Model-based Processing of Microcantilever Sensor Arrays
We have developed a model-based processor (MBP) for a microcantilever-array sensor to detect target species in solution. We perform a proof-of-concept experiment, fit model parameters to the measured data and use them to develop a Gauss-Markov simulation. We then investigate two cases of interest, averaged deflection data and multi-channel data. For this evaluation we extract model parameters via a model-based estimation, perform a Gauss-Markov simulation, design the optimal MBP and apply it to measured experimental data. The performance of the MBP in the multi-channel case is evaluated by comparison to a ''smoother'' (averager) typically used for microcantilever signal analysis. It is shown that the MBP not only provides a significant gain ({approx} 80dB) in signal-to-noise ratio (SNR), but also consistently outperforms the smoother by 40-60 dB. Finally, we apply the processor to the smoothed experimental data and demonstrate its capability for chemical detection. The MBP performs quite well, apart from a correctable systematic bias error.
- Research Organization:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 898006
- Report Number(s):
- UCRL-JRNL-212326; TRN: US200706%%189
- Journal Information:
- The Institute of Electrical and Electronics Engineers Journal of Microelectromechanical Systems, vol. 15, no. 5, October 1, 2006, pp. 1379-1391, Journal Name: The Institute of Electrical and Electronics Engineers Journal of Microelectromechanical Systems, vol. 15, no. 5, October 1, 2006, pp. 1379-1391
- Country of Publication:
- United States
- Language:
- English
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