Bibliographic record
Abstract
With the continually increasing operating frequencies and decreasing signal transition times, high-speed effects of interconnect structures are becoming increasingly influential in determining the performance of modern electronic designs.High-Speed effects can be many, such as delay, attenuation, cross talk and ground bounce, etc., which if not addressed properly can lead to failed designs.Hence accurate modeling and simulation of high-speed modules becomes a necessity in today's designs.The modules of importance at higher frequencies span the diverse design hierarchy such as chip, package and system level designs.These can be multiconductor transmission lines, package pins or complex electromagnetic modules.At higher frequencies they are often characterized by electromagnetic tools yielding tabulated scattering parameter based multiport descriptions or characterized directly using multiport measurements.However, integrating such tabulated data models in regular SPICE like tool environment is a challenge.This is addressed in the literature by using direct least squares approximation to synthesize a rational function model, however, it often encountered the problem of ill-conditioning.This was handled by the Vector Fitting (VF) technique which has gained popularity in the recent years, not only in electronic designs but also in other areas where system identification using multiport data is warranted.However, VF technique suffers in the presence of large number of ports or poles and becomes compu- would like to thank my thesis advisor Professor Ram Achar of the Electronics
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".