Process-Variation-Aware Rule-Based Optical Proximity Correction for Analog Layout Migration
Bibliographic record
Abstract
Optical proximity correction (OPC) is invaluable to precise layout manufacturing in the advanced nanometer technologies. However, lack of research activity on OPC for analog integrated circuits has currently led to sole dependence on digital solutions. In this paper, we propose a rule-based OPC (RB-OPC) methodology with process variation (PV) consideration, which is integrated into an analog layout migration process. Based on the unique features of analog layouts, the accuracy limitation of RB-OPC is compensated by local wire widening and wire shifting operations during layout migration. Moreover, innovative PV-band shifting is deployed to preserve analog circuit performance against PV. According to our experimental results, the proposed analog layout migration with PV-aware RB-OPC can achieve much higher efficiency with an even lower mask complexity and an acceptable edge placement error compared to some well-known commercial and academic digital solutions. The circuit performance by using our proposed methodology is also better than that from alternative methods in the mismatch scenarios due to our special PV-band handling on transistor gates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".