An on-chip MEMS testing device for uncertainty quantification at the microscale

Current trend towards miniaturization of micro electro-mechanical systems (MEMS) is leading to device featuring mechanical parts whose size are comparable to the morphology-induced length-scale of the of the polysilicon films they are made of. The device response to the external stimuli thus turns out to be affected more by scattering. We recently proposed an on-chip testing device, specifically designed to enhance such a scattering that can be caught by allowing for the morphology-affected mechanical properties of the silicon films and for etch defects induced by the micro-fabrication process. In this work, we discuss a stochastic framework allowing for the local fluctuations of the stiffness and of the etch-affected geometry of the silicon film. The method rests on a coarse-grained, semi-analytical solution for the mechanical response of the movable structure of the device, and it is shown to catch efficiently the measured scattering in the C-V plots collected during laboratory tests. We also discuss how deep learning can be adopted to further generalize the capability of the proposed frame in identifying on-line specific features of the devices, and reducing the time required for their calibration