Liu Yan, Ping Mingda, Han Jizhou, Cheng Xiang, Qin Hongbo, Wang Weidong
School of Mechano-Electronic Engineering, Xidian University, Xi'an 710071, China.
CityU-Xidian Joint Laboratory of Micro/Nano Manufacturing, Shenzhen 518057, China.
Micromachines (Basel). 2024 Nov 12;15(11):1368. doi: 10.3390/mi15111368.
As a kind of long-term favorable device, the microelectromechanical system (MEMS) sensor has become a powerful dominator in the detection applications of commercial and industrial areas. There have been a series of mature solutions to address the possible issues in device design, optimization, fabrication, and output processing. The recent involvement of neural networks (NNs) has provided a new paradigm for the development of MEMS sensors and greatly accelerated the research cycle of high-performance devices. In this paper, we present an overview of the progress, applications, and prospects of NN methods in the development of MEMS sensors. The superiority of leveraging NN methods in structural design, device fabrication, and output compensation/calibration is reviewed and discussed to illustrate how NNs have reformed the development of MEMS sensors. Relevant issues in the usage of NNs, such as available models, dataset construction, and parameter optimization, are presented. Many application scenarios have demonstrated that NN methods can enhance the speed of predicting device performance, rapidly generate device-on-demand solutions, and establish more accurate calibration and compensation models. Along with the improvement in research efficiency, there are also several critical challenges that need further exploration in this area.
作为一种长期有利的器件，微机电系统（MEMS）传感器已成为商业和工业领域检测应用中的强大主导者。在器件设计、优化、制造和输出处理方面，已经有一系列成熟的解决方案来解决可能出现的问题。神经网络（NNs）最近的参与为MEMS传感器的发展提供了一种新范式，并极大地加速了高性能器件的研究周期。在本文中，我们概述了NN方法在MEMS传感器发展中的进展、应用和前景。回顾并讨论了在结构设计、器件制造和输出补偿/校准中利用NN方法的优势，以说明NNs如何改变了MEMS传感器的发展。还介绍了NNs使用中的相关问题，如可用模型、数据集构建和参数优化。许多应用场景表明，NN方法可以提高预测器件性能的速度，快速生成按需定制的器件解决方案，并建立更准确的校准和补偿模型。随着研究效率的提高，该领域也存在一些关键挑战需要进一步探索。
