用少样本学习预测模拟电路寄生电容，提升精度并支持零样本迁移。
Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction
- 构建异质图表示电路网表，通过小跳采样生成子图并用混合图Transformer学习嵌入。
- 耦合存在预测准确率提升至少20%，电容估计平均绝对误差降低0.067以上。
- 支持零样本迁移，适合缺乏数据的模拟电路设计场景。
图表示学习能有效提取模拟/混合信号（AMS）电路等图结构数据的特征，但深度学习模型在AMS设计中的训练受限于集成电路设计数据的稀缺性。本文提出CircuitGPS，一种用于AMS电路寄生效应预测的少样本学习方法。电路网表被建模为异质图，耦合电容作为边连接。CircuitGPS在链接预测任务上预训练，再在边回归任务上微调。方法采用小跳采样技术，将链接或节点转化为子图，使用混合图Transformer学习子图嵌入，并引入低成本位置编码以捕捉子图的位置与结构信息。实验表明，CircuitGPS在耦合存在预测上的准确率较现有方法至少提升20%，电容估计的平均绝对误差（MAE）降低至少0.067。该方法具备强可扩展性，可通过零样本学习直接应用于多样化的AMS电路设计。消融实验证明了图模型在表示学习中的有效性。
原文摘要 · Abstract (English)
Graph representation learning is a powerful method to extract features from graph-structured data, such as analog/mixed-signal (AMS) circuits. However, training deep learning models for AMS designs is severely limited by the scarcity of integrated circuit design data. In this work, we present CircuitGPS, a few-shot learning method for parasitic effect prediction in AMS circuits. The circuit netlist is represented as a heterogeneous graph, with the coupling capacitance modeled as a link. CircuitGPS is pre-trained on link prediction and fine-tuned on edge regression. The proposed method starts with a small-hop sampling technique that converts a link or a node into a subgraph. Then, the subgraph embeddings are learned with a hybrid graph Transformer. Additionally, CircuitGPS integrates a low-cost positional encoding that summarizes the positional and structural information of the sampled subgraph. CircuitGPS improves the accuracy of coupling existence by at least 20\% and reduces the MAE of capacitance estimation by at least 0.067 compared to existing methods. Our method demonstrates strong inherent scalability, enabling direct application to diverse AMS circuit designs through zero-shot learning. Furthermore, the ablation studies provide valuable insights into graph models for representation learning.
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