Design of freeform geometries in a MEMS accelerometer with a mechanical motion preamplifier based on a genetic algorithm

This paper describes a novel, semiautomated design methodology based on a genetic algorithm (GA) using freeform geometries for microelectromechanical systems (MEMS) devices. The proposed method can design MEMS devices comprising freeform geometries and optimize such MEMS devices to provide high sensitivity, large bandwidth, and large fabrication tolerances. The proposed method does not require much computation time or memory. The use of freeform geometries allows more degrees of freedom in the design process, improving the diversity and performance of MEMS devices. A MEMS accelerometer comprising a mechanical motion amplifier is presented to demonstrate the effectiveness of the design approach. Experimental results show an improvement in the product of sensitivity and bandwidth by 100% and a sensitivity improvement by 141% compared to the case of a device designed with conventional orthogonal shapes. Furthermore, excellent immunities to fabrication tolerance and parameter mismatch are achieved. Based on a genetic algorithm using freeform geometries, a novel, powerful, semi-automated design methodology has been devised that can be applied to a wide range of micro-electrical-mechanical system (MEMS) devices. Thanks to their small size and low cost, MEMS devices find wide application in many areas, but the conventional, simple designs of the devices often limit their performance. A team headed by Jian Bai at Zhejiang University, China and Michael Kraft at University of Leuven, Belgium, has developed a method for designing MEMS devices in which the use of freeform geometries offers greater freedom in the design process, thereby enhancing the diversity and performance of the devices. The experimental results indicated a sensitivity improvement of 141% compared with a conventionally designed device. The authors believe that their methodology can be applied to many MEMS devices and result in unconventional geometric shapes with higher performance.