TY - JOUR

T1 - Bayesian inversion for nanowire field-effect sensors

AU - Khodadadian, Amirreza

AU - Stadlbauer, Benjamin

AU - Heitzinger, Clemens

N1 - Funding Information: The authors acknowledge support by the FWF (Austrian Science Fund) START project No. Y660 PDE Models for Nanotechnology. The authors also acknowledge the helpful comments by the anonymous reviewers. Funding Information: The authors acknowledge support by the FWF (Austrian Science Fund) START project No. Y660 PDE Models for Nanotechnology . The authors also acknowledge the helpful comments by the anonymous reviewers. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

PY - 2020/3/1

Y1 - 2020/3/1

N2 - 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 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).

AB - 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 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).

KW - Adaptive Metropolis–Hastings algorithm

KW - Markov chain Monte Carlo

KW - Silicon nanowire sensors

KW - Stochastic drift–diffusion–Poisson–Boltzmann system

UR - http://www.scopus.com/inward/record.url?scp=85075204088&partnerID=8YFLogxK

U2 - 10.1007/s10825-019-01417-0

DO - 10.1007/s10825-019-01417-0

M3 - Article

AN - SCOPUS:85075204088

VL - 19

SP - 147

EP - 159

JO - Journal of Computational Electronics

JF - Journal of Computational Electronics

SN - 1569-8025

IS - 1

ER -