o o-hν

)(2)(2

)(2)(2

gOadOh

ehh

adOegO

→−++

−++→

−→−+

ν (λUV ≥3.4eV )

)(2)(2

)(2)(2

gOadOh

ehh

adOegO

→−++

−++→

−→−+

ν (λUV ≥3.4eV )

0.0E+00

1.0E-11

2.0E-11

3.0E-11

4.0E-11

5.0E-11

6.0E-11

7.0E-11

0.0 0.3 0.6 0.9 1.2 1.5 1.8 2.1

Time (Sec. x 1000)

Cur

rent

(Am

p.

4.0V 3.8V 3.6V

PurgeExposure (20sccm) Purge

Exposure (30sccm) Purge


Total Flow Rate During Purge/ Exposure =100sccmCarrier Gas: Air

D.C. Bias Across UV LED

Test Gas : CH3OH

0.0E+00

1.0E-11

2.0E-11

3.0E-11

4.0E-11

5.0E-11

6.0E-11

7.0E-11

0.0 0.3 0.6 0.9 1.2 1.5 1.8 2.1

Time (Sec. x 1000)

Cur

rent

(Am

p.

4.0V 3.8V 3.6V

PurgeExposure (20sccm) Purge



Total Flow Rate During Purge/ Exposure =100sccmCarrier Gas: Air

D.C. Bias Across UV LED

Test Gas : CH3OH

4.0V

3.8V

3.6V

-2.5

-1.5

-0.5

0.5

1.5

2.5

-3 -2 -1 0 1 2 3PCA 1 (50.5%)

PC

A 2

(39.

6%)

c3

c2

c1

a1a2

a3

e1 e2 e3

b3

b2b1

d1 d2d3

Methanol

Ethanol

Acetone

Chloroform

Benzene*1=20sccm

*2=30sccm

*3=40sccm 4.0V

3.8V

3.6V

-2.5

-1.5

-0.5

0.5

1.5

2.5

-3 -2 -1 0 1 2 3PCA 1 (50.5%)

PC

A 2

(39.

6%)

c3

c2

c1

a1a2

a3

e1 e2 e3

b3

b2b1

d1 d2d3

Methanol

Ethanol

Acetone

Chloroform

Benzene*1=20sccm

*2=30sccm

*3=40sccm

568.3mV

500nm Grain size ≈ 80nm

568.3mV

500nm

568.3mV

500nm

568.3mV


568.3mV


568.3mV

500nm

568.3mV568.3mV

500nm500nm Grain size ≈ 80nm

568.3mV

500nm

568.3mV

500nm

568.3mV568.3mV

500nm500nm

568.3mV

500nm

568.3mV

500nm

568.3mV568.3mV

500nm500nm

In Air In Air with UV On In O2 with UV On

05

1015

2025

3035

4045

50

0 10 20 30 40 50 60Pe rcenta ge Hydrogen

S=(V

T| g-V

T| o)/V

T| o

0.0E+00

1.0E-12

2.0E-12

3.0E-12

4.0E-12

5.0E-12

6.0E-12

7.0E-12

8.0E-12

-3.00 -2.00 -1.00 0.00 1.00 2.00 3.00V G( Volts )

C (F

arad

)

0% H 2

10% H20% H 2

20% H20% H 2

30% H20% H 2

40% H20% H 250% H2

0% H 2

AC Bias = 20K Hz

2.5E-12

2.7E-12

2.9E-12

3.1E-12

3.3E-12

3.5E-12

3.7E-12

3.9E-12

0 500 1000 1500 2000 2500 3000 3500 4000

Time (sec.)

Cap

acita

nce

(fara

d)

100KHz 500KHz

Exposure Purge

DC Bias=0V

n-Si <100>, ?=2-10.5O-cm

Al

SiO2

Pt

Substrate Contact

VG

n-Si <100>, ?=2-10.5O-cm

Al

SiO2

Pt

Substrate Contact

VG

Thermally grown oxide (thickness 14nm)

Sputtered Pt (thickness 75nm) Dots dia=100, 80,

60, 40, 20μm

Thermally grown oxide (thickness 14nm)

Al back mettalization(annealed at 400°C in air

for 15 min.

Pt-SiO2-Si MIS Structure (dot dia= 100μm). 75nm thick sputtered gate metal. 500x Mag.

AFM topography of sputtered Pt

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0% 10% 0% 20% 0% 30% 0% 40% 0% 50% 0%

Hydrogen Conc.

VF

(-1*v

olts

)

20kHz 50Khz 100kHz 500kHz 800kHz

4.8E+10

5.0E+10

5.2E+10

5.4E+10

5.6E+10

5.8E+10

6.0E+10

10% 20% 30% 40% 50%

Hydrogen Conc.

