Python-Based Open-Source Electro-Mechanical Co-Optimization System for MEMS Inertial Sensors

Area of Science:

• Electrical Engineering
• Mechanical Engineering
• Materials Science

Background:

• Rapid advancements in micro- and nanodevice fabrication have outpaced the evolution of their design and optimization processes.
• Improved design, simulation, and optimization are crucial for unlocking the full potential of micro- and nanodevices.
• Current methodologies often lack integrated approaches for optimizing complex micro-electromechanical systems (MEMS).

Purpose of the Study:

• To present a novel methodology for the electro-mechanical co-optimization of MEMS inertial sensors.
• To develop a software tool that integrates geometry design, finite element method (FEM) analysis, damping calculation, and electronic domain simulation.
• To facilitate a system-level MEMS design flow enabling communication between electrical and mechanical domains for optimized performance.

Main Methods:

• Development of a software tool integrating geometry design, FEM analysis, damping calculation, and electronic domain simulation.
• Implementation of a genetic algorithm (GA) for the optimization process.
• Application of the methodology to an open-loop capacitive MEMS accelerometer and an open-loop Coriolis vibratory MEMS gyroscope.

Main Results:

• Achieved a sensitivity improvement of 193.77% for the MEMS accelerometer.
• Demonstrated a sensitivity improvement of 420.9% for the MEMS gyroscope.
• Validated the efficacy of the electro-mechanical co-optimization methodology in enhancing MEMS inertial sensor performance.

Conclusions:

• The developed methodology offers a significant advancement in the design and optimization of MEMS inertial sensors.
• Integrated electro-mechanical co-optimization leads to substantial performance improvements in MEMS devices.
• This approach facilitates a more efficient and effective system-level design flow for MEMS.