@inproceedings{635ebb41953c49c4933aa3960536b51b,
title = "A Mems Electro-Mechanical Co-Optimization Platform Featuring Freeform Geometry Optimization Based on a Genetic Algorithm",
abstract = "This paper describes a novel, system-level design methodology based on a genetic algorithm (GA) using freeform geometries for microelectromechanical systems (MEMS) devices. A MEMS accelerometer comprising a freeform mechanical motion preamplifier embedded in a closed-loop control system is presented to demonstrate the effectiveness of the design approach. The optimization process improves the main figure-of-merit (FOM) by 482%. Measurements show that the displacement of the MEMS accelerometer in the closed-loop system is decreased by 86% with a 4.85 V feedback voltage for 1 g acceleration at 100 Hz compared with an open-loop system.",
keywords = "Accelerometer, closed-loop system, genetic algorithm (GA), microelectromechanical systems (MEMS), microlevers, system-level optimization",
author = "Chen Wang and Aojie Quan and Weidong Fang and Haoyu Huang and Linlin Wang and Michiel Gidts and Yangyang Guan and Huafeng Liu and Hemin Zhang and Jian Bai and Yuan Wang and Michael Kraft",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 35th IEEE International Conference on Micro Electro Mechanical Systems Conference, MEMS 2022 ; Conference date: 09-01-2022 Through 13-01-2022",
year = "2022",
doi = "10.1109/MEMS51670.2022.9699829",
language = "英语",
series = "Proceedings of the IEEE International Conference on Micro Electro Mechanical Systems (MEMS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "774--777",
booktitle = "35th IEEE International Conference on Micro Electro Mechanical Systems Conference, MEMS 2022",
}