Rational Design of Field-Effect Sensors Using Partial Differential Equations, Bayesian Inversion, and Artificial Neural Networks

authored by

Amirreza Khodadadian, Maryam Parvizi, Mohammad Teshehlab, Clemens Heitzinger

Abstract

Silicon nanowire field-effect transistors are promising devices used to detect minute amounts of different biological species. We introduce the theoretical and computational aspects of forward and backward modeling of biosensitive sensors. Firstly, we introduce a forward system of partial differential equations to model the electrical behavior, and secondly, a backward Bayesian Markov-chain Monte-Carlo method is used to identify the unknown parameters such as the concentration of target molecules. Furthermore, we introduce a machine learning algorithm according to multilayer feed-forward neural networks. The trained model makes it possible to predict the sensor behavior based on the given parameters.

Organisation(s)

Institute of Applied Mathematics
PhoenixD: Photonics, Optics, and Engineering - Innovation Across Disciplines

External Organisation(s)

K.N. Toosi University of Technology
TU Wien (TUW)

Type

Article

Journal

Sensors

Volume

22

No. of pages

18

ISSN

1424-8220

Publication date

01.07.2022

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Analytical Chemistry, Information Systems, Biochemistry, Atomic and Molecular Physics, and Optics, Instrumentation, Electrical and Electronic Engineering

Electronic version(s)

https://doi.org/10.3390/s22134785 (Access: Open)