| Autor: |
Chen Chen, Jinqiu Zhou, Hongyi Wang, Youyou Fan, Xinyue Song, Jianbing Xie, Thomas Bäck, Hao Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Microsystems & Nanoengineering, Vol 10, Iss 1, Pp 1-13 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2055-7434 |
| DOI: |
10.1038/s41378-024-00792-4 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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