Inte

rface

Tra

p D

ensi

ty (e

Vxcm

2 )-1 20kHz 50Khz 100kHz 500kHz 800kHz

5.40

5.45

5.50

5.55

5.60

5.65

5.70

0% 10% 0% 20% 0% 30% 0% 40% 0% 50% 0%

Hydrogen Conc.

Wor

k Fu

nctio

n of

Pla

tinum

(eV

)

20kHz 50Khz 100kHz 500kHz 800kHzC

VG

With H

Without H

Constant C

ΔV

Constant V

ΔC

C

VG

With H

Without H

Constant C

ΔV

Constant V

ΔC

C

VG

Accumulation

di

di

CCCC+Cmin

Low Frequency

High Frequency

VT

CFB

Inversion

Depletion

Weak

Strong

dAC oxi /0εε= C

VG

Accumulation

di

di

CCCC+Cmin

Low Frequency

High Frequency

VT

CFB

Inversion

Depletion

Weak

Strong

dAC oxi /0εε=

Two different ways of measurement

Reason for gas sensitivity is due to the change in work function of the metal due to dipole layer at the interface by hydrogen atom

Operation of MISC in depletion and inversion region makes it interesting for sensing purpose

Response of 60μm dia dot of Pt-MOS Capacitor to hydrogen. Sampling interval = 30sec., Purge and Exposure time = 5min each. Carrier Gas – Nitrogen (100sccm). Test frequencies – 20, 50, 100, 500 and 800KHz. A.c. bias=500mV.

InducedH2

Ha HaO

Ha

Metal

SiO2

+

-+-

+

-+-

+

-+-

+

-+-

Ha

+

-+-

+

-+-

+

-+-

+

-+-

H2O

+

-+-

+

-+-

+

-+-

+

-+-

OH

Group PermanentInducedH2

Ha HaO

Ha

Metal

SiO2

+

-+-

+

-+-

+

-+-

+

-+-

Ha

+

-+-

+

-+-

+

-+-

+

-+-

H2O

+

-+-

+

-+-

+

-+-

+

-+-

OH

Group Permanent

Response Time≈40sec. (to reach 90% of max. value), Recovery time Time>200sec. (to reach 90% of min), with base line drift.

H2 conc.

(5000 to 50,000ppm)

WorkFunction

OxideCharge

SurfacePotential

BulkDoping

Box

d

ox

iMST C

QCQV Φ++⎟⎟

⎠

⎞⎜⎜⎝

⎛−Φ= 2

WorkFunction

OxideCharge

SurfacePotential

BulkDoping

Box

d

ox

iMST C

QCQV Φ++⎟⎟

⎠

⎞⎜⎜⎝

⎛−Φ= 2

VFB (Flat Band Voltage)

MO

S P

aram

eter

s E

xtra

ctio

n fro

m C

V/ G

vR

espo

nse

• Response and recovery of MOS capacitor are governed by metal work function.

•Interface tarp density increases with hydrogen conc. This is undesirable and causes slow drift in MOS response.

Typi

cal r

espo

nse

of M

OS

C to

H2

Hybrid Modified-Gate Field Effect Transistor (MG-FET)

MGFET consists of an isolated capacitor with an air gap and a physically separated field effect transistor, connected via a floating gate. Completely eliminates the problem of hydrogen diffusion.

IC Compatible MIS Device for Hydrogen Sensing(Team Members: Arvind K. Srivastava (Res. Asso.), Mike Miller (Graduate Student))

All-electronics MEMS Platform for Bio-Chem Sensing(Team Members: Soo-Hyun Tark (Graduate Student), Arvind K. Srivastava (Res. Asso.), Gajendra Shekhawat (Res. Asst. Prof.))

Stressed induced change in drain current in MOSFET is due to increase in mobility as a result of both longitudinal and transverse stess.

MOSFET embedded cantilever serves as a novel test-bed for investigating effect of stress (compressive and tensile) in controlling carrier mobility. This is important for improving performance of scaled transistor.

Finger E lectrodes

E m bedded M O SFE T

B ottom C ontact

1. Em ploys three different sensing m echanism s (res istive , capacitive and m echanica l) a ll in tegrated in to a s ing le entity sharing com m on sensing layer.

2. M easurem ent o f m ultip le param eters w ill enable detection the target gas w ith greater confidence. 3. P rovides us an opportun ity of having better understanding of gas sensing m echanism

H ybrid M E M S A rchitecture - M ethod to increase device perform ance through m aultiparam eterm easurem ent.

