GIF: A Conditional Multimodal Generative Framework for IR Drop Imaging in Chip Layouts

TL;DR

GIF is a novel generative framework that combines geometrical and topological information to produce high-quality IR drop images, improving accuracy over previous ML-based methods in chip layout analysis.

Contribution

The paper introduces GIF, a diffusion-based multimodal generative model that jointly models layout geometry and circuit topology for IR drop imaging.

Findings

GIF achieves higher SSIM and Pearson correlation than prior methods.

GIF produces IR drop images with 21.77 PSNR and 0.026 NMAE on CircuitNet-N28.

The framework effectively leverages geometric and topological features for accurate IR drop prediction.

Abstract

IR drop analysis is essential in physical chip design to ensure the power integrity of on-chip power delivery networks. Traditional Electronic Design Automation (EDA) tools have become slow and expensive as transistor density scales. Recent works have introduced machine learning (ML)-based methods that formulate IR drop analysis as an image prediction problem. These existing ML approaches fail to capture both local and long-range dependencies and ignore crucial geometrical and topological information from physical layouts and logical connectivity. To address these limitations, we propose GIF, a Generative IR drop Framework that uses both geometrical and topological information to generate IR drop images. GIF fuses image and graph features to guide a conditional diffusion process, producing high-quality IR drop images. For instance, On the CircuitNet-N28 dataset, GIF achieves 0.78 SSIM, 0.95 Pearson correlation, 21.77 PSNR, and 0.026 NMAE, outperforming prior methods. These results demonstrate that our framework, using diffusion based multimodal conditioning, reliably generates high quality IR drop images. This shows that IR drop analysis can effectively leverage recent advances in generative modeling when geometric layout features and logical circuit topology are jointly modeled. By combining geometry aware spatial features with logical graph representations, GIF enables IR drop analysis to benefit from recent advances in generative modeling for structured image generation.