Research on Co-Optimization of Control Logic and Structural Design for Intelligent MEMS via Digital Twin Technology

The performance of Micro-Electro-Mechanical Systems (MEMS) depends on the coordinated interaction between structural design and control logic. However, existing design methodologies treat these domains independently, lacking real-time feedback and multi-objective optimization. To address this limitation, this study proposes a digital-twin-driven co-optimization framework that integrates structural and control parameter tuning within a unified, synchronized environment. The framework combines finite-element modeling, real-time sensor feedback, and a hybrid evolutionary-gradient optimization algorithm to jointly minimize energy consumption, response delay, and resonance deviation under physical constraints. Experimental validation on a silicon-based micro-cantilever MEMS demonstrates a precision improvement of 4.6%, energy reduction of 18.7%, and response delay decrease of 23.5% compared to baseline methods. The framework achieved stable convergence within 150 epochs, with a 46% lower variance and performance retention above 95% across piezoelectric and thermal MEMS devices. SHAP-based interpretability analysis further revealed that stiffness, damping, and control gain jointly explain 63% of performance variance, confirming the physical consistency of the model. These results indicate that integrating digital twin synchronization with hybrid optimization provides a reproducible and interpretable pathway for intelligent MEMS co-design, enhancing precision, efficiency, and robustness across multi-physics operating conditions.