Research on Co-Optimization of Control Logic and Structural Design for Intelligent MEMS via Digital Twin Technology
Keywords:
digital twin, MEMS co-optimization, hybrid evolutionary-gradient algorithm, multi-physics robustness, structural-control couplingAbstract
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.References
1. G. Sharma, T. Kaur, S. K. Mangal, and A. Kohli, "Investigating bearing and gear vibrations with a Micro-Electro-Mechanical Systems (MEMS) and machine learning approach," Results in Engineering, vol. 24, p. 103499, 2024. doi: 10.1016/j.rineng.2024.103499
2. T. Sripriya, and A. A. Juliette, "Theoretical analysis and comparative study of assorted diaphragm primarily based Micro Electro Mechanical System (MEMS) optical pressure sensors," Expert Systems with Applications, vol. 245, p. 122993, 2024. doi: 10.1016/j.eswa.2023.122993
3. M. N. Khan, and I. Ahmad, "Harnessing Digital Twins: Advancing Virtual Replicas for Optimized System Performance and Sustainable Innovation," Babylonian Journal of Mechanical Engineering, vol. 2025, pp. 18-33, 2025.
4. D. Dadkhah, and S. R. Moheimani, "Data-driven design of damping controllers for a MEMS force sensor with uncertain dynamics," In 2025 American Control Conference (ACC), July, 2025, pp. 2560-2565.
5. A. Kurmendra, "MEMS switch realities: Addressing challenges and pioneering solutions," Micromachines, vol. 15, no. 5, p. 556, 2024.
6. J. Li, L. Wang, B. Cao, H. Zhang, J. Chen, Q. Liu, and Y. Yang, "Multi-objective optimization design and experiment for a magnetic micro-vibration isolator considering stiffness trade-offs," Review of Scientific Instruments, vol. 95, no. 8, 2024. doi: 10.1063/5.0217099
7. S. Rao, and S. Neethirajan, "Computational architectures for precision dairy nutrition digital twins: A technical review and implementation framework," Sensors, vol. 25, no. 16, p. 4899, 2025. doi: 10.20944/preprints202506.2401.v1
8. M. Cirstea, K. Benkrid, A. Dinu, R. Ghiriti, and D. Petreus, "Digital electronic system-on-chip design: Methodologies, tools, evolution, and trends," Micromachines, vol. 15, no. 2, p. 247, 2024. doi: 10.3390/mi15020247
9. M. R. Jadhav, S. Singh, P. John, H. N. Bhargaw, and D. K. Rajak, "Advancements and recent trends in shape memory alloy actuators: position control and emerging applications," International Journal of Dynamics and Control, vol. 13, no. 3, p. 114, 2025. doi: 10.1007/s40435-025-01615-8
10. D. Menon, B. Anand, and C. L. Chowdhary, "Digital twin: exploring the intersection of virtual and physical worlds," IEEE Access, vol. 11, pp. 75152-75172, 2023. doi: 10.1109/access.2023.3294985
11. Y. Xu, S. Liu, C. He, H. Wu, L. Cheng, G. Yan, and Q. Huang, "Reliability of MEMS inertial devices in mechanical and thermal environments: A review," Heliyon, vol. 10, no. 5, 2024. doi: 10.1016/j.heliyon.2024.e27481
12. W. Zheng, H. Lu, M. Zhang, Q. Wu, Y. Hou, and J. Zhu, "Distributed energy management of multi-entity integrated electricity and heat systems: A review of architectures, optimization algorithms, and prospects," IEEE Transactions on Smart Grid, vol. 15, no. 2, pp. 1544-1561, 2023. doi: 10.1109/tsg.2023.3310947
13. L. Roda-Sanchez, F. Cirillo, G. Solmaz, T. Jacobs, C. Garrido-Hidalgo, T. Olivares, and E. Kovacs, "Building a smart campus digital twin: System, analytics, and lessons learned from a real-world project," IEEE Internet of Things Journal, vol. 11, no. 3, pp. 4614-4627, 2023. doi: 10.1109/jiot.2023.3300447
14. X. He, L. Yang, Z. Gong, Y. Pang, J. Li, Z. Kan, and X. Song, "Digital twin-based online structural optimization? Yes, it's possible!," Thin-Walled Structures, vol. 208, p. 112796, 2025. doi: 10.1016/j.tws.2024.112796
15. J. Bofill, M. Abisado, J. Villaverde, and G. A. Sampedro, "Exploring digital twin-based fault monitoring: Challenges and opportunities," Sensors, vol. 23, no. 16, p. 7087, 2023. doi: 10.3390/s23167087
