Transceiver Design for CMUT-Based Super-Resolution Ultrasound Imaging
Bibliographic record
Abstract
A recently introduced structure for the capacitive micromachined ultrasonic transducers (CMUTs) has focused on the applications of the asymmetric mode of vibration and has shown promising results in construction of super-resolution ultrasound images. This paper presents the first implementation and experimental results of a transceiver circuit to interface such CMUT structures. The multiple input/multiple output receiver in this work supports both fundamental and asymmetric modes of operation and includes transimpedance amplifiers and low-power variable-gain stages. These circuit blocks are designed considering the trade-offs between gain, input impedance, noise, linearity and power consumption. The high-voltage transmitter can generate pulse voltages up to 60 V while occupying a considerably small area. The overall circuit is designed and laid out in a 0.35 μm CMOS process and a four-channel transceiver occupies 0.86 × 0.38 mm(2). The prototype chip is characterized in both electrical and mechanical domains. Measurement results show that each receiver channel has a nominal gain of 110 dBΩ with a 3 dB bandwidth of 9 MHz while consuming 1.02 mW from a 3.3 V supply. The receiver is also highly linear, with 1 dB compression point of minimum 1.05 V which is considerably higher than the previously reported designs. The transmitter consumes 98.1 mW from a 30 V supply while generating 1.38 MHz, 30 V pulses. The CMOS-CMUT system is tested in the transmit mode and shows full functionality in air medium.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".