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

Design of freeform geometries in a MEMS accelerometer with a mechanical motion preamplifier based on a genetic algorithm
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 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.
2055-7434
Wang, Chen
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Song, Xiaoxiao
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Fang, Weidong
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Chen, Fang
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Zeimpekis, Ioannis
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Wang, Yuan
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Quan, Aojie
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Bai, Jian
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Liu, Huafeng
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Schropfer, Gerold
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Welham, Chris
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Kraft, Michael
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Wang, Chen
e58fe9ef-a100-4155-bc8e-0c1f1cc3e39a
Song, Xiaoxiao
c57bc8dd-cb97-4a71-ba39-8e2acbee1a4e
Fang, Weidong
08f564fd-178d-4b9a-8c30-d6cbd25e12e8
Chen, Fang
c1c61717-ce9b-43d8-9604-d09a72f8a510
Zeimpekis, Ioannis
a2c354ec-3891-497c-adac-89b3a5d96af0
Wang, Yuan
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Quan, Aojie
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Bai, Jian
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Liu, Huafeng
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Schropfer, Gerold
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Welham, Chris
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Kraft, Michael
c2ff2439-b909-4af3-824d-9d7c0d14dc3e

Wang, Chen, Song, Xiaoxiao, Fang, Weidong, Chen, Fang, Zeimpekis, Ioannis, Wang, Yuan, Quan, Aojie, Bai, Jian, Liu, Huafeng, Schropfer, Gerold, Welham, Chris and Kraft, Michael (2020) Design of freeform geometries in a MEMS accelerometer with a mechanical motion preamplifier based on a genetic algorithm. Microsystems & Nanoengineering, 6, [104]. (doi:10.1038/s41378-020-00214-1).

Record type: Article

Abstract

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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s41378-020-00214-1 - Version of Record
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More information

Accepted/In Press date: 17 September 2020
e-pub ahead of print date: 30 November 2020
Additional Information: Funding Information: This work was funded by the Science Challenge Project under Grant TZ2016006-0502-02 and the Natural Science Foundation of Hubei Province under Grant 2019CFB108.

Identifiers

Local EPrints ID: 452146
URI: http://eprints.soton.ac.uk/id/eprint/452146
ISSN: 2055-7434
PURE UUID: d4dd1980-d079-43d6-9e89-eeab30e1091d
ORCID for Ioannis Zeimpekis: ORCID iD orcid.org/0000-0002-7455-1599

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Date deposited: 25 Nov 2021 20:38
Last modified: 21 Sep 2024 01:46

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Contributors

Author: Chen Wang
Author: Xiaoxiao Song
Author: Weidong Fang
Author: Fang Chen
Author: Yuan Wang
Author: Aojie Quan
Author: Jian Bai
Author: Huafeng Liu
Author: Gerold Schropfer
Author: Chris Welham
Author: Michael Kraft

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