Model-Based Processing of Microcantilever Sensor Arrays

In this paper, 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 multichannel 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 multichannel 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
$(sim 80~ dB)$
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.1625