# DeepRWCap: Neural-Guided Random-Walk Capacitance Solver for IC Design

arXiv: https://arxiv.org/abs/2511.06831 · HTML: https://mlsanity.com/paper/2511.06831/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Builds on (most cited first, 7 of 7)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution](https://mlsanity.com/paper/1807.03247/) (2018)
- [AnalogCoder: Analog Circuit Design via Training-Free Code Generation](https://mlsanity.com/paper/2405.14918/) (2024)
- [CubeNet: Equivariance to 3D Rotation and Translation](https://mlsanity.com/paper/1804.04458/) (2018)
- [Solving Poisson Equations using Neural Walk-on-Spheres](https://mlsanity.com/paper/2406.03494/) (2024)
- [PreRoutGNN for Timing Prediction with Order Preserving Partition: Global Circuit Pre-training, Local Delay Learning and Attentional Cell Modeling](https://mlsanity.com/paper/2403.00012/) (2024)
- [Efficient FRW Transitions via Stochastic Finite Differences for Handling Non-Stratified Dielectrics](https://mlsanity.com/paper/2507.09730/) (2025)

## Built on by (most cited first, 2 of 2)

- [Operator Learning Using Weak Supervision from Walk-on-Spheres](https://mlsanity.com/paper/2603.01193/) (2026)
- [Flash-CNNCap: Capacitance Extraction via Image Mapping](https://mlsanity.com/paper/2607.23877/) (2026)

## Abstract (arXiv)

Monte Carlo random walk methods are widely used in capacitance extraction for their mesh free formulation and inherent parallelism. However, modern semiconductor technologies with densely packed structures present significant challenges in unbiasedly sampling transition domains in walk steps with multiple high contrast dielectric materials. We present DeepRWCap, a machine learning guided random walk solver that predicts the transition quantities required to guide each step of the walk. These include Poisson kernels, gradient kernels, and the signs and magnitudes of weights. DeepRWCap employs a two stage neural architecture that decomposes structured outputs into face wise distributions and spatial kernels on cube faces. It uses 3D convolutional networks to capture volumetric dielectric interactions and 2D depthwise separable convolutions to model localized kernel behavior. The design incorporates grid based positional encodings and structural design choices informed by cube symmetries to reduce learning redundancy and improve generalization. Trained on 100000 procedurally generated dielectric configurations, DeepRWCap achieves a mean relative error of 1.24 +/- 0.53% when benchmarked against the commercial Raphael solver on the self capacitance estimation of 10 industrial designs spanning 12 to 55 nm nodes. Compared to the state of the art stochastic difference method Microwalk, DeepRWCap achieves an average speedup of 23%. On complex designs with runtimes over 10 seconds, it reaches an average acceleration of 49%.