Deep-Learning-Based Optimization for a Low-Frequency Piezoelectric MEMS Energy Harvester
Bibliographic record
Abstract
High operational frequency is one of the major limitations of the conventional MEMS vibration energy harvesters. In this work, we present a piezoelectric MEMS energy harvester with the capability of operating at a low resonant frequency (i.e., less than 200 Hz). The proposed harvester has a symmetric serpentine structure with a doubly clamped configuration comprising several proof masses at the junctions. In order to facilitate the design process and determine the optimum physical dimensions, an artificial neural network is used to model the design. In the first step, a dataset with 108 samples is generated by finite element modeling (FEM) to train a deep neural network. The validation results indicate that the trained deep neural network model can achieve around 90% estimation accuracy of device features, such as resonant frequency and harvested voltage. Next, this trained model is integrated with genetic algorithm as a performance estimator to optimize the geometry of the harvester to lower the resonant frequency and improve the harvested voltage. An optimized harvester with a total area of 8.7 mm2has been fabricated through a standard micromachining process. Our measurement results confirm that the proposed deep-learning-based method can help reach the balanced summit of both higher power density and lower resonant frequency among the published works. Our prototype device features the first resonant frequency of 121.7 Hz and the harvested power of$0.73~\mu \text{W}$under 0.1g input acceleration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".