Fogging-Effect-Aware Mixed-Signal IC Placement with Reinforcement Learning
Bibliographic record
Abstract
Electron beam lithography (EBL) has been consolidated as one of the most common techniques for patterning at the nanoscale, especially below 22nm dimensions, thanks to its cost advantage over extreme ultraviolet lithography (EUL). Fogging effect, which always leads to pattern distortion in layout and in turn causes performance degradation, has been considered as a significant concern for wider adoption of EBL. In this work, we propose a reinforcement learning (RL) placement method that applies deep Q-learning to train a neural network as an agent. Different from the previous RL-based placement works, our proposed method uses a topological representation scheme that can advantageously render smaller search space in comparison to the currently popular absolute-coordinates-based representation (e.g., the state-of-the-art analytical placement method). To more effectively tackle mixed-signal ICs, our method focuses on the sensitive analog devices, which are better protected from potential variations due to fogging effects of other digital/analog portions. The experimental results show that our proposed placer is able to efficiently decrease the fogging effect variation among sensitive transistors in the analog portion up to 92%, while it is 13 times faster than the analytical RL-based placement
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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) | 0.001 | 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 | 0.001 | 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".