@inproceedings{ded30b19b58947199b54b8068ec32987,
title = "Electro-Mechanical Co-optimization of MEMS Devices in Python",
abstract = "This paper describes a novel methodology for electro-mechanical co-optimization of microelectromechanical systems (MEMS) devices. It facilitates a MEMS design flow at system-level, in which mechanical and electrical domains interact with each other to achieve an optimal system performance. The proposed design methodology is fully based on Python, featuring a single platform, open-source as well as low computational cost. A simple open-loop MEMS accelerometer is presented to demonstrate the effectiveness of the design methodology. It improved the accelerometer in terms of the product of sensitivity and resonant frequency by 44% and sensitivity by 110%. ",
keywords = "FEM, genetic algorithm, mechanical modelling, MEMS, Python",
author = "Rui Esteves and Chen Wang and Pinto, {Joana Vaz} and Michael Kraft",
note = "This work was supported by the INTERREG V-A project “Einstein Telescope EMR Site & Technology” (E-TEST, EMR113).; 2020 IEEE Sensors, SENSORS 2020 ; Conference date: 25-10-2020 Through 28-10-2020",
year = "2020",
month = oct,
day = "25",
doi = "10.1109/SENSORS47125.2020.9278723",
language = "English",
series = "Proceedings of IEEE Sensors",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE Sensors, SENSORS 2020 - Conference Proceedings",
address = "United States",
}