Investigation of the Substrate’s Effect on the Transient Coupling to Printed Circuit Boards
Bibliographic record
Abstract
In this paper the effect of the dielectric substrate in the transient coupling of electromagnetic waves to printed circuit board (PCB) traces is investigated. It has already been shown that for accurate time-domain prediction of the induced voltages and currents on PCB traces the effect of the dielectric substrate must be taken into account. The time-domain features of the PCB coupling are investigated and are related back to the canonical problem of the interaction of a transient plane wave with a finite dielectric slab. This primary problem has not been studied extensively and very few analytical results are available in the literature. Full-field finite difference time-domain simulations are used to study the dielectric slab interaction. It is shown that the interaction of a transient plane wave with a dielectric slab can be split up into 2 distinct components: a surface wave travelling at the speed of light in air; and a transient component travelling inside the dielectric at the speed of light in the dielectric. The surface wave contains longitudinal and transverse components of the electric field in the direction of propagation while the dielectric wave contains only an electric field transverse to the direction of propagation. Both components contribute to the terminal response of a printed transmission line on the dielectric substrate.
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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".