Lithography-Aware Analog Layout Retargeting
Why this work is in the frame
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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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 it