Machine learning algorithm for the structural design of MEMS resonators

Abstract

MEMS resonators have become core devices in many fields, their geometric designs can profoundly affect performance. However, the theoretical modeling of MEMS resonators with complex structures is becoming more and more difficult, and the time consumption of using numerical simulation methods to solve the accurate analytical solution of performance is also increasing. In this paper, we report a working mode identification and a machine learning algorithm, which dramatically shorten the MEMS design cycle by constructing datasets and predicting physical properties with high speed and high accuracy. As an example, we apply the algorithms to the performance prediction of flower-like disk resonator. The typical structural parameters of MEMS resonators are used as the input layer of the neural network, and performance generated by finite element analysis methods are used as the output layer. For 50,000 modal shapes with 5000 different structural parameters, the accuracy of the working mode identification algorithm proposed in this paper to identify elliptical modes is 100%. After sufficient training, the obtained neural network calculators can predict the resonant frequency, thermoelastic quality factor, mechanical sensitivity and mechanical thermal noise of the MEMS resonator. Compared with traditional numerical simulation methods, the identification of resonant frequency and thermoelastic quality factor is 11,341 times faster, the identification of mechanical sensitivity and mechanical thermal noise is 1813 times faster, and the prediction regression accuracy is all greater than 96%. This high-speed and high-accuracy performance prediction method can effectively improve the design efficiency of MEMS resonators with complex structures, providing a promising tool for enhancing MEMS resonator performance.