Machine learning-driven discovery of high-performance MEMS disk resonator gyroscope structural topologies
Microsystems & Nanoengineering, vol. 10, pp. 161
Abstract
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 provide reward signals for DRL training. Benefiting from the computational efficiency of neural networks, our approach achieves a significant acceleration ratio of 4.03 × 105 compared with FEA, reducing each DRL training run to only 426.5 s. Through 8000 training runs, we discovered 7120 novel structural topologies that achieve navigation-grade precision. Many of these surpass traditional designs in performance by several orders of magnitude, revealing innovative solutions previously unconceived by humans.
Authors 7
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Affiliation as printed
Xi'an Jiaotong University, Faculty of Electronic and Information Engineering, Xi'an, China
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Northwestern Polytechnical University · Beijing Microelectronics Technology Institute
Affiliation as printed
Beijing Microelectronics Technology Institute, Beijing, China
Northwestern Polytechnical University, School of Mechanical Engineering, Xi'an, China
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Leiden University · Xi'an Jiaotong University
Affiliation as printed
Leiden University, Leiden Institute of Advanced Computer Science, Leiden, Netherlands
Xi'an Jiaotong University, Faculty of Electronic and Information Engineering, Xi'an, China. wanghongyi@mail.xjtu.edu.cn
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Northwestern Polytechnical University · Xi'an Jiaotong University
Affiliation as printed
Xi'an Jiaotong University, Faculty of Electronic and Information Engineering, Xi'an, China
Northwestern Polytechnical University, School of Mechanical Engineering, Xi'an, China
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Leiden University · Northwestern Polytechnical University
Affiliation as printed
Northwestern Polytechnical University, School of Mechanical Engineering, Xi'an, China
Leiden University, Leiden Institute of Advanced Computer Science, Leiden, Netherlands
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Affiliation as printed
Leiden University, Leiden Institute of Advanced Computer Science, Leiden, Netherlands
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Leiden University · Xi'an Jiaotong University
Affiliation as printed
Leiden University, Leiden Institute of Advanced Computer Science, Leiden, Netherlands
Xi'an Jiaotong University, Faculty of Electronic and Information Engineering, Xi'an, China. wanghongyi@mail.xjtu.edu.cn
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