Outperforming piezoelectric ultrasonics with high-reliability single-membrane CMUT array elements
Bibliographic record
Abstract
It has long been hypothesized that capacitive micromachined ultrasound transducers (CMUTs) could potentially outperform piezoelectric technologies. However, challenges with dielectric charging, operational hysteresis, and transmit sensitivity have stood as obstacles to these performance outcomes. In this paper, we introduce key architectural features to enable high-reliability CMUTs with enhanced performance. Typically, a CMUT element in an array is designed with an ensemble of smaller membranes oscillating together to transmit or detect ultrasound waves. However, this approach can lead to unreliable behavior and suboptimal transmit performance if these smaller membranes oscillate out of phase or collapse at different voltages. In this work, we designed CMUT array elements composed of a single long rectangular membrane, with the aim of improving the output pressure and electromechanical efficiency. We compare the performance of three different modifications of this architecture: traditional contiguous dielectric, isolated isolation post (IIP), and insulated electrode-post (EP) CMUTs. EPs were designed to improve performance while also imparting robustness to charging and minimization of hysteresis. To fabricate these devices, a wafer-bonding process was developed with near-100% bonding yield. EP CMUT elements achieved electromechanical efficiency values as high as 0.95, higher than values reported with either piezoelectric transducers or previous CMUT architectures. Moreover, all investigated CMUT architectures exhibited transmit efficiency 2-3 times greater than published CMUT or piezoelectric transducer elements in the 1.5-2.0 MHz range. The EP and IIP CMUTs demonstrated considerable charging robustness, demonstrating minimal charging over 500,000 collapse-snap-back actuation cycles while also mitigating hysteresis. Our proposed approach offers significant promise for future ultrasonic 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) | 0.000 | 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 | 0.000 | 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".