模式局域加速度计微杠杆形状优化

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement Pub Date : 2025-10-04 DOI:10.1016/j.measurement.2025.119178

Huizi Li , Yongcun Hao , Han Li , Honglong Chang

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引用次数: 0

摘要

微杠杆是提高MEMS传感器灵敏度的有效方法。然而，传统的微杠杆优化方法在提高放大系数方面存在局限性。本文首次将基于BOBYQA算法的形状优化方法应用于微杠杆优化中，以提高放大系数。与传统的矩形微杠杆相比，优化后的蜻蜓形微杠杆在相同尺寸下具有更优越的性能。为验证所提出的优化方法，制作了集成优化后的蜻蜓形微杠杆的模式定域加速度计。实验结果表明，经过形状优化后，加速度计的灵敏度达到630/g，比优化前提高264%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Shape optimization of microlever for mode-localized accelerometer

A microlever is an effective approach to increase the sensitivity of MEMS sensors. However, traditional optimization methods for microlevers have limitations in improving the amplification factor. In this paper, a shape optimization method based on the BOBYQA algorithm is utilized in microlever optimization for the first time to improve the amplification factor. Compared with the traditional rectangular microlever, the optimized dragonfly-shaped microlever achieves superior performance under the same dimensions. Mode-localized accelerometers integrating the optimized dragonfly-shaped microlever are fabricated to verify the proposed optimization method. The experimental results show that following shape optimization, the accelerometer achieves a sensitivity of 630/g, which is 264 % higher than before optimization.

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来源期刊

Measurement 工程技术-工程：综合

CiteScore

10.20

自引率

12.50%

发文量

1589

审稿时长

12.1 months

期刊介绍： Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.