Reinforcement-Learning-based Mixed-Signal IC Placement for Fogging Effect Control
Bibliographic record
Abstract
As the technology node shrinks to the nanometer regime, the demand for new lithography methods with high resolution and low cost is increasing. Electron beam lithography (EBL) is one of the promising next-generation lithography (NGL) technologies that can tackle both challenges compared to the traditional lithography methods. Fogging effect, a culprit of pattern distortion in layout, is one of the major challenges that prevent industry from adopting EBL in technologies below 22nm. This paper proposes a reinforcement-learning (RL) placement method that trains a neural network as an agent to effectively control fogging effect. To speed up our method, we benefit from the following innovations: using topological floorplan representation for the layouts during placement, and deploying a RL trained agent that can intelligently take actions. The experimental results show that our proposed placer is able to more effectively and efficiently reduce the fogging effect variation in the analog circuits in comparison with the conventional simulated-annealing-based placement method.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".