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

verfasst von

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.

Organisationseinheit(en)

Institut für Angewandte Mathematik
PhoenixD: Simulation, Fabrikation und Anwendung optischer Systeme

Externe Organisation(en)

K.N. Toosi University of Technology
Technische Universität Wien (TUW)

Typ

Artikel

Journal

Sensors

Band

22

Anzahl der Seiten

18

ISSN

1424-8220

Publikationsdatum

01.07.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Analytische Chemie, Information systems, Biochemie, Atom- und Molekularphysik sowie Optik, Instrumentierung, Elektrotechnik und Elektronik

Elektronische Version(en)

https://doi.org/10.3390/s22134785 (Zugang: Offen)