Bibliographic record
Abstract
Photolithographic defects during manufacture cannot only result in significant yield loss in digital integrated circuits, but are also deemed as an important factor in evaluating the quality of analog layouts. In this paper, we propose a graph-based lithography-aware analog layout retargeting methodology. We build up our fault model based on a classical defect size distribution function, geometrical critical area analysis, and probability of failure (POF). The objective of our algorithm is to minimize POF by intelligent redundant space allocation scheme during layout compaction. The optimizations handle the whole analog layout area by global wire widening, intradevice wire shifting (WS), and interdevice WS, which are achieved by updating the constraint-graph representation of the layout. Moreover, we propose an extra space allocation approach that can further reduce POF by an inconsiderably small chip-area compromise. The yield improvement and superior effectiveness of our algorithm are exhibited by retargeting operational amplifiers and being compared with a traditional linear programming-based layout compaction method and a well-known even wire distribution scheme.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".