Wireless ultrasonic power transfer using a pre-charged CMUT structure with a built-in charge storage capacitor
Bibliographic record
Abstract
Abstract Capacitive micromachined ultrasonic transducer (CMUT) technology is a potential candidate to implement an ultrasonic power receiver for implantable medical devices (IMDs) because CMUT technology employs photolithography-based microfabrication techniques amenable to miniaturization, integration with electronics, and biocompatibility. Pre-charged CMUTs operating in constant-charge mode eliminate the DC bias and this mode of operation is more suitable for ultrasound power transfer to IMDs. We designed and fabricated a novel pre-charged CMUT structure with a built-in charge storage capacitor. This new configuration features a floating electrode between the upper and lower electrodes. Charges are stored on this floating electrode prior to implantation by directly bringing the floating electrode into contact with the bottom electrode while applying a DC bias between the top and bottom electrodes of the CMUT. After pre-charging the CMUT, the charges are retained without any leakage, as confirmed by occasional measurements over the course of about two years. We have also demonstrated that this device allows operation without a DC bias and can be used as a power receiver in an IMD. In the presented design, the CMUT can be pre-charged at a desired precise charge level. The amount of trapped charge can be controlled by holding the floating electrode in contact with the bottom electrode by applying external ultrasound pressure and simultaneously maintaining a DC bias. The maximum received power was 10.1 mW, corresponding to a received power density of 3.1 mW/mm2, with a 14.5% efficiency. We have achieved an acoustic-to-electrical power conversion efficiency as high as 29.7% at lower input power levels.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".