Cross-section view of cantilever for hybrid sensing

C onducting Polym er

Thin O xide S iO 2

B ottom S ilicon

S i3N 4 Passivation LayerG old F inger E lectrodes

Schem atic o f proposed hybrid sensor architecture system

R

SGDId

C

Silicon N itride Passivation Layer

Vsg

V sd

B ottom S iC ontact

C onductivity M easurem ent

-R esponse

C apacitive M easurem ent

R

SGDId

C M O SFET

Silicon N itride W indow

Au F inger E lectrodes

Vsg

V sd

B ottom S iC ontact


M echanicalR esponse


D PND PN

Fabricated m ulti-input H ybrid D evice

Finger E lectrodes

E m bedded M O SFE T

B ottom C ontact

1. Em ploys three different sensing m echanism s (res istive , capacitive and m echanica l) a ll in tegrated in to a s ing le entity sharing com m on sensing layer.

2. M easurem ent o f m ultip le param eters w ill enable detection the target gas w ith greater confidence. 3. P rovides us an opportun ity of having better understanding of gas sensing m echanism

H ybrid M E M S A rchitecture - M ethod to increase device perform ance through m aultiparam eterm easurem ent.

Cross-section view of cantilever for hybrid sensing


Thin O xide S iO 2

B ottom S ilicon



Thin O xide S iO 2

B ottom S ilicon


Schem atic o f proposed hybrid sensor architecture system

R

SGDId

C


Vsg

V sd

B ottom S iC ontact


-R esponse


R

SGDId

C M O SFET



Vsg

V sd

B ottom S iC ontact




D PND PN

R

SGDId

C


Vsg

V sd

B ottom S iC ontact


-R esponse


R

SGDId

C M O SFET



Vsg

V sd

B ottom S iC ontact




D PND PND PN

Fabricated m ulti-input H ybrid D evice

(A) Schematic of the interaction between probe and target molecules on an embedded-MOSFET cantilever system. The silicon nitride cantilever is a reference, and the gold-coated one is used as a sensing cantilever. Specific biomolecular interactions between receptor and target bend the cantilever. Magnified view of embedded MOSFET in cross- section shows stressed gate region when cantilever bends, resulting in change of drain current due to conductivity modulation of the channel underneath the gate. (B) Schematic of change in a MOSFET drain current upon probe-target binding. (C) Change in drain current over time due to deflection of a microcantilever.

(A) SEM image of two microcantilevers (from a 50 × 1 array) displaying embedded MOSFET and geometry of the gold-coated and SiNx cantilever beam pair; each cantilever is about 250 µm long, 1.5 µm thick, and 50 µm wide. (B) Details of MOSFET location on cantilever beam, which is released by etching a 2.5 µm sacrificial oxide layer.

Goat IgGRabbit IgG

DTSSPGold

(A) Measured ID versus VD characteristics for embedded n-MOSFET transistor at VG = 5 V for detection of goat ant-rabbit antibodies (secondary IgG) by rabbit antibodies (primary IgG). (B) Interaction of rabbit IgG and goat anti-rabbit IgG on a gold coated cantilever over time at a fixed drain voltage of 2 V.

Deflection

Carrier transport

Enhanced modulation of the channel region

10X1 Array Deflection Carrier transport

1D doping profile of the S/D junction region

0.0 0.2 0.4 0.6 0.8 1.01E15

1E16

1E17

1E18

1E19

1E20

Con

cent

ratio

n (c

m-3)

Depth (μm)

Boron P as doped P after annealing

Label- and optics-freeHigh sensitivity

Detection of ~5 nm deflection by analyteconcentration in ppt range

Simple direct current measurement with large signal-to-noise ratioDirect integration with application-specific microelectronicsMassively parallel signal sensingEnable creating a portable deviceMass-production at low-cost

Grow field oxide (1 μm, 1000 °C, 3hrs)

SOI Wafer

Open S/D implant windowS/D implantation

Clear cantilever area

Grow gate oxide (30 nm) S/D implant activation

Silicon nitride deposition(40 nm, 850 °C, 3 hrs)

Define cantilevers

Open S/D contact window

Metallization

Release cantilevers(RIE Si, SiO2)

Significance of the platform

MOSFET cantilever performance evaluation Detection of antibody-antigen binding

(A) Calibration of drain current vs. deflection (inset shows current senstivity of MOSFET with cantilever bending: 0.2 to 0.8 mA per 1 nanometer deflection); (B) 1/f noise in MOSFET (The noise calculated by integrating the PSD over 1/f bandwidth is approximately 40-100 nA); (C) Signal to noise ratio of MOSFET

Carrier transport direction parallelto the cantilever deflection

Carrier transport direction perpendicular to the cantilever deflection

New MOSFET-embedded microcantilever design nMOSFET device and process simulation

Simulated IV characteristics

0 2 4 6 8 100

2

4

6

8

10

Dra

in c

urre

nt (m

A)

Drain voltage (V)

Gate voltages = 1-10 V, 1 V stepCalculated threshold voltage = 0.698 V

2D structure and doping profile of the SD junction region

Conceptualization of MOSFET-embedded microcantilever sensor paradigm comprising two-dimensional microcantilever arrays with embedded-MOSFETs.An adsorption-induced surface stress at the functionalized cantilevers leads to precise, measurable and reproducible change in the MOSFET drain current. This new electronic transduction approach will allow massively parallel signal sensing and integration with application-specific on-chip microelectronics platform.

