CNN-Based Surrogate Models of the Electrostatic Field for a MEMS Motor: A Bi-Objective Optimal Shape Design

Abstract

The use of a convolutional neural network to develop a surrogate model of the electric field in MEMS devices is proposed. An electrostatic micromotor is considered as the case study. In particular, different CNNs are trained for the prediction of the torque profile and the maximum torque value at a no-load condition and the radial force which could arise in case of the radial displacement of the rotor during motion. The proposed deep learning approach is able to predict the abovementioned quantities with a low error and, in particular, it allows for a decrease in the computational cost, especially in case of optimization problems based on FE models.