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

TL;DR

DeepRWCap introduces a neural-guided random walk method for capacitance extraction in IC design, significantly improving accuracy and speed over traditional stochastic methods by leveraging advanced neural architectures trained on diverse dielectric configurations.

Contribution

It presents a novel neural network architecture that guides random walk-based capacitance calculations, enhancing efficiency and accuracy in complex semiconductor structures.

Findings

Achieves 1.24% mean relative error on benchmark tests.

Provides 23% average speedup over Microwalk.

Reaches 49% acceleration on complex, long-runtime designs.

Abstract

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%.