LDE-aware Analog Layout Migration with OPC-inclusive Routing
Bibliographic record
Abstract
Performance degradation in analog circuits due to layout dependent effects (LDEs) has become increasingly challenging in advanced technologies. To address this issue, LDEs have to be seriously considered as performance constraints in the physical design process. In this article, we have proposed an innovative LDE-aware retargeting methodology for analog layout migration from old technologies to new ones with LDEs optimized for performance preservation. The LDE constraints, which are first identified with the aid of a specialized sensitivity analysis scheme, are satisfied during the layout migration process. Moreover, optical proximity correction (OPC), as one of the most popular resolution enhancement techniques for subwavelength lithography in modern nanometer technology manufacturing, is also included in this study. We have developed an OPC-inclusive ILP-based analog router to route electrical nets for improving image fidelity of the final layout while the routability and other analog constraints are respected in the meantime. The experimental results show our proposed layout migration methodology along with the routing scheme is able to retarget analog layouts with better circuit performance and finer image quality compared to the previous works.
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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".