Study of fiberweave effect through simulation and measurement on performance of differential stripline at high frequency
Bibliographic record
Abstract
Investigation on simulation and measurement of fiber-weave effect on performance of a differential stripline at high frequency is presented in this paper. Meg 4 substrate with 2x1067 fiber-weave is used to fabricate a 4.1 inch differential stripline on a 4 mil substrate. This paper shows the benefits of routing with at least 10 degree angle. Even by using a dense 2x1067 fiber-weave, if two fibers place on top of each other, routing in 0 degree can create 3.6 Ps/inch delay between P and N on a differential stripline, which can create a deep notch on insertion loss of 4.1 inch differential stripline at 31 GHz. To be able to do the measurement, a 2.92mm - K connector - to stripline transition is designed. Then a wide band TRL calibration kit was used to de-embed the break-out traces and the K-connector from the S parameter measurements. Frequency and time domain measurement and simulation results have acceptable correlation. A high resolution confocal microscope with x100 magnification is used to measure the dimension of fiber-weave to be able to do an accurate simulation.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".