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Deep learning for non-parameterized MEMS structural design

Engineering and Technology

Deep learning for non-parameterized MEMS structural design

R. Guo, F. Sui, et al.

This groundbreaking research by Ruiqi Guo and team harnesses deep learning to revolutionize MEMS design, predicting physical properties of designs with impressive speed and accuracy. Discover how their innovative approach outshines traditional methods, enabling rapid screening of design candidates for enhanced efficiency.

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Playback language: English
Abstract
This paper explores the use of deep learning to accelerate MEMS design by predicting physical properties of various designs. A non-parameterized approach, representing designs as pixelated images, allows for the exploration of a vast design space. The deep neural network accurately predicts modal frequency and quality factor of disk-shaped resonators, significantly faster than conventional finite element analysis (FEA), achieving speedups of 4.6 × 10³ and 2.6 × 10⁴ times, respectively, with high accuracy (98.8 ± 1.6% and 96.8 ± 3.1%). This approach enables rapid screening of design candidates and promotes data-driven MEMS design.
Publisher
Microsystems & Nanoengineering
Published On
Oct 26, 2022
Authors
Ruiqi Guo, Fanping Sui, Wei Yue, Zekai Wang, Sedat Pala, Kunying Li, Renxiao Xu, Liwei Lin
Tags
deep learning
MEMS design
physical properties
modal frequency
quality factor
finite element analysis
data-driven design

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