An Open-Source AMS Circuit Optimization Framework Based on Reinforcement Learning—From Specifications to Layouts
Bibliographic record
Abstract
This paper presents a fully open-sourced AMS integrated circuit optimization framework based on reinforcement learning (RL). Specifically, given a certain circuit topology and target specifications, this framework optimizes the circuit in both schematic and post-layout phases. We propose using the heterogeneous graph neural network as the function approximator for RL. Optimization results suggest that it can achieve higher reward values with fewer iterations than the homogeneous graph neural networks. We demonstrate the applications of transfer learning (TL) in optimizing circuits in a different technology node. Furthermore, we show that by transferring the knowledge of schematic-level optimization, the trained RL agent can optimize the post-layout performance more efficiently than optimizing post-layout performance from scratch. To showcase the workflow of our approach, we extended our prior work to optimize latched comparators in the SKY130 and GF180MCU processes. Simulation results demonstrate that our framework can satisfy various target specifications and generate LVS/DRC clean circuit layouts.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".