Distributed Microsystems Laboratory

ENose Toolbox: Application to Array Optimization including Electronic Measurement

and Noise Effects for Composite Polymer Chemiresistors

Denise Wilson, Associate ProfessorLisa Hansen, Graduate Research Assistant

Department of Electrical EngineeringUniversity of Washington

• Why a Toolbox?– Introduction– Motivation– Barriers– Approach

• Example– Design Problem– Design Solution– Results

• Conclusion• Current Status

ENose Toolbox:Outline

• To combine– Background theory– Empirical Models

• Into a general purpose simulation tool for:– Chemical– Biological– Mixed Mode

• Sensing Systems that can be optimized in terms of :– Number of sensors– Redundancy of sensors– Signal to noise behavior– Robustness to interferents

• For optimizing and customizing designs to appropriately targeted applications

ENose Toolbox:Motivation

• Many chemical/biological sensor technologies do not translate to– Through– Across

• Variable models that can be simulated and combined using superposition

• Sensor theory is often not completely understood• Systems cross disciplines (chemistry, biology, electrical engineering, photonics,

etc...) causing language and research barriers that limit simulation tools

• Sensor response is often dependent on sensor history

• Interferents are numerous and problematic

Is a hybrid (empirical/theory), evolving simulation tool better than none at all?

ENose Toolbox:Barriers

ENose Toolbox:Approach -- Based on User/Designer

Stage 1:Identify the candidatesappropriate for application

Is sensor output ready formeasurement electronics?

Stage 2:Identify additional transduc-tion mechanisms

Stage 3:Evaluate impact of interfer -ents of primary concern

yes

no

Humidity

Temperature

Other Analytes

Stage 4:Evaluate sensor(s) r esponseto analyte mixtures

Stage 5:Evaluate temporal r esponseof sensor/sensor array

Is selecitivity adequate?Even in presence of noise?

no

yes

Additional Stages:Evaluate sampling; Modelequivalent impedance, etc.

yes

yes

Many factors (that are not often separable) influence chemical and biological sensing systems design. A simulation platform for these systems must be dynamic and robust enough to incorporate additional theory and empirical

understanding as it grows in scope and sophistication.

• Sensor Technology: composite polymer chemiresistors– Insulating, chemically sensitive polymer– Conductive medium

• Transduction Mechanism: – Polymer swelling is measured as an increase in resistance– Resistance increases linearly with concentration for small concentrations

• Vulnerable to humidity, drift, other interferents• Swelling induces a small change in resistance on top of a large baseline

– Measurement circuits must preserve resolution and detection limit when converting small changes in resistance to final output

• Design Goal– Optimize resolving power for discrimination of two analytes

(methanol and benzene) – Using a heterogeneous array of composite polymer films

ENose Toolbox:Example

• Evaluate Design Optimization (Array Selection)– For different measurement circuits– In the presence of thermal noise

• Why?– The impact of the dynamic range of the sensor (very small changes in resistance on

top of a large baseline resistance) is often rendered “invisible” by conventional means to address this design goal.

• Additional concerns (advanced stages of simulation should address):– Effect of humidity/drift/aging/poisoning on array behavior– Introduce compensating sensors/design measures for these effects

• Humidity sensor• Redundant sensors to reduce variation• Reference sensors to compensate for aging and quantify drift

ENose Toolbox:Example

• Two measurement circuits • Same sensor inputs

• Wheatstone bridge (top): – differential measurement – eliminates “baseline”

• Voltage divider (bottom): – single-ended measurement – preserves “baseline”

• Separability – Both resolving power

(between analytes)– And resolution (between

concentrations) is better for – The Wheatstone Bridge

ENose Toolbox:Example -- Results

• Four sensor arrays • Same stimuli:

– methanol and benzene)• Wheatstone bridge output• Without Noise (top):

– Sensor Array #2 has the best resolving power

• With Noise (bottom): – Sensor Array #3 has the

best resolving power• Impact of Noise

– Variations in Dynamic Range remain invisible

– Yet impact noise levels– In “real” array/system

design

ENose Toolbox:Example -- Results

Array #1 Array #2 Array #3 Array #4

• Because of:– Sensor response = small change on top of a large baseline (resistance)

• The selection of measurement circuit:– differential vs. single-ended measurement– Significantly impacts discrimination capability

• The presence of thermal noise:– Inherent in the chosen transduction mechanism (resistance)– Alters the selection of optimal array for maximum resolving power

• The Enose Toolbox enables:– Access to these “complicating” parameters– During the design(simulation) rather than post-fabrication characterization

of sensor array system designs– When design changes are far less costly

ENose Toolbox:Conclusions

• Various functions, analytes, materials, and technologies accessed in Matlab• Sensor Technologies Currently Available

– Composite polymer chemiresistors– Tin-oxide chemiresistors– Surface Plasmon Resonance

• Additional features– Noise (observed in actual sensor responses)

• Coming up– Additional sensor technologies (ChemFETs, ISFETs, LAPS, and more)– Additional functions: mixtures, equivalent impedance– Additional features: noise, drift– Additional response characteristics: transient

Where is it?

www.ee.washington.edu/research/enose

ENose Toolbox:Current Status