A Unified AI Approach for Modeling the Properties of MEMS Ultrasonic Transducers
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it