university of kentucky, auburn universityslide 1 system level design of chemical sensing...

University of Kentucky, Auburn University Slide 1

System Level Design of Chemical Sensing

Microsystems

D.M. WilsonUniversity of Kentucky, Electrical Engineering

T. Roppel and M.L. PadgettAuburn University, Electrical Engineering

April 2, 19982nd Southeastern Workshop

Mixed-Signal VLSI and Monolithic Sensors


Outline

Project Goals System Architecture System Analysis Results of (sample) System

Analysis Modularization of sensing solution Front-end Processing Back-end Processing Summary


Project Goals

Develop a Chemical Sensing Framework for adaptability to a variety of low-cost or modular chemical sensing applications• Characteristics

– Good reproducibility among batch -fabricated sensors– High sensitivity through low noise transduction of sensory signal – Reduction in communication bandwidth via local signal

processing– Resistance to drift via adaptable pattern recognition engine

• Phases– Sensing Technology Development– Sensory Plane Signal Processing Design and Implementation– Base Station Processing and Interactive Feedback


System Architecture

Sensing Nodes:• Sensing Technology• Large Arrays of Sensors• Local Signal Processing• Smart A/D Conversion

Features Communicated over Standardized Protocol

Base Station Processing stimulus


System ArchitectureFeedforward Objectives

Raw Sensor Output

Raw Sensor Output

Raw Sensor Output

Raw Sensor Output

Raw Sensor Output

Raw Sensor Output

Robust Aggregate

Output

Robust Aggregate

Output

Robust Aggregate

Output

Feature Extraction

Feature Extraction

Conversion to Data Transfer Protocol


Pattern Recognition and Interpretation


System ArchitectureFeedback Objectives

Sensor Control

Sensor Control

Sensor Control

Sensor Control

Sensor Control

Sensor Control

Feedback Feedback Feedback

Distribution of Parameters


Definition of Array Parameters and Constraints


Distribution of Parameters


Components of System Architecture Design

Analyze the problem Determine a block-level solution Modularize the solution

Establish communication protocols between modules

Build and characterize modules Integrate modules Test System


Analyzing the Chemical Sensing Problem

Arrays of discrete sensors (tin-oxide powder) Initial Data Collection

• wide range of array characteristics (temperature, dopant type)• representative set of chemicals

Use of science to determine initial set of features Clustering and Analysis of raw data Determination of optimal array size

Principal Component Analysis of Steady-state features

Principal Component Analysis of Temporal Features


Initial Architecture for Feature Analysis

Size: 15 sensors Type: Tin-oxide powder

• TGS822: alcohol sensitivity• TGS880: ammonia sensitivity• TGS813: carbon monoxide sensitivity

Array Dimensions• Three types of sensors, Five operating

temperatures • Operating temperatures from (320-420 deg C)

Six Representative Chemicals: Acetone, butanol, ethanol, methanol, propanol, xylene


Principal Component and Feature Analysis

Raw data Steady-State Features

• median of array• baseline-immune response• saturated slope

Temporal (transient) Features• Time to threshold• Mean of first d erivative• Initial first derivative (beginning of transient response)• Initial saturated output (end of transient response)


Results: Raw Data

0 5000

1

2

3

4

5acetone

0 5000

1

2

3

4

5butanol

0 5000

1

2

3

4

5ethanol

0 5000

1

2

3

4

5methanol

0 5000

1

2

3

4

5propanol

0 5000

1

2

3

4

5xylene

Sens

or C

ircu

it R

epso

nse,

V

Time, x 600ms


Results: Deep Saturation

PC 15 10 15

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

a

a aa

aa

a

a

aa

aa

e

ee

e ee

e

e

ee e

e

m m

m

m

m

mm

mm

mm

m

a

a aa

aa

a

a

aa

aa

e

ee

e ee

e

e

ee e

e

m m

m

m

m

mm

mm

mm

m

PC

2

Deep Saturation

Time

Sens

or R

espo

nse

(d)(c)

(b)

