Khodadadian, A., Parvizi, M., Teshnehlab, M., & Heitzinger, C. (2022). Rational Design of Field-Effect Sensors Using Partial Differential Equations, Bayesian Inversion, and Artificial Neural Networks. Sensors, 22(13), Article 4785. https://doi.org/10.3390/s22134785
E101-03-2 - Forschungsgruppe Maschinelles Lernen und Unsicherheitsquantifizierung
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Zeitschrift:
Sensors
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ISSN:
1424-8220
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Datum (veröffentlicht):
1-Jul-2022
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Umfang:
18
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Verlag:
MDPI
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Peer Reviewed:
Ja
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Keywords:
field-effect sensors; Bayes Theorem; Monte Carlo Method; Bayesian inversion; biosensors; charge transport; Algorithms; inverse modeling; neural networks; Nanowires
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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.
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Projekttitel:
Partielle Differentialgleichungen für Nanotechnologie: Y660-N25 (Fonds zur Förderung der wissenschaftlichen Forschung (FWF))
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Projekt (extern):
Alexander von Humbold Foundation EXC 2122
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Projektnummer:
H−matrix approximability of the inverses for FEM, BEM and FEM–BEM coupling of the electromagnetic problems 390833453