标题： 基于图对比学习和标签再平衡的 AMS 电路可迁移寄生估计 显示英文标题

标题： Transferable Parasitic Estimation via Graph Contrastive Learning and Label Rebalancing in AMS Circuits

摘要： 图表示学习在模拟-混合信号（AMS）电路中对于各种下游任务至关重要，例如寄生参数估计。 然而，设计数据的稀缺性、标签的不平衡分布以及电路实现的固有多样性给学习鲁棒且可迁移的电路表示带来了重大挑战。 为解决这些限制，我们提出了CircuitGCL，一种新颖的图对比学习框架，该框架结合了表示扩散和标签再平衡，以增强跨异构电路图的可迁移性。 CircuitGCL采用自监督策略，通过超球面表示扩散学习拓扑不变的节点嵌入，消除了对大规模数据的依赖。 同时，引入了平衡均方误差（BMSE）和平衡softmax交叉熵（BSCE）损失，以缓解电路之间的标签分布差异，从而实现鲁棒且可迁移的寄生参数估计。 在TSMC 28nm AMS设计中的寄生电容估计（边级任务）和地电容分类（节点级任务）上进行评估，CircuitGCL优于所有最先进的（SOTA）方法，边回归的$R^2$改进为$33.64\% \sim 44.20\%$，节点分类的F1分数提升了$0.9\times \sim 2.1\times$。 我们的代码可在https://github.com/ShenShan123/CircuitGCL获取。 显示英文摘要

摘要： Graph representation learning on Analog-Mixed Signal (AMS) circuits is crucial for various downstream tasks, e.g., parasitic estimation. However, the scarcity of design data, the unbalanced distribution of labels, and the inherent diversity of circuit implementations pose significant challenges to learning robust and transferable circuit representations. To address these limitations, we propose CircuitGCL, a novel graph contrastive learning framework that integrates representation scattering and label rebalancing to enhance transferability across heterogeneous circuit graphs. CircuitGCL employs a self-supervised strategy to learn topology-invariant node embeddings through hyperspherical representation scattering, eliminating dependency on large-scale data. Simultaneously, balanced mean squared error (BMSE) and balanced softmax cross-entropy (BSCE) losses are introduced to mitigate label distribution disparities between circuits, enabling robust and transferable parasitic estimation. Evaluated on parasitic capacitance estimation (edge-level task) and ground capacitance classification (node-level task) across TSMC 28nm AMS designs, CircuitGCL outperforms all state-of-the-art (SOTA) methods, with the $R^2$ improvement of $33.64\% \sim 44.20\%$ for edge regression and F1-score gain of $0.9\times \sim 2.1\times$ for node classification. Our code is available at https://github.com/ShenShan123/CircuitGCL.