A vectorless framework for power grid electromigration checking
Bibliographic record
Abstract
Electromigration (EM) in the on-die metal lines has re-emerged as a significant concern in modern VLSI circuits. The higher levels of temperature on die and the very large number of metal lines, coupled with the conservatism inherent in traditional EM checking strategies, have led to a situation where trying to guarantee EM reliability often leads to unacceptably conservative designs that may not meet the area or performance specs. Due to unidirectional currents, this problem is most significant in the power and ground grids. Thus, this work is aimed at reducing the pessimism in EM prediction for power/ground grids. There are two sources for the high pessimism: 1) the use of the traditional series model for EM checking and 2) pessimistic assumptions about the chip workload and the corresponding supply currents. To address this problem, we propose a framework for EM checking that allows users to specify conditions-of-use type constraints that help capture realistic chip workload and which includes the use of a novel mesh model for EM prediction in the grid, instead of the traditional series model.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".