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

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 differenti...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-06, Vol.22 (13), p.4785
Main Authors: Khodadadian, Amirreza, Parvizi, Maryam, Teshnehlab, Mohammad, Heitzinger, Clemens
Format: Article
Language:English
Subjects:
Algorithms
Bayes Theorem
Bayesian analysis
Bayesian inversion
Biosensors
charge transport
Electric currents
Field effect transistors
field-effect sensors
inverse modeling
Markov chains
Monte Carlo Method
Multilayers
Nanowires
Neural networks
Neural Networks, Computer
Parameter estimation
Partial differential equations
Semiconductor devices
Sensors
Simulation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22134785