Transparent capacitive micromachined ultrasonic transducer (CMUT) arrays for real-time photoacoustic applications
Bibliographic record
Abstract
Photoacoustic imaging has shown great potential for non-invasive high-resolution deep-tissue imaging. Minimizing the optical and acoustic paths for excitation and detection could significantly increase the signal-to-noise ratio. This could be accomplished by transparent transducers permitting through-transducer illumination. However, most ultrasound transducers are not optically transparent. Capacitive micromachined ultrasound transducer (CMUT) technology has compelling properties compared to piezoelectric transducers such as wide bandwidth and high receive sensitivity. Here, we introduce transparent CMUT linear arrays with high transparency in the visible and near-infrared range. To fabricate the devices, we used an adhesive wafer bonding technique using photosensitive benzocyclobutene (BCB) as both a structural and adhesive layer with a glass-indium-tin-oxide (ITO) substrate. Silicon nitride is used as the membrane material ensuring hermiticity and optical transparency. Our fabricated transducer arrays consist of 64 and 128 elements with immersion operation frequency of 8 MHz, enabling high-resolution imaging. ITO, along with thin metal strips, are used as a conductive layer for the top electrodes with minimal impact on device transparency. Fabricated devices have shown average transparency of 70% in the visible wavelength range that goes up to 90% in the near-infrared range. Arrays are wire-bonded to interfacing electronics and connected to a research ultrasound platform for phantom imaging. Arrays exhibited signal-to-noise (SNR) of 40 dB with 30V bias voltage and laser fluence of 13.5 mJ / cm 2 . Arrays with 128 channels provided lateral and axial resolutions of 234 µm and 220 µm , respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".