Design and Fabrication of a High-Power Air-Coupled Capacitive Micromachined Ultrasonic Transducer Array With Concentric Annular Cells
Bibliographic record
Abstract
One shortcoming of capacitive micromachined ultrasonic transducers (CMUTs) for commercial use is the weak output power relative to traditional ultrasonic transducers. Recently, we reported an annular CMUT cell with an improved transmit efficiency over a conventional circular cell in air. Extending this paper, we designed and fabricated a nine-element concentric CMUT array to enhance the transmit power and offer depth focusing. The proposed 200-kHz array has an aperture diameter of 2.13 cm and a fill factor of 81%. A pillar-free etching process was developed to create the deep large-area cavities of the proposed wafer-bonded CMUT. The fabricated device was characterized for the static and dynamic performance using a profilometer and a laser Doppler vibrometer, respectively. The average maximum plate dynamic displacements of the CMUT driven by 20-Vpp ac excitation voltage at dc biases of 100 and 150 V were measured to be 1.28 and 1.97 μm, respectively. Accordingly, the surface output power densities were calculated to be 0.40 and 0.96 KW/m2. We investigated the crosstalk between neighboring cells and the plate-cracking phenomenon, and provided relative suggestions for improvement. This paper demonstrates the feasibility of the concentric annular-cell CMUT array design 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 | 0.000 |
| 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 | 0.001 | 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".