S/D implantation E=35 KeVS/D doping dose=1e15 cm-2Activation temp= 900 °CActivation time= 30 min

Shallow S/D with high doping concentration required for sensing surface stressHigh ID desirable for detecting small bendingLow VG for low power consumption

Device & process optimization to achieve:

10 100 100010-19

10-18

10-17

10-16

10-15

10-14

Cur

rent

noi

se s

pect

ral d

ensi

ty (A

2 /Hz)

Frequency (Hz)

VG= 1 V VG= 2 V VG= 3 V

0 50 100 150 200 250 300 350 4000.40.60.81.0

10203040506070

Dra

in c

urre

nt (m

A)

Deflection (nm)

Drain current vs. deflection at VG= 5 V measured using Zyvex nanomanipulator system.

5000 10000-100

-90

-80

-70

-60

-50

dBV

Frequency (Hz)

DC signal

SNR=42.7 dB

Noise floor

5000 10000-100

-90

-80

-70

-60

-50

dBV

Frequency (Hz)

DC signal

SNR=42.7 dB

Noise floor

Increase the MOSFET aspect ratio to increase ID level

Microfabrication of MOSFET cantilevers

(A) SEM image of two microcantilevers (from a 50 × 1 array) displaying embedded MOSFET and geometry of the gold- coated and SiNx cantilever beam pair; each cantilever is about 250 µm long, 1.5 µm thick, and 50 µm wide.

(B) Details of MOSFET location on cantilever beam, which is released by etching a 2.5 µm sacrificial oxide layer.

FabfricatedMicrocantilever Array Chip

( )1 2

S

4 5 6 7 8 9 10 10 9 8 7 6 5 4 3 2 1L R

Au

SiN

x

D

G

31 2

S

4 5 6 7 8 9 10 10 9 8 7 6 5 4 3 2 1L R

Au

SiN

x

D

G

3

Finite Element Analysis stress simulation

L o a d R e f . = -1 0L o a d R e f . = -1 0

2)(2 GD

oxD VV

LWCI −= μ

Z Control (Manual)

Semiconductor Parameter Analyzer

(SCS4200)

PC

(I vs t)ADC

Z Control (Manual)


(SCS4200)

PC

(I vs t)ADC

0 50 100 150 200 250 300 3501.96m

1.97m

1.98m

1.99m2.97m

2.98m

2.99m

3.00m

3.01m

3.02m -1010-108-106-104-102-100Load Reference

-10

I d (am

p.)

Time (sec.)

Vd=2, Vg=5V Vd=2, Vg=5V Vd=2, Vg=8V Vd=7, Vg=5V

Transverse Stress (σ2)

Longitudinal Stress (σ1)

Stre

ss C

ompo

nent

ZZ

DistanceDistance

E

EdE/dkdE2/dk2

k ∝ 1/l

2*

2

*

22*

221

21 kp

mmvmE h

===

22

2*

kdEd

m h=

2

22

*

)(

hkd

Edtq

mtq==μ

• Under the influence of stress, curvature of energy band becomes sharper. • Sharper energy band causes effective mass of electron to decrease, which in turn increases the

carrier mobility in MOSFET.• Increase in carrier mobility is the reason why drain current increases.

Increase in carrier mobility with stress has important technological significance in improving the performance of scaled transistor. MOSFET embedded cantilever serves as a nice test-bed for investigating the effect of tensile strain and compressive stress in scaled devices.

0 2 4 6 8 101550

1600

1650

1700

1750

1800

1850

Mob

ility

(cm

2 / V

-s)

Ref. Load

Finite Element Analysis stress simulation

L o a d R e f . = -1 0L o a d R e f . = -1 0

2)(2 GD

oxD VV

LWCI −= μ

Z Control (Manual)


(SCS4200)

PC

(I vs t)ADC

Z Control (Manual)


(SCS4200)

PC

(I vs t)ADC

0 50 100 150 200 250 300 3501.96m

1.97m

1.98m

1.99m2.97m

2.98m

2.99m

3.00m

3.01m

3.02m -1010-108-106-104-102-100Load Reference

-10

I d (am

p.)

