Foggy- and Proximity-Aware Reinforcement Learning for Analog and Mixed-Signal Circuit Placement
Bibliographic record
Abstract
As artificial intelligence (AI) continues to transform various fields, leveraging these advancements to overcome the challenges for achieving high performance and fine precision in electronic design automation (EDA) becomes increasingly crucial. Analog circuit layout placement, a complex and time-intensive task, often suffers from inefficiencies in traditional design approaches due to intensive optimization needed. In this paper, we introduce an advanced reinforcement learning (RL) method to automate the analog circuit placement process with improved accuracy and scalability. Our approach leverages the proximal policy optimization (PPO) framework to optimize layout placement objectives such as area, wire length, and layout effects during fabrication, including foggy and proximity effects. By utilizing a topological representation, we further reduce the complexity of the state space, enhancing optimization efficiency. Our experimental results demonstrate that the proposed method reduces foggy and proximity effect variations by up to 98.9% and 95.8% respectively and decreases wire length up to 34.6% compared to other RL-based and popular analytical methods, while maintaining competitive performance in terms of area.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".