Machine learning-driven discovery of high-performance MEMS disk resonator gyroscope structural topologies.

ABSTRACT: The design of the microelectromechanical system (MEMS) disc resonator gyroscope (DRG) structural topology is crucial for its physical properties and performance. However, creating novel high-performance MEMS DRGs has long been viewed as a formidable challenge owing to their enormous design space, the complexity of microscale physical effects, and time-consuming finite element analysis (FEA). Here, we introduce a new machine learning-driven approach to discover high-performance DRG topologies. We represent the DRG topology as pixelated binary matrices and formulate the design task as a path-planning problem. This path-planning problem is solved via deep reinforcement learning (DRL). In addition, we develop a convolutional neural network-based surrogate model to replace the expensive FEA to

SUBMITTER: Chen C 

PROVIDER: S-EPMC11522492 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature