Customizing MEMS Designs via Conditional Generative Adversarial Networks


We present a novel systematic MEMS structure design approach based on a “deep conditional generative model”. Utilizing the conditional generative adversarial network (CGAN) on a case study of circular-shaped MEMS resonators, three major advancements have been demonstrated: 1) a high-throughput vectorized MEMS design generation scheme that satisfies the geometric constraints; 2) MEMS structural customization toward tunable, desired physical properties with excellent generation accuracy; and 3) experience-free design space explorations to achieve extreme physical properties, such as low anchor loss of micro resonators. Excellent agreements with experimental data, numerical simulations, and a previously reported machine learning-based analyzer are achieved for validation of our methodology. As such, the proposed scheme could open up a new class of data-driven, intelligent design systems for a wide range of MEMS applications.

Keywords: MEMS Design, Conditional Generative Adversarial Networks, Data-driven Design, Machine Learning

Publication date: 
December 27, 2021
Publication type: 
Conference Paper (Proceedings)
F. Sui, R. Guo, W. Yue, K. Behrouzi and L. Lin, "Customizing Mems Designs via Conditional Generative Adversarial Networks," 2022 IEEE 35th International Conference on Micro Electro Mechanical Systems Conference (MEMS), 2022, pp. 450-453, doi: 10.1109/MEMS51670.2022.9699476.

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