.png){kind=icon}
返回
Deep learning for non-parameterized MEMS structural design
.png){kind=icon}
OA
AI
DOI：10.1038/s41378-022-00432-9.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 x 10(3) times and 2.6 x 10(4) times faster, respectively, than conventional numerical simulation packages, with good accuracies of 98.8 +/- 1.6% and 96.8 +/- 3.1%, respectively. When simultaneously predicting the frequency and the quality factor, up to similar to 96.0% of the total computation time can be saved during the design process. The proposed technique can rapidly screen over thousands of design candidates and promotes experience-free and data-driven MEMS structural designs.
Keyword:
RESONATORS
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
M
IF:
9.9
论文数:
1.3K
被引数:
6.7K
机构
引用论文
Comparing different supervised machine learning algorithms for disease prediction比较用于疾病预测的不同监督机器学习算法OAAI
OAAI分享收藏Finding the optical properties of plasmonic structures by image processing using a combination of convolutional neural networks and recurrent neural networks使用卷积神经网络和递归神经网络的组合通过图像处理找到等离子体结构的光学特性OAAI
OAAI分享收藏AlN/3C-SiC Composite Plate Enabling High-Frequency and High-Q Micromechanical Resonators.png)
ADVANCED MATERIALS
ADVANCED MATERIALSIF26.8
PREAI
分享收藏
查看更多内容