Impedance and Propagation Delay Characterization for FR-4 Printed Circuit Boards
Bibliographic record
Abstract
ABSTRACT Printed Circuit Boards (PCBs) are increasingly being considered as critical components of electrical and electronic subsystems. Signal integrity is a critical factor on electrical and electronic subsystems operating at higher logic speeds. Transmission lines on PCBs directly affect the signal integrity. Impedance and propagation delay are the two most important characteristics of transmission lines. A good understanding and control of these two characteristics is a must. An accurate prediction model is imperative to calculate the impedance of a transmission line prior to design. This practice, which could shorten design cycle time, is essential particularly to original equipment manufacturers (OEMs). It is also essential for the OEMs to characterize the supply base to manufacture controlled impedance PCBs. In this research, three 2-dimensional impedance prediction tools were compared. A 14-layer FR-4 PCB was designed as a test vehicle with different transmission line configurations and structures. A stack-up was designed with dielectric materials of varying thickness and glass to resin ratio. The test vehicle design addressed issues relating to single-ended, edge-coupled differential and coplanar impedance. Propagation delay was measured to determine the effective dielectric constant of FR4 on finished boards. Statistical analysis was performed to understand the impact of design and process parameters on impedance and propagation delay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".