Time (sec.)

Vd=2, Vg=5V Vd=2, Vg=5V Vd=2, Vg=8V Vd=7, Vg=5V

Transverse Stress (σ2)

Longitudinal Stress (σ1)

Stre

ss C

ompo

nent

ZZ

DistanceDistance

E

EdE/dkdE2/dk2

k ∝ 1/l

2*

2

*

22*

221

21 kp

mmvmE h

===

22

2*

kdEd

m h=

2

22

*

)(

hkd

Edtq

mtq==μ

• Under the influence of stress, curvature of energy band becomes sharper. • Sharper energy band causes effective mass of electron to decrease, which in turn increases the

carrier mobility in MOSFET.• Increase in carrier mobility is the reason why drain current increases.

Increase in carrier mobility with stress has important technological significance in improving the performance of scaled transistor. MOSFET embedded cantilever serves as a nice test-bed for investigating the effect of tensile strain and compressive stress in scaled devices.

0 2 4 6 8 101550

1600

1650

1700

1750

1800

1850

Mob

ility

(cm

2 / V

-s)

Ref. Load

Impedimetric ENOSE(Team Member: Arvind K. Srivastava (Res. Asso.))

Interrogating the sensor at an

optimum frequency results in large

change in magnitude and

phase as a finger print of test analyte.

Peltier SettingsPeltier Settings

SensorSelection

SensorSelection

VOC SelectionVOC Selection

Sensor DiagnosticSensor DiagnosticVOC SelectionVOC Selection

MFC SettingsMFC SettingsSensor SelectionSensor Selection

Peltier SettingsPeltier Settings

SensorSelection

SensorSelection

VOC SelectionVOC Selection

Sensor DiagnosticSensor DiagnosticVOC SelectionVOC Selection

MFC SettingsMFC SettingsSensor SelectionSensor Selection

USB30USB30

Breaks 3.3V, +/-5V, +/-12V into

+2.4V, -2.4V, +5V, -5V, 9V,

12V, +15V, -15V

16CH, 4 Analog O/p,

24 DIO, 250KS/s,

14Bits

Power Supply

Data Acquisition Card

Valve Driver Board

4:1 Valve 4:1 Valve Sensor Array

MFC MFC MFC MFCMFC

Mother Board

FG

SA

SI

TC

USB30USB30USB30USB30USB30USB30

Breaks 3.3V, +/-5V, +/-12V into

+2.4V, -2.4V, +5V, -5V, 9V,

12V, +15V, -15V

16CH, 4 Analog O/p,

24 DIO, 250KS/s,

14Bits

Power Supply

Data Acquisition Card

Valve Driver Board

4:1 Valve 4:1 Valve Sensor Array

MFC MFC MFC MFCMFC

Mother Board

FG

SA

SI

TC

Interaction of gas-solid being primarily limited to dispersion/ diffusion, dipolar and hydrogen bonding interaction results in the formation of surface charge and modulation of bulk conductivitywhich is typical for each gas-solid interaction and can be represented as a complex RC network drifting with the passage of time.

By interrogation the sensor at different frequencies variations in electrical parameters of such type of system can be studied and hence can be used as a chemical finger prints for sensitive and selective detection.

Interdigitated gold microelectrode coated

w ith polypyrrole (doped w ith chloride counter anion) and exposed to

isopropyl alcohol

|Z|

Frequency (H z)

Frequency (H z)

The

ta

Z”

Z ’



M ethanol

|Z|

Frequency (H z)

Frequency (H z)

The

ta

Z”

Z ’



isopropyl alcohol

|Z|

Frequency (H z)

Frequency (H z)

The

ta

Z”

Z ’



isopropyl alcohol

|Z|

Frequency (H z)

Frequency (H z)

The

ta

Z”

Z ’



M ethanol

|Z|

Frequency (H z)

Frequency (H z)

The

ta

Z”

Z ’-8.00

-6.00

-4.00

-2.00

0.00

2.00

4.00

-0.30 -0.20 -0.10 0.00 0.10 0.20 0.30 0.40 0.50

(|z|g-|z|o)/|z|o

(Pg-

Po)/P

o

EthanolAcetonePropanolMIAKDichloroethaneMethanolIsopropyl Alcohol

Prin

cipl

eProof of C

onceptM

easu

rem

ent o

f Im

peda

nce

(|ZD

UT|)

and

Pha

se (Ø

) : P

rinci

ple

ZFeedbackDUT

~ Vi-+ Vo

|ZDUT|=ZFEEDBACK/|Av|

ZFeedbackDUT

~ Vi-+ Vo


(555 Timmer)

VMAG

VMAG=30x(+5dB)+900

VMAG=30x(-5dB)+900

J

K

J

KCL

CL

XOR

CLK

CLK

Q

Q’

