Electrical impedace matching of CMUT cells
Bibliographic record
Abstract
Capacitive micromachined ultrasonic transducers (CMUTs) have admirable performance that make them suitable for use in ultrasound imaging and therapeutic applications. Maximizing acoustic power output from CMUTs is of major importance to ensure competitive signal-to-noise ratio. In this paper, ADS validated models were used to predict device performance before and after using impedance matching networks. Electrical impedance matching of the large signal models of the CMUT cells were studied in detail. Membrane velocity and acoustic pressure of a single 3MHz CMUT cell, a 2 by 10 linear CMUT array and 6 by 6 CMUT square array were measured and compared with and without matching networks. For a given bias voltage, we found the maximum possible AC drive-level we could apply without collapsing the membrane, then found the mean membrane velocity at this signal drive-level in the cases where a matching network was present or absent. The results show remarkable improvements in output acoustic pressure which can be useful for some imaging and especially ultrasound therapeutic applications. The scattering parameters of a 5 by 100 linear CMUT array were measured using a vector network analyzer. Different kinds of impedance matching networks were designed to transfer more power to the arrays. Experiments show admirable improvements and have a good agreement with simulation results.
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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 | 0.002 |
| 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 | 0.001 | 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".