Reinforcement-Learning-Based Foggy-Aware Optimal Placement Method for Analog and MixedSignal Circuits
Bibliographic record
Abstract
Despite advancement in artificial intelligence (AI) and subsequent successful applications in a vast variety of areas, addressing progressive need in electronic design automation for high performance and fine precision remains a significant challenge. Optimal layout placement design, a notably time-consuming task, often poses an issue to the conventional design process with an aim of maintaining high circuit performance. To mitigate this problem, in this paper we propose an AI-based optimization method to automate this process with better accuracy. We utilize a reinforcement learning (RL) method, advantage actor critic (A2C), to implement a full automation procedure for analog circuit layout design. A topological representation is employed to decrease the size of states in the optimization process. In addition, we have considered the foggy effect caused by electron-beam lithography (EBL) technology. Our simulation results demonstrate the remarkable efficacy of our approach, which can achieve 44 times smaller foggy effect variation and reduce the run time by 42 times in comparison with the analytical and another RL-based (DQN) method, without compromising the wire length and chip area minimization.
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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 | 0.000 | 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 | 0.000 | 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".