Frequency Tuning and Efficiency Improvement of Piezoelectric MEMS Vibration Energy Harvesters
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".