New Higher Order Method of Moments for Accurate Inductance Extraction in Transmission Lines of Complex Cross Sections
Bibliographic record
Abstract
A new higher order (HO) method of moments is proposed for high accuracy extraction of the resistance and inductance matrices in the multiconductor transmission lines (MTLs) of complex cross sections. The computational framework is based on the numerical solution of a surface-volume-surface electric field integral equation of magnetostatics. In order to achieve an exponentially efficient reduction in the error of the solution, HO geometrical representation of the conductor cross sections is accompanied with the discretization of the unknown field quantities on the conductor boundaries and cross sections with 1-D and 2-D HO polynomial basis functions, respectively. The methodology allows for extraction of the network parameters in broad ranges of frequencies for which resistive and inductive contributions to the impedance matrix vary within a wide dynamic range. Comparison of numerically computed currents to the currents obtained analytically for canonical transmission line configurations is performed. Solutions of the extraction problem for MTLs with complex cross sections are compared against the finite-element method solutions to demonstrate the efficiency of the proposed methodology.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".