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

Abstract

Due to their size, Micro Electromechanical Systems (MEMS) display performance indices affected by uncertainties linked to the mechanical properties and to the geometry of the films constituting their movable parts. In this perspective, a recently proposed multiscale and hybrid solution for uncertainty quantification is discussed. 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, foressen to finally feed a Monte Carlo uncertainty quantification engine. 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. 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.