Computer-aided design of MEMS-FP based on reinforcement learning

Abstract The Micro-Electro-Mechanical Systems Fabry-Perot (MEMS-FP) filter is a pivotal photosensitive component within optical instrumentation, serving an integral role across various optical applications, including frequency selection spectroscopy and spectral analysis. The advent of MEMS-FP technology has significantly contributed to advancements in the optical field, underscoring the necessity for precise pre-fabrication theoretical predictions of device properties through finite element analysis (FEA). Nonetheless, the complexity inherent in three-dimensional modelling of MEMS-FP structures, coupled with the intricate nature of finite element simulations, presents substantial challenges that impede progress in MEMS-FP research. To navigate these challenges, this paper introduces a novel approach employing reinforcement learning (RL) for the automated design of MEMS-FP devices. This method encompasses the development of a Deep Q-Network (DQN) algorithm, the formulation of a tailored reward function, and an innovative strategy for updating the status of MEMS-FP devices. Various designs schemes of MEMS-FP devices can be acquired using MEMS-FP state search. The disparity between the MEMS-FP device design scheme and performance index can be assessed using a reward function. The DQN algorithm is crafted to update the network responsible for predicting Q-values. The agent trained by our method can rapidly calculate the MEMS-FP device design scheme that fulfils a specified performance index when provided with the performance index of the MEMS-FP device. By amalgamating reinforcement learning with MEMS-FP device research, this approach adeptly identifies optimal design configurations that meet specified performance criteria with enhanced precision and efficiency and promote the advancement of MEMS-FP devices.