3 papers
cs.LG2026
ParasGB: A Graph Benchmark Suite for Parasitic Estimation on AMS Circuits
Jiajun Zou, Jiawei Liu, Ao Liu +8
As chip manufacturing processes advance to deep submicron nodes, parasitic interconnect effects increasingly dominate the performance of analog and mixed-signal (AMS) circuits and…
cs.CV2026
R2G: A Multi-View Circuit Graph Benchmark Suite from RTL to GDSII
Zewei Zhou, Jiajun Zou, Jiajia Zhang +8
Graph neural networks (GNNs) are increasingly applied to physical design tasks such as congestion prediction and wirelength estimation, yet progress is hindered by inconsistent cir…
cs.LG2025
Transferable Parasitic Estimation via Graph Contrastive Learning and Label Rebalancing in AMS Circuits
Shan Shen, Shenglu Hua, Jiajun Zou +4
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 u…