Layout regularity metric as a fast indicator of high variability circuits
Bibliographic record
Abstract
Integrated circuits design faces increasing challenge as we scale down due to the increase of the effect of sensitivity to process variations. Layout regularity is one of the trending techniques suggested by design for manufacturability (DFM) to mitigate process variations effect. However, there is no study relating either lithography or electrical variations to layout regularity. In this paper, a novel method is presented to model electrical variations due to systematic lithographic variations. Then, geometrical-based layout regularity metric was derived; this metric can be used as a fast indicator of designs more susceptible to lithography and hence electrical variations. The validity of using the regularity metric to flag circuits that have high variability using the developed electrical variations model is shown. The metric results compared to the electrical variability model results show matching percentage that can reach 80%. Calculation of the metric takes only few minutes on 1 mm × 1 mm.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".