Redundancy-aware Electromigration checking for mesh power grids
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electromigration (EM) is re-emerging as a significant problem in modern integrated circuits (IC). Especially in power grids, due to shrinking wire widths and increasing current densities, there is little or no margin left between the predicted EM stress and that allowed by the EM design rules. Statistical Electromigration Budgeting (SEB) estimates the reliability of the grid by considering it entirely as a series system. However, a power grid with its many parallel paths has much inherent redundancy. In this paper, we propose a new model to estimate the MTF and reliability of the power grid under the influence of EM, which accounts for these redundancies. We refer to this as the mesh model. To implement the mesh model, we also develop a framework to estimate the change in statistics of an interconnect as its effective-EM current varies. The proposed algorithm is quite fast and has an overall observed empirical complexity of 0(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.4</sup> ). The results indicate that the series model, which is currently used in the industry, gives a pessimistic estimate of power grid MTF and reliability by a factor of 3-4.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it