ArticleMicrosystems & nanoengineering2024
Machine learning-driven discovery of high-performance MEMS disk resonator gyroscope structural topologies.
Article in Microsystems & nanoengineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A fabrication-aware vectorized generative representation for topology optimization of path-defined MEMS microstructures.Microsystems & nanoengineering · 2026Article
- Design, Implementation and Calibration of Analog Gyro-Based Angular Rate Data Acquisition System for High-Spinning Rotation Rate Applications.Sensors (Basel, Switzerland) · 2026Article
- Analyzing the geometric dependence of thermoelastic Q-factor in micro hemispherical resonators via a data-augmented CNN-transformer model.Microsystems & nanoengineering · 2026Article
- Deep learning-driven performance prediction and design of high-DoF MEMS resonators.Microsystems & nanoengineering · 2026Article
- Terahertz MEMS actuators and applications.Microsystems & nanoengineering · 2026Review
- Structure Design Optimization of a Differential Capacitive MEMS Accelerometer Based on a Multi-Objective Elitist Genetic Algorithm.Micromachines · 2026Article
- Review of Research Advances in Gyroscopes' Structural Forms and Processing Technologies Viewed from Performance Indices.Sensors (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
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 × 10
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.