Q

Q’

UP

DN

Up/Dn4-bit Counter IC

ABCD

ABCD

1 2 16. . . . . . . 4-to-16 Line Decoder IC

. . . . . . . 1 162SPST Analog Switch

Feedback Resistor Logic

DUT

~ Vi

ZFeedback

-+


INPA

INPB

AD8302

Gain/Phase

Analyzer IC

VMAG

VPHS

INPA

INPB

AD8302

Gain/Phase

Analyzer IC

VMAG

VPHS

180 ° PhaseShifter

90 ° PhaseShifter

VMAG=30x20log(V2/V1)+900

VPHS=-10(| ø (V2)-ø (V1)|-90)+900


Ø=-tan-1(Av”/Av

’)

+180-180 -90 +900

+900

+30dB-30dB 0

1.8V

0V

0.9V

1.8V

0V

VPH

S

0V

0.9V

VM

AG

1.8V

0.9V

-180

VPH

S(90

)+

ve-v

e

-90 +180

(555 Timmer)

VMAG

VMAG=30x(+5dB)+900

VMAG=30x(-5dB)+900

J

K

J

KCL

CL

XOR

CLK

CLK

Q

Q’

Q

Q’

UP

DN

Up/Dn4-bit Counter IC

ABCD

ABCD

1 2 16. . . . . . . 4-to-16 Line Decoder IC

. . . . . . . 1 162SPST Analog Switch

Feedback Resistor Logic

DUT

~ Vi

ZFeedback

-+


INPA

INPB

AD8302

Gain/Phase

Analyzer IC

VMAG

VPHS

INPA

INPB

AD8302

Gain/Phase

Analyzer IC

VMAG

VPHS

180 ° PhaseShifter

90 ° PhaseShifter

VMAG=30x20log(V2/V1)+900

VPHS=-10(| ø (V2)-ø (V1)|-90)+900


Ø=-tan-1(Av”/Av

’)

+180-180 -90 +900

+900

+30dB-30dB 0

1.8V

0V

0.9V

1.8V

0V

VPH

S

0V

0.9V

VM

AG

1.8V

0.9V

-180

VPH

S(90

)+

ve-v

e

-90 +180

Impedim

etricEN

OSE: H

ardware &

Software

Sen

sitiv

ity E

nhan

cem

ent

Selectivity Enhancement

Gas Sensitive MaterialGas Sensitive Materiale- Exchange+ -

Insulator

Active Site

Gas Sensitive MaterialGas Sensitive Materiale- Exchange+ -

Insulator

Active Site

VINA

VINB

VOUTA = VSLP log( VINA/VZ )

VOUTB = VSLP log( VINB/VZ )

Phase

Gain

0

500

1000

1500

2000

2500

400 450 500 550 600 650 700 750 800 850 900

Time (Sec.)

Res

ista

nce

(Ohm

s)

P U R G E (N2 100 Sccm) E X P O S U R E (C7H8 10 sccm + N2 100 Sccm)

Freq. (mHz) Purge Phase Exposure Phase f1 50 30 f2 100 70 f3 150 110 f4 198 150

0

2 0 0

4 0 0

6 0 0

8 0 0

10 0 0

12 0 0

14 0 0

16 0 0

18 0 0

0 : 08 0 : 08 0 : 03 0 : 04 0 : 03 0 : 03 0 : 03 0 : 08 0 : 09 0 : 04 0 : 08 0 : 01 0 : 01 0 : 01 0 : 01 0 : 09 0 : 04 0 : 03 0 : 0 2 0 : 02 0 : 02

T i m e

Volta

ge

(mV

)

8 Oct

T e m p era t u re

H um d i t y

9 O ct 10 Oct

1 1 Oc t

12 Oct

1 3 O ct

14 Oct

1 5 Oc t

16 Oct

1 7 Oc t 1 8 Oc t

1 9 Oc t 2 0 Oc t 21 Oct

2 2 Oc t 23 O ct 2 4 Oct 25 O ct 2 6 Oct 27 Oct 2 8 Oc tT W T F S S M T W T F S S M T W T F S S M

0

2 0 0

4 0 0

6 0 0

8 0 0

10 0 0

12 0 0

14 0 0

16 0 0

18 0 0

0 : 08 0 : 08 0 : 03 0 : 04 0 : 03 0 : 03 0 : 03 0 : 08 0 : 09 0 : 04 0 : 08 0 : 01 0 : 01 0 : 01 0 : 01 0 : 09 0 : 04 0 : 03 0 : 0 2 0 : 02 0 : 02

T i m e

Volta

ge

(mV

)

