DESIGNING WEAKLY COUPLED MEMS RESONATORS WITH MACHINE LEARNING-BASED METHOD
2022 IEEE 35TH INTERNATIONAL CONFERENCE ON MICRO ELECTRO MECHANICAL SYSTEMS CONFERENCE (MEMS)(2022)
摘要
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.
更多查看译文
关键词
Weakly Coupled Resonators, Machine Learning, Design Space Exploration
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要