Bayesian estimation of physical and geometrical parameters for nanocapacitor array biosensors

Abstract

Massively parallel nanosensor arrays fabricated with low-cost CMOS technology represent powerful platforms for biosensing in the Internet-of-Things (IoT) and Internet-of-Health (IoH) era. They can efficiently acquire "big data" sets of dependable calibrated measurements, representing a solid basis for statistical analysis and parameter estimation.

In this paper we propose Bayesian estimation methods to extract physical parameters and interpret the statistical variability in the measured outputs of a dense nanocapacitor array biosensor. Firstly, the physical and mathematical models are presented. Then, a simple 1D-symmetry structure is used as a validation test case where the estimated parameters are also known a-priori. Finally, we apply the methodology to the simultaneous extraction of multiple physical and geometrical parameters from measurements on a CMOS pixelated nanocapacitor biosensor platform.