8 Oct

T e m p era t u re

H um d i t y

9 O ct 10 Oct

1 1 Oc t

12 Oct

1 3 O ct

14 Oct

1 5 Oc t

16 Oct

1 7 Oc t 1 8 Oc t

1 9 Oc t 2 0 Oc t 21 Oct

2 2 Oc t 23 O ct 2 4 Oct 25 O ct 2 6 Oct 27 Oct 2 8 Oc tT W T F S S M T W T F S S M T W T F S S M

Train Data: Oct. 8-Oct 28

0

5 00

1 0 00

1 5 00

2 0 00

2 5 00

0: 0 9 6 : 0 9 12 : 0 9 1 8 : 09 0 : 0 9 6 :0 9 12 : 0 9 1 8: 0 9 0 : 09 6: 0 9 1 2 :0 9 18 : 0 9 0 :0 9 6 : 0 9 1 2: 0 9 1 8 : 09 0: 0 9 6 : 09 12 : 0 9 1 8 :0 9T i m e

Volta

ge (

mV

D e c . 5 T

D e c . 6 F

D ec . 7 S

D e c . 8 S

D e c . 9 M

Hu m id i t y

T e m p era t u re

0

5 00

1 0 00

1 5 00

2 0 00

2 5 00

0: 0 9 6 : 0 9 12 : 0 9 1 8 : 09 0 : 0 9 6 :0 9 12 : 0 9 1 8: 0 9 0 : 09 6: 0 9 1 2 :0 9 18 : 0 9 0 :0 9 6 : 0 9 1 2: 0 9 1 8 : 09 0: 0 9 6 : 09 12 : 0 9 1 8 :0 9T i m e

Volta

ge (

mV

D e c . 5 T

D e c . 6 F

D ec . 7 S

D e c . 8 S

D e c . 9 M

Hu m id i t y

T e m p era t u re

Test Data: Dec. 5-Dec.9

Field Application of Gas Sensors – Predictive Alarm Algorithm

Left: Training data (from Oct. 8-Oct. 28) and Test Data (from Dec. 5-Dec. 9) collected from the site. Data sets consists of the response of four Figaro sensors, one temperature and one humidity sensor.

Middle: Two stage NN based Predictive Alarm System for on-line monitoring and prediction of odor profile.

Right: Prediction of future odor profile of one of the gas sensors in next 30min. Predicted response perfectly matches with the target response (R = 1.00)

Classification and Decision Making (Neuro-Genetic Pattern Classifier)

Left: Genetic evolution of NN. PCA transformed data greatly simplifes the NN learning.

Middle: Two parents Multi Point Restricted (MRX) Crossover and FAST encoding scheme.

Right: Classification performance of backpropagation trained NN and Genetically trained and evolved NN. Genetically trained/ evolved NN outperforms backpropagation trained NN in gas identification problems.

Feature Generation, Feature Selection and Dimensionality Reduction

Left: By modulating the operating temperature sensors followed by subsequent FFT of the response patterns, chemically significant features typical for each gas-solid pair can be generated.

Right: PCA loading and score plots of the gas sensor array response in 2D space showing relative contribution of sensors and spatial distribution of clusters of the test gases. PCA not only reduces the dimension of the data set but can also be used to knockout redundant features.

Biological Olfaction to Machine Olfaction

T G S 2 6 2 0 (5 V )

T G S 2 6 1 1 (2 V )

T G S 2 6 1 1 (3 .5 V )

T G S 2 6 1 1 (5 V )

T G S 8 2 6 (2 V )

T G S 8 2 6 (3 .5 V )

T G S 8 2 6 (5 V )

T G S 8 2 1 (2 V )

T G S 8 2 1 (3 .5 V )

T G S 8 2 1 (5 V )

T G S 2 6 2 0 (2 V )

T G S 2 6 2 0 (3 .5 V )

-3

-2

-1

0

1

2

3

4

5

-3 -2 -1 0 1 2 3 4 5 6

P C A 1 (4 0 .4 % )

PCA

2 (2

5.2%

)

a 3

b 2 b 3b 1c 3

a 2d 3

d 2

c2c 1

e 1

d 1

e 3e 2

a 1

N H3

C 7 H 8

C 2 H5 C O

C H C l3

C 2 H5 O H

VOC Classification

0

10

20

30

40

5060

70

80

90

100

Training Test Training Test Training Test

Back Propagation GA - W eight GA - Topology

Clas

sific

atio

n %

79%86%

93%

VOC Classification

0

10

20

30

40

5060

70

80

90

100

Training Test Training Test Training Test

Back Propagation GA - W eight GA - Topology

Clas

sific

atio

n %

79%86%

93%

79%86%

93%

C ro sso ver o p era tion (M R X op era to r)… .{ O / p , B i a s , S y n a p t i c 1 , W t . 1 , S y n a p t i c 2 , W t 2 , S y n a p t i c 3 , . . } …

