Design of a MEMS discretized hyperbolic paraboloid geometry ultrasonic sensor microarray
Bibliographic record
Abstract
Design of a discretized hyperbolic paraboloid geometry beamforming array of capacitive micromachined ultrasonic transducers (CMUT) has been presented. The array can intrinsically provide a broadband constant beamwidth beamforming capability without any microelectronic signal processing. A mathematical model has been developed and verified to characterize the array response. A design methodology has been presented that enables determination of the array's physical dimensions and CMUT modeling in a straightforward manner. Developed methodology has been used to design two discretized hyperbolic paraboloid geometry beamforming CMUT arrays: one in the 2.3 MHz to 5.2 MHz frequency range and another in the 113 kHz to 167 kHz frequency range. CMUTs have been designed using a cross-verification method that involves lumped element modeling, 3-D electromechanical finite element analysis (FEA), and microfabrication simulation. The developed array has the potential to be used in real-time automotive collision-avoidance applications, medical diagnostic imaging and therapeutic applications, and industrial sensing.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".