(a)

thr

tref

For clarity, only acetone, methanol, and ethanol clusters are shown. One possible outlier is indicated by

ANN Result:95% correct discriminationAvg. of 100 trialsTwo different BP models

Feature (d)

PCA


Results: Initial Saturation

Time

Sens

or R

espo

nse

(d)(c)

(b)

(a)

thr

tref


Feature (c)

5 6 7 8 9 10 11 12-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

aa

a

a

aaa

a

a

a

a

a

e

ee

ee

ee

ee

e

e

e

m

mmm

m

m m

m

m

m

mm

PC 1

Initial Saturation

PC

2

PCA


0 0.05 0.1 0.15 0.2 0.25-0.04

-0.02

0

0.02

0.04

0.06

0.08

a

a

aaaa

a

a

a

a

a

a

e

e

e

e eee

e

e

e

ee

mm

m

m

m

m

mm

m

m

m

m

PC 1

PC

2

Slope

Results: Transient Slope

Time

Sens

or R

espo

nse

(d)(c)

(b)

(a)

thr

tref


Feature (b)

PCA


Results: Time-to-Threshold

10 20 30 40 50 60 70 80 90 100

-40

-30

-20

-10

0

10

20

30

40

50

a a

a

a

a

a

a

a

a

a

a

a

e

e

e

e

e

e

e

e

e

ee

em

mm

m

m

mm

mm

m

mm

PC 1

PC

2

Time-to-Threshold

a?m?

Time

Sens

or R

espo

nse

(d)(c)

(b)

(a)

thr

tref


Feature (a)

PCA


Transient Results- New Data

0 0.01 0.02 0.03 0.04 0.05-0.01

-0.008

-0.006

-0.004

-0.002

0

0.002

0.004

0.006

0.008

0.01

a

aa

a

a

b

bb

bb

e

ee

e

em

m

m

mm

p

p

p

p

p

xx

x xx

PC 1

PC

2

Feature: Initial slope•Provides coarse distinction between “fast” and “slow responses, and some additional clustering.

•Potentially useful as one element of a hierarchical classifier.


Homogeneous Processing

Averaging over sensors reduces sensor noise Averaging over time reduces ambient noise Example: Effect of averaging over time



Effect of Averaging over Sensors• 24 element array of TGS822 Tin-Oxide Sensors• All sensors operate at same temperature in any

given experiment• Temperature is varied from experiment to

experiment



Circuits for Averaging over Sensors: Voltage Mode

-

+Vin_n

Vout_n

Vbias

-

+Vin_n

Vout_n

Vmean

voltage averaging outlier removal



Circuits for Averaging over Sensors: Current Mode

Vin_n

Vmean

current averaging outlier removal

Calculation of Outlier Current

(Adjustable)

Vout_n


Heterogeneous Processing

Heterogeneous Processing• Extract features from sensor arrays consisting

of:– different types of sensors

– different operating conditions (temperature) • Example: 15 sensor array

– 3 types of sensors

– 5 operating temperatures

– Extracted feature: median value of array

– Feature presentation: binary with respect to median



Heterogeneous Processing• Circuits for extracting median from 15

sensor array

Voutn

Vbiasn

Vbiasp

Ibiasn

Ibiasp

Vcom2

M3

M2

M1

M4

Imedian

Iin

Vn Min



Heterogeneous Processing• Experimental Results for median

thresholding of array

Acetone Ethanol Methanol


Summary

Completed work• analytically established features appropriate for extraction

from arrays of metal-oxide chemical sensors• proof-of-concept for homogeneous processing of such arrays• CMOS circuits designed and fabricated for first stage of

homogenous processing• CMOS circuits designed for first stage of feature extraction

Next Step• additional homogeneous processing and feature extraction

circuits • repeat experiments for discrete, thin film, smaller sensors to

establish benefits of miniaturizing• long-term: extend to integrated, metal-oxide sensor arrays

university of kentucky, auburn universityslide 1 system level design of chemical sensing...

Documents

sensors b b type

modules b b build

deg c b b

feature analysis b b

blocklevel solution

modules b b test system

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