PV-Aware Analog Sizing for Robust Analog Layout Retargeting with Optical Proximity Correction
Bibliographic record
Abstract
For analog integrated circuits (ICs) in nanometer technology nodes, process variation (PV) induced by lithography may not only cause serious wafer pattern distortion, but also result in device mismatch, which can readily ruin circuit performance. Although the conventional optical proximity correction (OPC) operations can effectively improve the wafer image fidelity, an analog circuit without robust device sizes is still highly vulnerable to such a mismatch effect. In this article, a PV-aware sizing-inclusive analog layout retargeting framework, which encloses an efficient hybrid OPC scheme for yield enhancement, is proposed. The device sizes are tuned during the layout retargeting process by using a deterministic circuit-sizing algorithm considering PV conditions. Our hybrid OPC method combines global rule-based OPC with local model-based OPC functions to boost the wafer image quality improvement but without degrading the computational efficiency. The experimental results show that our proposed framework can achieve the best wafer image quality and circuit performance preservation compared to any other alternative approaches.
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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 | 0.001 |
| 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".