Bibliographic Details
| Title: |
Automated multiphysics MEMS co-optimization platform: Integrated fabrication constraints and accelerated design for high-linearity sensors. |
| Authors: |
Yu, Peizhi, Wang, Chenzi, Lu, Wenshuai, You, Zheng |
| Source: |
Nanotechnology & Precision Engineering; Jun2026, Vol. 9 Issue 2, p1-8, 8p |
| Subject Terms: |
Accelerometers, Optimization algorithms, Computer simulation, Reduced-order models, Mathematical optimization |
| Reviews & Products: |
MatLab (Computer software) |
| 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. HIGHLIGHTS: • An automated multiphysics MEMS co-optimization platform integrates Coventor MEMS+ ROM with MATLAB, embedding manufacturing constraints to bridge simulation–fabrication gaps and enhance sensor linearity. • Modified Nelder–Mead outperforms evolutionary algorithms (e.g., genetic algorithms) by ∼ 100 × computational efficiency in comb/beam optimization, while maintaining superior design quality (e.g., 51.2% linearity gain). • The platform delivers manufacturable designs: 96.79% linearity improvement for combs, and sensitivity preservation for beams, enabling rapid high-performance MEMS prototyping. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |