Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-14976
Authors: Heringhaus, Monika E.
Zhang, Yi
Zimmermann, André
Mikelsons, Lars
Title: Towards reliable parameter extraction in MEMS final module testing using Bayesian inference
Issue Date: 2022
metadata.ubs.publikation.typ: Zeitschriftenartikel
metadata.ubs.publikation.seiten: 22
metadata.ubs.publikation.source: Sensors 22 (2022), No. 5408
URI: http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-149953
http://elib.uni-stuttgart.de/handle/11682/14995
http://dx.doi.org/10.18419/opus-14976
ISSN: 1424-8220
Abstract: In micro-electro-mechanical systems (MEMS) testing high overall precision and reliability are essential. Due to the additional requirement of runtime efficiency, machine learning methods have been investigated in recent years. However, these methods are often associated with inherent challenges concerning uncertainty quantification and guarantees of reliability. The goal of this paper is therefore to present a new machine learning approach in MEMS testing based on Bayesian inference to determine whether the estimation is trustworthy. The overall predictive performance as well as the uncertainty quantification are evaluated with four methods: Bayesian neural network, mixture density network, probabilistic Bayesian neural network and BayesFlow. They are investigated under the variation in training set size, different additive noise levels, and an out-of-distribution condition, namely the variation in the damping factor of the MEMS device. Furthermore, epistemic and aleatoric uncertainties are evaluated and discussed to encourage thorough inspection of models before deployment striving for reliable and efficient parameter estimation during final module testing of MEMS devices. BayesFlow consistently outperformed the other methods in terms of the predictive performance. As the probabilistic Bayesian neural network enables the distinction between epistemic and aleatoric uncertainty, their share of the total uncertainty has been intensively studied.
Appears in Collections:07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik

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