CapBench: A Multi-PDK Dataset for Machine-Learning-Based Post-Layout Capacitance Extraction
We present CapBench, a fully reproducible, multi-PDK dataset for capacitance extraction. The dataset is derived from open-source designs, including single-core CPUs, systems-on-chip, and media accelerators. All designs are fully placed and routed using 14 independent OpenROAD flow runs spanning three technology nodes: ASAP7, NanGate45, and Sky130HD. From these layouts, we extract 61,855 3D windows across three size tiers to enable transfer learning and scalability studies. High-fidelity capacitance labels are generated using RWCap, a state-of-the-art random-walk solver, and validated against the industry-standard Raphael, achieving a mean absolute error of 0.64% for total capacitance. Eac...
| Authors | Hector R. Rodriguez, Jiechen Huang, Wenjian Yu |
|---|---|
| Primary category | cs.AR |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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