Novel Partitioning-Based Approach for Electromigration Assessment With Neural Networks
Bibliographic record
Abstract
Due to continuing technology scaling, electromigration (EM) remains a prominent reliability concern in integrated circuit design. Traditional empirical methods often result in over-design in very large scale integration (VLSI) due to model inaccuracy. Recently, researchers have focused on analyzing EM susceptibility by tracking hydrostatic stress evolution in metal lines, governed by computationally expensive partial differential equations (PDEs). In this paper, we propose a partitioning-based approach using neural networks to efficiently forecast the stress evolution along interconnect trees during the void nucleation and growth phases. This approach begins by decomposing the interconnect tree into subcomponents, providing computationally efficient analytical solutions for predicting stress evolution within each subtree. Subsequently, we employ a lightweight neural network to reassemble these components with their corresponding solutions to the original structure, ensuring accurate stress prediction. This divide-and-conquer strategy can accommodate various tree structures, with offshoots at arbitrary junctions, and holds substantial promise for using NN-based methods to solve mesh-free stress evolution on much larger interconnect trees than previously possible, with reduced computational overhead and heightened accuracy. The proposed approach eliminates the need for time discretization and grid meshing typically required in numerical methods. Numerical results confirm its advantages in accuracy and computational efficiency.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".