Fabrication and performance of a miniaturized 64-element high-frequency endoscopic phased array
Bibliographic record
Abstract
We have developed a 40-MHz, 64-element phased-array transducer packaged in a 2.5 x 3.1 mm endoscopic form factor. The array is a forward-looking semi-kerfed design based on a 0.68Pb(Mg(1/3)Nb(2/3))O(3) - 0.32PbTiO3 (PMN-32%PT) single-crystal wafer with an element-to-element pitch of 38 µm. To achieve a miniaturized form factor, a novel technique of wire bonding the array elements to a polyimide flexible circuit board oriented parallel to the forward looking ultrasound beam and perpendicular to the array was developed. A technique of partially dicing into the back of the array was also implemented to improve the directivity of the array elements. The array was fabricated with a single-layer P(VDF-TrFE)-copolymer matching layer and a polymethylpentene (TPX) lens for passive elevation focusing to a depth of 7 mm. The two-way -6-dB pulse bandwidth was measured to be 55% and the average electromechanical coupling (k(eff)) for the individual elements was measured to be 0.62. The one-way -6-dB directivities from several array elements were measured to be ±20°, which was shown to be an improvement over an identical kerfless array. The -3-dB elevation focus resulting from the TPX lens was measured to be 152 µm at the focal depth, and the focused lateral resolution was measured to be 80 µm at a steering angle of 0°. To generate beam profiles and images, the probe was connected to a commercial ultrasound imaging platform which was reprogrammed to allow for phased array transmit beamforming and receive data collection. The collected RF data were then processed offline using a numerical computing script to generate sector images. The radiation pattern for the beamformed transmit pulse was collected along with images of wire phantoms in water and tissue-equivalent medium with a dynamic range of 60 dB. Finally, ex vivo tissue images were generated of porcine brain tissue.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".