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Micro and Nanosystems

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ISSN (Print): 1876-4029
ISSN (Online): 1876-4037

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Perspective

Hybrid Model-Based and Data-Driven Solution for Uncertainty Quantification at the Microscale

Author(s): Jose Pablo Quesada-Molina and Stefano Mariani*

Volume 14, Issue 4, 2022

Published on: 17 May, 2022

Page: [281 - 286] Pages: 6

DOI: 10.2174/1876402914666220328123601

Abstract

Background: Due to their size, microelectromechanical systems (MEMS) display performance indices affected by uncertainties linked to the mechanical properties and to the geometry of the films constituting their movable parts.

Objective: In this perspective, a recently proposed multiscale and hybrid solution for uncertainty quantification is discussed.

Methods: The proposed method is based on the (deep) learning of the morphology-affected elasticity of the polycrystalline films and of the microfabrication-induced defective geometry of the devices. The results at the material and at the device levels are linked through a reduced-order representation of the response of the entire device to the external stimuli, foreseen to finally feed a Monte Carlo uncertainty quantification engine.

Results: Preliminary results relevant to a single-axis resonant Lorentz force micro-magnetometer have shown a noteworthy capability of the proposed multiscale deep learning method to account for the mentioned uncertainty sources at the microscale.

Conclusion: A promising two-scale deep learning approach has been proposed for polysilicon MEMS sensors to account for both materials- and geometry-governed uncertainties and to properly describe the scale-dependent response of MEMS devices.

Keywords: Machine learning, artificial neural networks (ANNs), polysilicon mems, uncertainty quantification, Monte Carlo simulations, multiscale analysis.

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Graphical Abstract

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