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Design of freeform geometries in a MEMS accelerometer with a mechanical motion preamplifier based on a genetic algorithm

This paper describes a novel, semiautomated design methodology based on a genetic algorithm (GA) using freeform geometries for microelectromechanical systems (MEMS) devices. The proposed method can design MEMS devices comprising freeform geometries and optimize such MEMS devices to provide high sens...

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Autores principales: Wang, Chen, Song, Xiaoxiao, Fang, Weidong, Chen, Fang, Zeimpekis, Ioannis, Wang, Yuan, Quan, Aojie, Bai, Jian, Liu, Huafeng, Schropfer, Gerold, Welham, Chris, Kraft, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8433402/
https://www.ncbi.nlm.nih.gov/pubmed/34567713
http://dx.doi.org/10.1038/s41378-020-00214-1
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author Wang, Chen
Song, Xiaoxiao
Fang, Weidong
Chen, Fang
Zeimpekis, Ioannis
Wang, Yuan
Quan, Aojie
Bai, Jian
Liu, Huafeng
Schropfer, Gerold
Welham, Chris
Kraft, Michael
author_facet Wang, Chen
Song, Xiaoxiao
Fang, Weidong
Chen, Fang
Zeimpekis, Ioannis
Wang, Yuan
Quan, Aojie
Bai, Jian
Liu, Huafeng
Schropfer, Gerold
Welham, Chris
Kraft, Michael
author_sort Wang, Chen
collection PubMed
description This paper describes a novel, semiautomated design methodology based on a genetic algorithm (GA) using freeform geometries for microelectromechanical systems (MEMS) devices. The proposed method can design MEMS devices comprising freeform geometries and optimize such MEMS devices to provide high sensitivity, large bandwidth, and large fabrication tolerances. The proposed method does not require much computation time or memory. The use of freeform geometries allows more degrees of freedom in the design process, improving the diversity and performance of MEMS devices. A MEMS accelerometer comprising a mechanical motion amplifier is presented to demonstrate the effectiveness of the design approach. Experimental results show an improvement in the product of sensitivity and bandwidth by 100% and a sensitivity improvement by 141% compared to the case of a device designed with conventional orthogonal shapes. Furthermore, excellent immunities to fabrication tolerance and parameter mismatch are achieved.
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spelling pubmed-84334022021-09-24 Design of freeform geometries in a MEMS accelerometer with a mechanical motion preamplifier based on a genetic algorithm Wang, Chen Song, Xiaoxiao Fang, Weidong Chen, Fang Zeimpekis, Ioannis Wang, Yuan Quan, Aojie Bai, Jian Liu, Huafeng Schropfer, Gerold Welham, Chris Kraft, Michael Microsyst Nanoeng Article This paper describes a novel, semiautomated design methodology based on a genetic algorithm (GA) using freeform geometries for microelectromechanical systems (MEMS) devices. The proposed method can design MEMS devices comprising freeform geometries and optimize such MEMS devices to provide high sensitivity, large bandwidth, and large fabrication tolerances. The proposed method does not require much computation time or memory. The use of freeform geometries allows more degrees of freedom in the design process, improving the diversity and performance of MEMS devices. A MEMS accelerometer comprising a mechanical motion amplifier is presented to demonstrate the effectiveness of the design approach. Experimental results show an improvement in the product of sensitivity and bandwidth by 100% and a sensitivity improvement by 141% compared to the case of a device designed with conventional orthogonal shapes. Furthermore, excellent immunities to fabrication tolerance and parameter mismatch are achieved. Nature Publishing Group UK 2020-11-30 /pmc/articles/PMC8433402/ /pubmed/34567713 http://dx.doi.org/10.1038/s41378-020-00214-1 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Wang, Chen
Song, Xiaoxiao
Fang, Weidong
Chen, Fang
Zeimpekis, Ioannis
Wang, Yuan
Quan, Aojie
Bai, Jian
Liu, Huafeng
Schropfer, Gerold
Welham, Chris
Kraft, Michael
Design of freeform geometries in a MEMS accelerometer with a mechanical motion preamplifier based on a genetic algorithm
title Design of freeform geometries in a MEMS accelerometer with a mechanical motion preamplifier based on a genetic algorithm
title_full Design of freeform geometries in a MEMS accelerometer with a mechanical motion preamplifier based on a genetic algorithm
title_fullStr Design of freeform geometries in a MEMS accelerometer with a mechanical motion preamplifier based on a genetic algorithm
title_full_unstemmed Design of freeform geometries in a MEMS accelerometer with a mechanical motion preamplifier based on a genetic algorithm
title_short Design of freeform geometries in a MEMS accelerometer with a mechanical motion preamplifier based on a genetic algorithm
title_sort design of freeform geometries in a mems accelerometer with a mechanical motion preamplifier based on a genetic algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8433402/
https://www.ncbi.nlm.nih.gov/pubmed/34567713
http://dx.doi.org/10.1038/s41378-020-00214-1
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