Frequency Tuning and Efficiency Improvement of Piezoelectric MEMS Vibration Energy Harvesters

Although lots of efforts have been made to produce the optimal piezoelectric MEMS vibration energy harvesters in the past decade, the state-of-the-art study is still dependent on the designers' discretion, which demands a considerable amount of design time to gain optimum structure. In this paper, we propose a new design automation technique with minimum human effort based on a genetic algorithm (GA), which is an evolutionary computation method for optimizing complex problems. In this regard, the analytic equations to estimate resonant frequency and amplitude of the harvested voltage for two different configurations of unimorph MEMS piezoelectric harvesters (i.e., with and without integration of a proof mass) are presented. Thanks to the sufficient estimation accuracy, they are utilized as the required fitness functions of the GA. By simultaneously considering operation at lower frequency and higher energy conversion efficiency in a small silicon area as the objectives, the GA strives to optimize the physical aspects of the harvesters (e.g., beam length, beam width, and proof mass length). The GA performance is studied and evaluated analytically, numerically, and experimentally with its effects on the mechanical properties discussed. By leveraging the micro-fabrication process, we demonstrate that the GA can optimize the mechanical geometry of the prototyped harvester effectively and efficiently, whose peak harvested voltage increases from 310 to 1900 mV at the reduced resonant frequency from 886 to 425 Hz with the highest normalized voltage density of 163.88 among the alternatives.