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

2026-04-11

AI Summary

This work addresses the inefficiency of traditional EDA tools in IR drop analysis for high-density chips and the limitations of existing machine learning approaches, which struggle to capture both local and long-range dependencies while neglecting layout geometry and circuit topology. To overcome these challenges, the authors propose GIF, a novel framework that jointly models geometry-aware spatial images and logical circuit graphs for the first time. GIF employs a multimodal conditional diffusion mechanism to generate high-fidelity IR drop maps. Through image-graph fused feature extraction and conditional diffusion generation, the method achieves state-of-the-art performance on the CircuitNet-N28 dataset, yielding 0.78 SSIM, 0.95 Pearson correlation coefficient, 21.77 PSNR, and 0.026 NMAE—significantly outperforming existing solutions and establishing new benchmarks in both accuracy and physical consistency.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Computer Vision: Diffusion Models for Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

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.

Problem

• IR drop analysis
• chip layout
• geometrical information
• topological information
• power integrity

Innovation

• conditional diffusion
• multimodal fusion
• graph-image representation
• IR drop prediction
• generative modeling