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四质量振动微机电系统陀螺仪的全系统仿真与分析

Full-System Simulation and Analysis of a Four-Mass Vibratory MEMS Gyroscope.

作者信息

Ouyang Chenguang, He Wenzheng, Jia Lu, Wang Peng, Zhao Kaichun, Xing Fei, You Zheng

机构信息

Department of Precision Instrument, Tsinghua University, Beijing 100084, China.

Engineering Research Center for Semiconductor Integrated Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100084, China.

出版信息

Micromachines (Basel). 2025 Mar 30;16(4):414. doi: 10.3390/mi16040414.

Abstract

This study presents a full-system simulation methodology for MEMS, addressing the critical need for reliable performance prediction in microsystem design. While existing digital tools have been widely adopted in related fields, current approaches often remain fragmented and focused on specific aspects of device behavior. In contrast, our proposed framework conducts comprehensive device physics-level analysis by integrating mechanical, thermal and electrical modeling with process simulation. The methodology features a streamlined workflow that enables direct implementation of simulation results into fabrication processes. We model a MEMS gyroscope as an example to verify our simulation approach. Multiphysics coupling is considered to capture real-world device behavior, followed by quantitative assessment of manufacturing variations through virtual prototyping and experimental validation demonstrating the method's accuracy and practicality. The proposed approach not only improves design efficiency but also provides a robust framework for MEMS gyroscope development. With its ability to predict device performance, this methodology is expected to become an essential tool in microsensor research and development.

摘要

本研究提出了一种用于微机电系统(MEMS)的全系统仿真方法,满足了微系统设计中对可靠性能预测的迫切需求。虽然现有数字工具已在相关领域广泛应用,但当前方法往往仍较为零散,且侧重于器件行为的特定方面。相比之下,我们提出的框架通过将机械、热和电建模与工艺仿真相结合,进行全面的器件物理级分析。该方法具有简化的工作流程,能够将仿真结果直接应用于制造工艺。我们以一个MEMS陀螺仪为例进行建模,以验证我们的仿真方法。考虑多物理场耦合以捕捉实际器件行为,随后通过虚拟原型制作对制造变化进行定量评估,并通过实验验证证明该方法的准确性和实用性。所提出的方法不仅提高了设计效率,还为MEMS陀螺仪的开发提供了一个强大的框架。凭借其预测器件性能的能力,该方法有望成为微传感器研发中的重要工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c96e/12029152/b4d5355c982d/micromachines-16-00414-g001.jpg

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