Capacitive Micromachined Transducers With Out-of-Plane Repulsive Actuation for Enhancing Ultrasound Transmission in Air
Bibliographic record
Abstract
This paper presents a novel approach to enhance ultrasound transmission using capacitive micromachined ultrasonic transducers (CMUTs). This is achieved by increasing the cavity height through the use of electrostatic repulsion. Conventional CMUTs based on attractive forces have promising electroacoustic characteristics but limited output pressure, compared to piezoelectric transducers due to the limited motion ranges for CMUTs imposed by the capacitive transduction gap. Therefore, we propose here an electrostatic repulsive CMUT design with three fixed electrodes and one movable electrode that displaces out-of-plane. Simulation results demonstrate the design’s effectiveness in increasing the transducer’s range of motion, thus enhancing transmission sound pressure. Prototypes were fabricated using MEMSCAP’s PolyMUMPs process. Repulsive actuation allows for more than an order of magnitude (11x) improvement in the allowable motion range and therefore an improvement in the acoustic output by a factor up to 25.42 dB. Experimental tests using a vibrometer and an ultrasonic microphone confirm the effectiveness of the proposed approach. The CMUT array operates over a wide band of frequencies from 150 kHz to 650 kHz, which opens the doors for several applications such as ranging, gesture recognition, and non-destructive testing, with the potential for further improvements in ultrasound transmission. [2023-0158]
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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) | 0.000 | 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) | 0.001 | 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".