# Flash-CNNCap: Capacitance Extraction via Image Mapping

arXiv: https://arxiv.org/abs/2607.23877 · HTML: https://mlsanity.com/paper/2607.23877/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 26 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [DeepRWCap: Neural-Guided Random-Walk Capacitance Solver for IC Design](https://mlsanity.com/paper/2511.06831/) (2025)
- [CapBench: A Multi-PDK Dataset for Machine-Learning-Based Post-Layout Capacitance Extraction](https://mlsanity.com/paper/2604.11202/) (2026)

## Abstract (arXiv)

We present Flash-CNNCap, a CNN-based capacitance extractor that reformulates full-matrix capacitance prediction as image-to-image regression over spatial contribution maps. Prior scalar CNN-based extractors require O(n2) forward passes to recover all pairwise capacitances in a window with n conductors. Flash-CNNCap replaces the scalar target with dense contribution maps: a total-capacitance model and a master-conditioned coupling model each predict a spatial map that is reduced to conductor-level values through mask aggregation, cutting full-matrix reconstruction to O(n) passes. The resulting totals and symmetrized pairwise couplings define the corresponding Maxwell-style capacitance matrix under the standard off-diagonal sign convention. The maps are learned from conductor-level labels without per-pixel supervision. An ablation study over 13 model configurations selects a U-Net that matches ResNet baselines on total capacitance (1.5-3.1% MARE) and achieves the strongest coupling accuracy (3.0-4.6% MARE) across all evaluated CapBench subsets, with a 17.5× full-matrix speedup on windows containing 134 conductors on average. A deployed pipeline reads Design Exchange Format (DEF) geometry and writes Standard Parasitic Exchange Format (SPEF) output, processing 1,024 windows in 51.23 seconds with a 4.4× speedup over OpenRCX on the same benchmark. Code and trained models are available at https://github.com/THU-numbda/flash-cnncap.