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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