Distributed Microsystems Laboratory

1. 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

2. ENose Toolbox: Outline • Why a Toolbox? • Introduction • Motivation • Barriers • Approach • Example • Design Problem • Design Solution • Results • Conclusion • Current Status

3. 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 : • Number of sensors • Redundancy of sensors • Signal to noise behavior • Robustness to interferents • For optimizing and customizing designs to appropriately targeted applications

4. 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?

5. ENose Toolbox: Approach -- Based on User/Designer 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.

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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 • 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