An Optimization and Comparative Study of Air-Coupled CMUT Cells With Circular and Annular Geometries
Bibliographic record
Abstract
Air-coupled capacitive micromachined ultrasonic transducers (CMUTs) with annular cell geometry have recently been reported to have a promising transmit sensitivity. This paper reports three optimization schemes, which further improve the transmit sensitivity and also help achieve a reasonable comparison between the novel annular and conventional circular cells. Lumped element models of both cell types with laminate plate structures are presented. Based on these models, a design optimization flowchart was constructed to facilitate analytical optimization on the three schemes. Circular and annular CMUTs with a common 97-kHz natural resonance frequency were fabricated and characterized to verify the efficacy of the optimization principle. Using the optimization flowchart, annular and circular cells with frequencies ranging from 100 to 300 kHz were analytically optimized and then compared. The comparison results demonstrate that, given the same dc bias and ac excitation voltage, the output power density at the plate surface of the optimized annular cell is double that of the optimized circular cell. Additionally, when generating the same surface power density, an optimized annular cell requires either half the dc bias or half the ac excitation voltage of an optimized circular cell. This paper provides a practical optimization framework for CMUT cell design and demonstrates the superiority of annular cells for air-coupled applications.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".