Fine Pitch Flexible Printed Circuit Board Patterning for Miniaturized Endoscopic MicroUltrasound Arrays
Bibliographic record
Abstract
Microultrasound (micro-US) has become an invaluable tool for preclinical research and in emerging applications in clinical diagnosis and treatment guidance. Several such applications can benefit from arrays with a small footprint and endoscopic form factor. However, critical challenges arise in making electrical connections to array elements in such spatial constraints. In this work, we describe a method to pattern a high-density flexible circuit cabling on a copper-on polyimide film, using laser ablation of a polymer resist and wet etching, and then demonstrate a connection to a micro-US array. We investigate laser ablation process parameters and evaluate the ability to consistently pattern continuous copper traces. A minimum 30- [Formula: see text] pitch was achieved with 5- [Formula: see text]-wide electrode lines, and continuity of a 5-m-long trace is demonstrated. A flexible circuit with 30-mm-long traces with 30- [Formula: see text] line and 30- [Formula: see text] space before fan-out was fabricated to connect in an interleaved manner to a 32-element array with 30- [Formula: see text] element pitch. Metal deposition and laser ablation were used to connect and pattern the element electrodes to the copper traces of the flexible circuit. Electrical and acoustic measurements show good yield and consistent impedance across channels. Element pulse-echo tests demonstrated device functionality; the two-way pulse had 43-MHz center frequency and 40% fractional bandwidth (-6 dB). The proposed manufacturing methods facilitate the prototyping and fabrication of flexible endoscopic or small-footprint micro-US devices.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".