发表机构
Nanjing University of Science and Technology; The Chinese University of Hong Kong; Tsinghua University; Nanjing University; National Center of Technology Innovation for EDA; Southeast University（南京理工大学; 香港中文大学; 清华大学; 南京大学; 国家集成电路设计自动化技术创新中心; 东南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对 AMS 电路寄生估计问题，引入 ParasGB 开源基准套件，提供大规模异构 RC 网络与统一评估协议，用标准化流程对 GNN 架构基准测试，揭示相关挑战，为电路图学习和寄生感知模型开发提供可重复研究平台。
AI 中文摘要
随着芯片制造工艺进入深亚微米节点，寄生互连效应在模拟和混合信号（AMS）电路性能中愈发关键，常导致昂贵的布局迭代。因此，在完整物理实现前进行寄生电容和电阻的早期估计对寄生感知设计探索很重要。然而，基于 GNN 的寄生建模因缺乏支持可重复评估的公共、高保真 RC 基准而受阻。为填补这一空白，我们引入了 ParasGB，首个用于电路图预布局寄生参数预测的开源基准套件。ParasGB 提供了大规模、异构的 RC 网络，以及统一评估协议。在此框架内，我们用标准化训练流程对多种 GNN 架构进行基准测试，揭示了极端标签不平衡、长尾寄生分布和强结构异质性等挑战。通过建立早期寄生预测的物理基础和标准化基准，ParasGB 为电路图学习和寄生感知模型开发的可重复研究提供了开放平台。所有数据集、预处理脚本和配置都可在我们的代码库中公开获取。
英文摘要
As chip manufacturing processes advance to deep submicron nodes, parasitic interconnect effects increasingly dominate the performance of analog and mixed-signal (AMS) circuits and often lead to costly layout iterations. This makes early-stage estimation of parasitic capacitance and resistance important for parasitic-aware design exploration before full physical implementation. However, progress on GNN-based parasitic modeling has been hindered by the lack of public, high-fidelity RC benchmarks that support reproducible evaluation. To address this gap, we introduce ParasGB, the first open-source benchmark suite for pre-layout parasitic parameter prediction on circuit graphs. ParasGB provides large-scale, heterogeneous RC networks extracted with commercial EDA tools from tape-out-proven designs, together with a unified evaluation protocol covering node-level ground capacitance, edge-level resistance, and edge-level coupling capacitance. Within this framework, we benchmark diverse GNN architectures using a standardized training pipeline and expose challenges such as extreme label imbalance, long-tailed parasitic distributions, and strong structural heterogeneity. By establishing a physically grounded and standardized benchmark for early-stage parasitic prediction, ParasGB provides an open platform for reproducible research on circuit graph learning and parasitic-aware model development. All datasets, preprocessing scripts, and configurations are publicly available in our code repository https://github.com/ShenShan123/ParasGB.git.
CommentsPublished at ICCAD2026. Full appendix version
