Bayesian estimation of physical and geometrical parameters for nanocapacitor array biosensors

Highlights: • First application of Bayesian inversion to a novel massively parallel nanoscale sensor platform and its validation. • Multi-frequency Bayesian inversion in conjunction with a PDE correctly identifies multi-dimensional parameters. • Multi-frequency Bayesian inversion allows to investigate the sizes of nanoelectrodes in biosensors with remarkable accuracy. • This approach makes it possible to estimate the accuracy and the limits of a new biosensor technology in silico. -- 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.