Sensitivity Analysis of Novel PolyMUMPs-Based Ultrasonic MEMS Microphones
Bibliographic record
Abstract
MEMS (micro-electro-mechanical-systems) microphones have been widely studied over the past decades. Studies have focused on the audible frequency range, while microphones operating in the ultrasonic range beyond 20 kHz (inaudible range) have rarely been explored. The aim of this work is to design a capacitive-based ultrasonic MEMS microphone having a flat response of up to 100 kHz. One major application for this is leak detection from highly pressurized pipes. The proposed microphone structure accounts for the limitations associated to the PolyMUMPs standard surface micro-machining process to enable its potential for future fabrication. Equations governing static and dynamic behavior are derived and solved using MATLAB by implementing the Galerkin method. The finite element simulations are conducted using COMSOL to present the sensitivity analysis while the model is further validated by obtaining the sensitivity curve of an audible MEMS microphone available in the literature. The sensitivity of the proposed microphone is -78 dB with a flat response within ±2 dB at up to 100 kHz. Considering the sensitivity of the benchmark ultrasonic bulk microphone B&K 4138 being equal to -60 dB, it can be concluded that although the proposed PolyMUMPs-based device has lower sensitivity, it eliminates the need for the costly additional back-etch processing required to create a larger back chamber.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".