Trial-and-Error Learning for MEMS Structural Design Enabled by Deep Reinforcement Learning

We present a systematic MEMS structural design approach via a "trial-and-error" learning process by using the deep reinforcement learning framework. This scheme incorporates the feedback from each "trial" to obtain sophisticated strategies for MEMS design optimizations. Disk-shaped MEMS resonators are selected as case studies and three remarkable advancements have been realized: 1) accurate overall performance predictions (97.9%) via supervised learning models; 2) efficient MEMS structural optimizations to guarantee targeted structural properties with an excellent generation accuracy of 97.7%; and 3) superior design explorations to achieve one order of magnitude performance enhancement than the training dataset. As such, the proposed scheme could facilitate a wide spectrum of MEMS applications with this data-driven inverse design methodology.