The MEMS Design Process

Lamers, Tina L.; Pruitt, Beth L.

doi:10.1007/978-0-387-47318-5_1

Cited by 1 publication

(1 citation statement)

References 42 publications

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“…Li et al [10] designed a novel ZnO film piezoelectric MEMS accelerometer with a composite cantilever beam, significantly improving the sensitivity of the device to 4.5 times that of the regular one. The capacitive MEMS accelerometer is superior with its high accuracy, strong environmental adaptability and excellent dynamic response [11], thus its relative design research, such as material, process and structure, has attracted much attention [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

The High-Efficiency Design Method for Capacitive MEMS Accelerometer

Liu,

Zhao,

et al. 2023

Micromachines

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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 le, nf, lf and wPM, respectively. le and lf are the length of the sensing electrode and fixed electrode on the right. nf is the number of electrode pairs, and wPM 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 SV and mass Ma 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 SV and Ma are 1.2941% and 0.1335%, respectively, proving the feasibility of the high-efficiency design method.

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