Electromigration- and Parasitic-Aware ILP-Based Analog Router
Bibliographic record
Abstract
Interconnect and via failures due to the electromigration (EM) effect have become increasingly challenging in the advanced technology as a result of shrinking feature size. The parasitic effect, which has to be normally represented as analog and radio frequency (RF) circuit constraints for effective suppression in practice, has also made the modern physical design more intractable. In this paper, we present a new integer-linear-programming-based EM-aware and sensitivity-aware analog/RF router, which can effectively address the EM constraints as well as interconnect parasitics and matching constraints along the routing paths. Interconnect parasitics can be evaluated in the sensitivity analysis so that an appropriate width can be adopted for each subnet in order to gain better circuit performance. Any mismatch can be also minimized between ideally matching nets whenever the primitive methods fail to find exactly matched routes for these sensitive nets. By using our proposed routing methodology, the EM constraints are taken into account to guarantee a final EM-compliant routing solution. The experimental results show that our proposed method is able to produce better circuit performance compared to the previous works while EM can be seriously addressed for analog/RF circuits.
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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".