Design and Optimization of Low-Dropout Voltage Regulator Using Relational Graph Neural Network and Reinforcement Learning in Open-Source SKY130 Process
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Bibliographic record
Abstract
Design automation and optimization for analog integrated circuits (ICs) are challenging, especially for transistor sizing. Given certain design specifications and circuit topology, circuit designers need to size various components to achieve the desired performance, possibly involving many optimization iterations. Recently, reinforcement learning (RL) has been applied to optimize analog circuits. The trained RL agents can achieve very high sample efficiency over evolutionary-based algorithms. By using the ability of transfer learning, the trained agent can be applied to optimize the same circuit across different technology nodes and even the circuits with different topologies. However, a significant bottleneck in applying machine learning (ML) techniques to analog IC design is the non-disclosure agreement (NDA) of the process development kit (PDK), which makes reproducibility of the prior art a big challenge. This work presents an RL framework that leverages the open-source SKY130 PDK to address the limitation above. We apply a novel heterogeneous graph neural network (GNN) called relational graph convolutional network (RGCN) as the function approximator of RL to capture more topological information about a circuit. As a proof-of-concept, low-dropout voltage regulators (LDO) are optimized by our proposed RL circuit optimizer framework to show its feasibility, achieving promising results.
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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 it