ArticleMicrosystems & nanoengineering2022
Deep learning for non-parameterized MEMS structural design.
Article in Microsystems & nanoengineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- 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
- Behavior of Electrothermal Actuator Analyzed by Polynomial Point Interpolation Collocation Method.Micromachines · 2025Article
- Using Adaptive Surrogate Models to Accelerate Multi-Objective Design Optimization of MEMS.Micromachines · 2025Article
- Application of Generalized Finite Difference Method for Nonlinear Analysis of the Electrothermal Micro-Actuator.Micromachines · 2025Article
- Neural Network Methods in the Development of MEMS Sensors.Micromachines · 2024Review
- Machine learning-driven discovery of high-performance MEMS disk resonator gyroscope structural topologies.Microsystems & nanoengineering · 2024Article
- Structural Engineering in Piezoresistive Micropressure Sensors: A Focused Review.Micromachines · 2023Review
- A novel evolutionary method for parameter-free MEMS structural design and its application in piezoresistive pressure sensors.Microsystems & nanoengineering · 2023Article
- Towards smart scanning probe lithography: a framework accelerating nano-fabrication process with in-situ characterization via machine learning.Microsystems & nanoengineering · 2023Article
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Authors and funding
8 authors.
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Abstract
The geometric designs of MEMS devices can profoundly impact their physical properties and eventual performances. However, it is challenging for researchers to rationally consider a large number of possible designs, as it would be very time- and resource-consuming to study all these cases using numerical simulation. In this paper, we report the use of deep learning techniques to accelerate the MEMS design cycle by quickly and accurately predicting the physical properties of numerous design candidates with vastly different geometric features. Design candidates are represented in a nonparameterized, topologically unconstrained form using pixelated black-and-white images. After sufficient training, a deep neural network can quickly calculate the physical properties of interest with good accuracy without using conventional numerical tools such as finite element analysis. As an example, we apply our deep learning approach in the prediction of the modal frequency and quality factor of disk-shaped microscale resonators. With reasonable training, our deep learning neural network becomes a high-speed, high-accuracy calculator: it can identify the flexural mode frequency and the quality factor 4.6 × 10
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