Uncertainty Quantification of the Insertion Loss of an Automotive PCB Stripline
Bibliographic record
Abstract
In this combination of measurement and modeling, the relative effect of process variations present in the cross-section dimensions and material properties of two automotive printed circuit boards (PCBs), with 10-inch striplines is studied. Specifically, the insertion loss (IL) uncertainty up to 20 GHz. The PCBs were horizontally cut into fifteen cross-section samples each. Normal distributions were obtained after measurements of their physical dimensions and for the PCB dielectric properties, a uniform distribution was selected. The measured distributions in combination with assumed stochastic variations for the dielectric properties of the striplines were used to simulate a 2D cross-section via an electromagnetic (EM) 2D-Method of Moments (MoM) solver. The sensitivities due to the input variables were obtained through a second order polynomial chaos expansion (PCE) in two different analysis. The first case focused on the measured physical dimensions variability and showed that the conductor roughness had the biggest effect on the IL uncertainty. The second case analyzed the combination between measured data and the assumed stochastic variation of the dielectric properties; and showed a bigger dependence of the IL uncertainty due to the dielectric properties than the physical dimensions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".