  Nanowire field-effect sensors have recently been developed for label-free
detection of biomolecules. In this work, we introduce a computational technique
based on Bayesian estimation to determine the physical parameters of the sensor
and, more importantly, the properties of the analyte molecules. To that end, we
first propose a PDE based model to simulate the device charge transport and
electrochemical behavior. Then, the adaptive Metropolis algorithm with delayed
rejection (DRAM) is applied to estimate the posterior distribution of unknown
parameters, namely molecule charge density, molecule density, doping
concentration, and electron and hole mobilities. We determine the device and
molecules properties simultaneously, and we also calculate the molecule density
as the only parameter after having determined the device parameters. This
approach makes it possible not only to determine unknown parameters, but it
also shows how well each parameter can be determined by yielding the
probability density function (pdf).
