Development of a Novel CMUT-Based Concentric Dual-Element Ultrasonic Transducer: Design, Fabrication, and Characterization
Bibliographic record
Abstract
Capacitive micromachined ultrasonic transducers (CMUTs) have shown great potential to make a strong impact on ultrasound imaging technologies over the years. However, a critical weakness of CMUTs is their relatively low output pressure at transmission. To address this, we present a novel dual-element ultrasonic transducer that incorporates two transducer technologies by using a circular commercial piezoelectric element for ultrasound transmission and an annular CMUT element for reception. The hybrid transducer combines the broad bandwidth and high receive sensitivity of the CMUT and the high output pressure of the piezoelectric transducer to improve the overall sensitivity and axial resolution. The annular CMUT is designed, fabricated, and concentrically aligned with the piezoelectric probe via a custom housing. A through-wafer etching scheme based on deep reactive-ion etching is developed to create the CMUT annulus. Immersion characterization reveals that the CMUT has a center frequency of 2.63 MHz, a -6-dB fractional bandwidth of 131%, and a receive sensitivity of 22.57 mV/kPa, compared with 2.34 MHz, 42%, and 4.28 mV/kPa of the piezoelectric probe. The round-trip measurements show that the hybrid dual-element transducer improves the axial resolution by 25.58% and the signal-to-noise ratio by 8.55 dB over the commercial piezoelectric probe.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".