A Unified AI Approach for Modeling the Properties of MEMS Ultrasonic Transducers
Bibliographic record
Abstract
Finite-element (FE) sweeps remain the standard for analyzing microelectromechanical systems (MEMS) ultrasonic transducers but are slow to traverse large geometry–bias spaces. This paper develops a multi-output surrogate that predicts the ultrasonic response of piezoelectric micromachined ultrasonic transducers (PMUTs) directly from compact design and operating descriptors, enabling millisecond-level evaluation for design-space exploration and bias tuning. Inputs include shape (circle/square), diaphragm diameter (200–600 μm), anchor count/geometry (2–8), and DC bias (5–15 V), with optional fabrication process features (e.g., membrane thickness, cavity depth). Targets comprise resonance frequency f0, quality factor (Q), sensitivity, center displacement, bias-tuning coefficient, β, and motional resistance, Rm. A dataset of 40 PiezoMUMPs chips is assembled with labels from COMSOL sweeps, laser Doppler vibrometry (LDV), and admittance-based fits. Six architectures, residual multilayer perceptron (MLP), convolutional neural network (CNN), gated recurrent unit (GRU), long short-term memory (LSTM), Transformer, and a CNN+LSTM hybrid, are benchmarked under a standardized pipeline (feature scaling, multi-target loss, 70/15/15 split, and five-fold cross-validation) with physics-preserving augmentation and multi-fidelity densification. All models achieve sub-percent normalized error; a compact Transformer encoder attains ≈ 0.03% normalized RMSE while preserving physically consistent trends with respect to shape, size and bias. The surrogate generalizes and supports inverse design, multi-objective optimization, and closed-loop bias control, reducing reliance on inner-loop FE sweeps.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".