G en etic rep resen ta tio n o f a n eu ro n fo r w eigh ts an d top o log y evo lu tio n

P re dic tio n o f F u tu re R esp on se (1 st S ta g e N N )

D A F a cto r S c ore (F 1 a n d F 2 )

C la ss Id en tif ica tio n (2 nd S ta g e N N )

C lu ste r #

D A F a cto r S c ore s (F 1 a n d F 2 )


C lu ste r #

S E N T IN E L (R aw D ata)

F il te ring a n d N orm a liza tion

Id entification of c ur re nt resp onse

K no w led g e B a se

P re d_ v .da t P re d_ w .da t


C la ss_ v .da t C la ss_ w .da t


F 1 a n d F 2 C o e ff. (O ct 2 0 2 8 , 0 2 )

Id entification of p red icted resp onse

P re dic tio n o f F u tu re R esp on se (1 st S ta g e N N )

D A F a cto r S c ore (F 1 a n d F 2 )


C lu ste r #

D A F a cto r S c ore s (F 1 a n d F 2 )


C lu ste r #

S E N T IN E L (R aw D ata)

F il te ring a n d N orm a liza tion

Id entification of c ur re nt resp onse


P re d_ v .da t P re d_ w .da t


C la ss_ v .da t C la ss_ w .da t


F 1 a n d F 2 C o e ff. (O ct 2 0 2 8 , 0 2 )

Id entification of p red icted resp onse

00 . 10 . 2

0 . 30 . 40 . 50 . 60 . 7

1 5 1 1 0 1 1 5 1 2 0 1 2 5 1 3 0 1 3 5 1 4 0 1 4 5 1 5 0 1 5 5 1 6 0 1

N o . o f s a m p l e s

No

rm.

Vo

ltag

T g t - C h 6 P d t - C h 5R=1.00

?S en so r A rra y P rep ro cess in g F ea tu re E x tra ctio n

a n d F ea tu r e S e lec tio nD im en sio n a lity R ed u ctio n

C la ss if ica tio n an d D ec is io n M a k in g

P a tte rn s o u tp u t (R a w m ea su re m en ts)

N o r m a liz ed m ea su re m en ts

F ea tu re v e cto r O u tp u t c la ss P o st p ro c essed o d o r c la ss

T im eSens

or R

espo

nse

F ea tu re 1

Feat

ure

2

C la ss 2

C la ss 1

C la ss 3 ?

S en so r A rra y P rep ro cess in g F ea tu re E x tra ctio n a n d F ea tu r e S e lec tio n

D im en sio n a lity R ed u ctio n

C la ss if ica tio n an d D ec is io n M a k in g

P a tte rn s o u tp u t (R a w m ea su re m en ts)

N o r m a liz ed m ea su re m en ts

F ea tu re v e cto r O u tp u t c la ss P o st p ro c essed o d o r c la ss

T im eSens

or R

espo

nse

T im eSens

or R

espo

nse

F ea tu re 1

Feat

ure

2

F ea tu re 1

Feat

ure

2

C la ss 2

C la ss 1

C la ss 3

C la ss 2

C la ss 1

C la ss 3

C la ss 1

C la ss 3

Intelligent Pattern Classifier for Machine Olfaction(Team Member: Arvind K. Srivastava (Res. Asso.))

Robotics Robotics -- Underwater DetectionUnderwater DetectionUnderwater Odor Sensing Robot with GPS Locator

Water

Mine

Detection

Telemedicine Through B

reath Monitoring

Telemedicine Through B

reath Monitoring

Real-Time Monitoring of Patient’s Condition Through Breath Analysis

ICU Patient

Mon

itorin

g S

uspe

ct C

hem

ical

s fo

r Hom

elan

d S

ecur

ityM

onito

ring

Sus

pect

Che

mic

als

for H

omel

and

Sec

urity

Base Station (Data Analysis)

Nano Sensors Dust Biomimetic

Odor Sensing Insect

Chemical Sensing: Proposed Applications

Embedded MOSFET

ReferenceReferenceCantileverCantilever

CMOS Differential Amplifier

SensorSensorCantileverCantilever

Radio TransmitterMicrocontroller

DSP (FFT/ PCA/feature Selection)

Implemented into FPGA

Embedded MOSFET

ReferenceReferenceCantileverCantilever

CMOS Differential Amplifier

SensorSensorCantileverCantilever

Radio TransmitterMicrocontroller

DSP (FFT/ PCA/feature Selection)

Implemented into FPGA

Smart Chemical Sensor

Education

Snapshots of The Research Projects