Download SPR (Surface Plasmon Resonance) Chemical Sensing Microsystems

Distributed Microsystems Laboratory
ENose Toolbox:
Application to Array Optimization including Electronic Measurement
and Noise Effects for Composite Polymer Chemiresistors
Denise Wilson, Associate Professor
Lisa Hansen, Graduate Research Assistant
Department of Electrical Engineering
University of Washington
ENose Toolbox:
Outline
• Why a Toolbox?
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Introduction
Motivation
Barriers
Approach
• Example
– Design Problem
– Design Solution
– Results
• Conclusion
• Current Status
ENose Toolbox:
Motivation
• 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 :
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Number of sensors
Redundancy of sensors
Signal to noise behavior
Robustness to interferents
• For optimizing and customizing designs to appropriately targeted applications
ENose Toolbox:
Barriers
• 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:
Approach -- Based on User/Designer
Stage 1:
Identify the candidates
ap prop riate for app lication
Is sens or ou tput ready for
measurement electronics?
Temperature
Other Analytes
Stage 2:
Identify ad ditiona l transduction mechanisms
no
yes
Stage 3:
Evaluate impact of interfer ents of primary concern
Humidity
no
Is selecitivity adequate?
Even in pres ence of no ise?
yes
Stage 4:
Evaluate sensor(s) r esponse
to analyte mixtures
yes
Stage 5:
Evaluate temporal r esponse
of sensor/sensor array
yes
Additional Stages:
Evaluate sampling ; Mod el
equivalent impedance, etc.
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.
ENose Toolbox:
Example
• 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 -- Results
• 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
Array #1
Array #2
Array #3
Array #4
• 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:
Conclusions
• 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:
Current Status
• 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
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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