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Research Article | Open Access

Automated multiphysics MEMS co-optimization platform: Integrated fabrication constraints and accelerated design for high-linearity sensors

Peizhi Yu Chenzi Wang Wenshuai Lu( )Zheng You ( )
Department of Precision Instrument, Tsinghua University, Beijing, China
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Abstract

This paper proposes an innovative automated multiphysics Microelectromechanical systems (MEMS) co-optimization platform integrating the embedded reduced-order modeling offered by Coventor MEMS+ with MATLAB’s numerical computing environment, addressing critical limitations in conventional design workflows. Our framework demonstrates three key advances. First, the platform enables systematic co-optimization of structural parameters (comb geometries and suspension beams) through constrained design space exploration, achieving an order-of-magnitude improvement in sensor linearity while maintaining baseline sensitivity as validated by accelerometer case studies. Second, the implementation of manufacturing-aware optimization incorporates process tolerance constraints and geometric feasibility checks, effectively bridging the gap between simulation-based optimization and physical fabrication requirements. Third, comprehensive algorithm benchmarking reveals that the modified Nelder–Mead method achieves a superior convergence efficiency (~100× computational efficiency) compared with evolutionary algorithms, while maintaining design quality, providing critical advantages for rapid MEMS prototyping. The platform establishes a new paradigm for MEMS co-design through tight integration of multiphysics simulation, manufacturing constraints, and intelligent optimization algorithms.

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Nanotechnology and Precision Engineering

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Cite this article:
Yu P, Wang C, Lu W, et al. Automated multiphysics MEMS co-optimization platform: Integrated fabrication constraints and accelerated design for high-linearity sensors. Nanotechnology and Precision Engineering, 2026, 9(2). https://doi.org/10.1063/5.0299387

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Received: 27 August 2025
Accepted: 23 December 2025
Published: 04 May 2026
© 2026 Author(s).

All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC BY-NC-ND) license (https://creativecommons.org/licenses/by-nc-nd/4.0/).