Bibliographic record
Abstract
As integrated circuit (IC) feature sizes scaled below a quarter of a micron, thereby defining the deep submicron (DSM) era, there began a gradual shift in the impact on performance due to the metal interconnections among the active circuit components. Once viewed as merely parasitics in terms of their relevance to the overall circuit behavior, the interconnect can now have a dominant impact on the IC area and performance. Beginning in the late 1980's there was significant research toward better modeling and characterization of the resistance, capacitance and ultimately the inductance of on-chip interconnect. IC Interconnect Analysis covers the state-of-the-art methods for modeling and analyzing IC interconnect based on the past fifteen years of research. This is done at a level suitable for most practitioners who work in the semiconductor and electronic design automation fields, but also includes significant depth for the research professionals who will ultimately extend this work into other areas and applications. IC Interconnect Analysis begins with an in-depth coverage of delay metrics, including the ubiquitous Elmore delay and its many variations. This is followed by an outline of moment matching methods, calculating moments efficiently, and Krylov subspace methods for model order reduction. The final two chapters describe how to interface these reduced-order models to circuit simulators and gate-level timing analyzers respectively. IC Interconnect Analysis is written for CAD tool developers, IC designers and graduate students
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.039 |
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".