A MEMS electro-mechanical co-optimization platform featuring freeform geometry optimization based on a genetic algorithm.

IF 7.3 1区 工程技术 Q1 INSTRUMENTS & INSTRUMENTATION
Chen Wang, Xinyu Wu, Sina Sadeghpour, Milad Shojaeian, Linlin Wang, Bernardo Pereira Madeira, Yangyang Guan, Huafeng Liu, Yuan Wang, Pan Zhang, Pui-In Mak
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Abstract

This paper describes a novel, system-level design methodology based on a genetic algorithm (GA) using freeform geometries for microelectromechanical systems (MEMS) devices. The proposed method can concurrently design and co-optimize the electronic and mechanical parts of a MEMS device comprising freeform geometries to achieve a better system performance, i.e., a high sensitivity, a good system stability, and large fabrication tolerances. Also, the introduction of freeform geometries allows higher degrees of freedom in the design process, improving the diversity and potentially the performance of the MEMS devices. A MEMS accelerometer comprising a freeform mechanical motion preamplifier in a closed-loop control system is presented to demonstrate the effectiveness of the design approach. The optimization process shows the main figure-of-merit (FOM) is improved by 195%. In the mechanical component alone (open-loop system), the product of sensitivity and bandwidth has improved by 151%, with sensitivity increasing by 276%. For closed-loop performance, there is an improvement of 120% for the ratio of open and closed-loop displacements. The product of sensitivity and bandwidth is improved by 27% in the closed-loop system. Excellent immunities to fabrication errors and parameter mismatch are achieved. Experiments show that the displacement of the MEMS accelerometer in the closed-loop system decreased by 86% with 4.85 V feedback voltage compared with that in the open-loop system under a 1 g 100 Hz acceleration input. The static and dynamic nonlinearities in the closed-loop system are improved by 64% and 61%, respectively, compared with those in the open-loop system, in the ±1 g acceleration input range. Besides, the closed-loop system improves the cross-axis sensitivity by 18.43%, compared with that in the open-loop system. It is the first time a closed-loop system for a MEMS accelerometer comprising a mechanical motion preamplifier is successfully implemented experimentally.

基于遗传算法的微机电系统机电协同优化平台。
本文描述了一种基于遗传算法(GA)的新型系统级设计方法,该方法使用自由几何形状用于微机电系统(MEMS)器件。该方法可以同时设计和优化由自由几何形状组成的MEMS器件的电子和机械部件,以获得更好的系统性能,即高灵敏度、良好的系统稳定性和大的制造公差。此外,自由几何形状的引入允许在设计过程中具有更高的自由度,从而提高了MEMS器件的多样性和潜在性能。最后给出了一种包含自由形式机械运动前置放大器的闭环控制MEMS加速度计,以验证该设计方法的有效性。优化过程表明,主性能因数(FOM)提高了195%。在单独的机械部件(开环系统)中,灵敏度与带宽的乘积提高了151%,灵敏度提高了276%。在闭环性能方面,开闭环排量比提高了120%。在闭环系统中,灵敏度和带宽的乘积提高了27%。对制造误差和参数失配具有良好的免疫力。实验表明,当反馈电压为4.85 V时,在1 g 100 Hz的加速度输入下,闭环系统中的MEMS加速度计的位移比开环系统中的位移减小了86%。在±1g加速度输入范围内,与开环系统相比,闭环系统的静态和动态非线性分别提高了64%和61%。与开环系统相比,闭环系统的交叉轴灵敏度提高了18.43%。这是首次在实验上成功地实现了由机械运动前置放大器组成的MEMS加速度计闭环系统。
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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来源期刊
Microsystems & Nanoengineering
Microsystems & Nanoengineering Materials Science-Materials Science (miscellaneous)
CiteScore
12.00
自引率
3.80%
发文量
123
审稿时长
20 weeks
期刊介绍: Microsystems & Nanoengineering is a comprehensive online journal that focuses on the field of Micro and Nano Electro Mechanical Systems (MEMS and NEMS). It provides a platform for researchers to share their original research findings and review articles in this area. The journal covers a wide range of topics, from fundamental research to practical applications. Published by Springer Nature, in collaboration with the Aerospace Information Research Institute, Chinese Academy of Sciences, and with the support of the State Key Laboratory of Transducer Technology, it is an esteemed publication in the field. As an open access journal, it offers free access to its content, allowing readers from around the world to benefit from the latest developments in MEMS and NEMS.
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