Python-based MEMS inertial sensors design, simulation and optimization

• Autor(a) principal: Esteves, Rui Amendoeira
• Data de Publicação: 2020
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10362/110349
• topic: Microelectromechanical systems (MEMS); inertial sensors; Python; finite element method; genetic algorithm; optimization; Domínio/Área Científica::Engenharia e Tecnologia::Nanotecnologia

With the rapid growth in microsensor technology, a never-ending range of possible applications emerged. The developments in fabrication techniques gave room to the creation of numerous new products that significantly improve human life. However, the evolution in the design, simulation, and optimization process of these devices did not observe a similar rapid growth. Thus, the microsensor technology would benefit from significant improvements in this domain. This work presents a novel methodology for electro-mechanical co optimization of microelectromechanical systems (MEMS) inertial sensors. The developed software tool comprises geometry design, finite element method (FEM) analysis, damping calculation, electronic domain simulation, and a genetic algorithm (GA) optimization process. It allows for a facilitated system-level MEMS design flow, in which electrical and mechanical domains communicate with each other to achieve an optimized system performance. To demonstrate the efficacy of the co-optimization methodology, an open-loop capacitive MEMS accelerometer and an open-loop Coriolis vibratory MEMS gyroscope were simulated and optimized - these devices saw a sensitivity improvement of 193.77% and 420.9%, respectively, in comparison to its original state.