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

IR drop analysis is a critical step in physical chip design, ensuring the power integrity of on-chip power delivery networks. As transistor density scales, the computational cost of IR drop analysis grows substantially, motivating scalable ML-based alternatives. Existing ML approaches formulate IR drop analysis as a spatial regression problem using power map representations, which fail to jointly model the geometric and topological factors that govern voltage drop distribution across the layout. We propose GIF (Generative IR drop Framework), to our knowledge the first framework to reformulate IR drop analysis as a conditional image generation problem. GIF conditions a diffusion model on two complementary inputs: a 34-channel geometry-aware feature image capturing local power consumption, cell density, and PDN resistance factors; and graph tokens encoding logical connectivity from the gate-level netlist, injected via gated cross-attention. This allows GIF to jointly capture local geometric variation and long-range structural dependencies that regression-based approaches do not model. On CircuitNet-N28 (28 nm), GIF achieves 0.791 SSIM and 0.9406 Pearson correlation. As a generative model, GIF produces calibrated uncertainty estimates as a natural byproduct of sampling. GIF also reports IR drop results on CircuitNet-N14 (14 nm), achieving 0.9106 Pearson and 0.8284 Spearman correlation across diverse design styles and establishing baselines for future work at this technology node.