Modeling of Differential Stripline in the Presence of Oblique Crossing Traces
Bibliographic record
Abstract
Due to continually increasing operating frequencies previously neglected effects of interconnects such as delay, crosstalk and attenuation are becoming major bottlenecks in high-speed designs.Effect of crossing trace angle on signal integrity of differential edge-coupled stripline is studied in this thesis.The closed-form solutions exist for simple TEM geometries such as symmetric or asymmetric differential striplines.In the literature analytical modeling for the case of 90 • trace crossings is available.However, for more complicated structures, such as differential stripline with crossing trace at oblique angles, computationally expensive 3D numerical methods are often employed.In this thesis, an efficient modeling and analysis method is presented for high-speed interconnects represented by differential asymmetrical transmission lines with oblique crossings.The method is based on dividing the structure into several sections, then using an appropriate technique for estimating the capacitance matrix of each section and subsequently evaluating the impedance matrix using the chain parameters.The thesis also investigates the implication of different angles for crossing traces on signal integrity metrics. 1 List of Tables 5.1 Comparison of Capacitance Matrix Element C 11 . . . . . . . .5.2 Comparison of Capacitance Matrix Element C 22 . . . . . . . .5.3 Comparison of Capacitance Matrix Element C 33 . . . . . . . .5.4 Comparison of Capacitance Matrix Element C 13 . . . . . . . .5.5 Comparison of Capacitance Matrix Element C 23 . . . . . . . .5.6 Comparison of Capacitance Matrix Element C 12 . . . . . . . .5.7 Comparison of Differential Impedance Z d . . . . . . . . . . . .5.8 Comparison of Common-Mode Impedance Z c . . . . . . . . . .5.9 Cross-section Dimensions (in mils) for the Experiments 5-8 . .5.10 Eye Diagram Measurements . . . . . . . . . . . .
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".