Designing Weakly Coupled MEMS Resonators with Machine Learning-Based Method


We demonstrate a design scheme for weakly coupled resonators (WCRs) by integrating the supervised learning (SL) with the genetic algorithm (GA). In this work, three distinctive achievements have been accomplished: 1) the precise prediction of coupling characteristics of WCRs with an accuracy of 98.7% via SL; 2) the stepwise evolutionary optimization of WCR geometries while maintaining their geometric connectivity via GA; and 3) the highly efficient generation of WCR designs with a mean coupling factor down to 0.0056, which outperforms 98% of random designs. The coupling behavior analysis and prediction are validated with experimental data of coupled microcantilevers from a published work. As such, this newly proposed scheme could shed light upon the structural optimization methods for high-performance MEMS devices with high degree of design freedom.

Keywords: Weakly Coupled Resonators, Machine Learning, Design Space Exploration

Publication date: 
December 27, 2021
Publication type: 
Conference Paper (Proceedings)
F. Sui, W. Yue, R. Guo, K. Behrouzi and L. Lin, "Designing Weakly Coupled Mems Resonators with Machine Learning-Based Method," 2022 IEEE 35th International Conference on Micro Electro Mechanical Systems Conference (MEMS), 2022, pp. 454-457, doi: 10.1109/MEMS51670.2022.9699450.

*Only registered BSAC Industrial Members may view project materials & publications. Click here to request member-only access.