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The High-Efficiency Design Method for Capacitive MEMS Accelerometer

In this research, a high-efficiency design method of the capacitive MEMS accelerometer is proposed. As the MEMS accelerometer has high precision and a compact structure, much research has been carried out, which mainly focused on the structural design and materials selection. To overcome the inconve...

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Detalles Bibliográficos
Autores principales: Liu, Wen, Zhao, Tianlong, He, Zhiyuan, Ye, Jingze, Gong, Shaotong, Wang, Xianglong, Yang, Yintang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10609016/
https://www.ncbi.nlm.nih.gov/pubmed/37893328
http://dx.doi.org/10.3390/mi14101891
Descripción
Sumario:In this research, a high-efficiency design method of the capacitive MEMS accelerometer is proposed. As the MEMS accelerometer has high precision and a compact structure, much research has been carried out, which mainly focused on the structural design and materials selection. To overcome the inconvenience and inaccuracy of the traditional design method, an orthogonal design and the particle swarm optimization (PSO) algorithm are introduced to improve the design efficiency. The whole process includes a finite element method (FEM) simulation, high-efficiency design, and verification. Through the theoretical analysis, the working mechanism of capacitive MEMS accelerometer is clear. Based on the comparison among the sweep calculation results of these parameters in the FEM software, four representative structural parameters are selected for further study, and they are l(e), n(f), l(f) and w(PM), respectively. l(e) and l(f) are the length of the sensing electrode and fixed electrode on the right. n(f) is the number of electrode pairs, and w(PM) is the width of the mass block. Then, in order to reduce computation, an orthogonal design is adopted and finally, 81 experimental groups are produced. Sensitivity S(V) and mass M(a) are defined as evaluation parameters, and structural parameters of experimental groups are imported into the FEM software to obtain the corresponding calculation results. These simulation data are imported into neural networks with the PSO algorithm. For a comprehensively accurate examination, three cases are used to verify our design method, and every case endows the performance parameters with different weights and expected values. The corresponding structural parameters of each case are given out after 24 iterations. Finally, the maximum calculation errors of S(V) and M(a) are 1.2941% and 0.1335%, respectively, proving the feasibility of the high-efficiency design method.