High-Frequency Annular Array Fabrication Using a Flex Circuit Matching Layer
Bibliographic record
Abstract
Fabricating arrays for high-frequency image applications such as ophthalmic imaging, intravascular imaging, and small animal imaging is challenging. For example, an array for intravascular imaging must be small enough to fit within the lumen of a catheter and inexpensive enough to be discarded after a single use. This article presents a new method for fabricating high-frequency annular arrays that is simple and inexpensive. The annular array elements are defined by the electrode pattern on a back surface of a polyimide quarter-wavelength matching layer that is glued to the front face of a ceramic transducer substrate (PZT5H). Electrical losses associated with bonding the matching layer to the transducer substrate are reduced by fabricating a second set of electrodes on the transducer substrate and then bonding the substrates using an anisotropic conductive epoxy. The feasibility of this technique was established by fabricating a seven-element, 20-MHz, 5-mm diameter annular array. The prototype array produced a pulse with a -6-dB factional bandwidth of 50%, an insertion loss of 22 dB, and secondary lobes in the radiation pattern at f/2 that decreased to -65 dB with respect to the main lobe with a peak amplitude of -53 dB.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".