CLDRoute: Conditional Latent Diffusion for Routability Map Generation in Physical Design

2026-07-18

AI Summary

This work addresses the limitations of traditional routability estimation methods, which treat the problem as deterministic prediction and fail to capture the spatial uncertainty inherent in routing congestion and DRC violations. To overcome this, the paper introduces a diffusion model—applied for the first time to this task—and proposes a conditional latent diffusion framework. By incorporating a physics-aware conditioning mechanism and a task-specific latent representation, the model jointly captures the spatial structures of both congestion and DRC violation maps while enabling sample-based uncertainty quantification. Evaluated on CircuitNet 2.0 (N28), the approach achieves an SSIM of 0.9678 (MAE=0.0028) for DRC violation generation and an SSIM of 0.9031 (MAE=0.0286) for congestion generation, significantly outperforming existing methods.

Abstract

Accurate routability estimation during physical design is important for reducing costly post-routing iterations. Prior learning-based methods treat this task as deterministic prediction, mapping placement-stage features to a single congestion or DRC outcome. We instead formulate routability estimation as a conditional generation problem, where both routing congestion and DRC violations are modeled as spatially structured routability fields. Our framework, Conditional Latent Diffusion for Routeability estimation (CLDRoute), uses physics-aware conditioning and task-specific latent modeling to handle the different characteristics of congestion and DRC maps. This allows our method to supports sample-based inference, producing both a mean prediction and a spatial uncertainty estimate for the same input design. On CircuitNet 2.0 (N28), our method achieves, for DRC violation generation, an SSIM of 0.9678, an MAE of 0.0028, and a TopK@1% of 0.3494; for congestion generation, it achieves an SSIM of 0.9031, an MAE of 0.0286, and an NZ-Pearson of 0.3692. Overall, our framework provides a more practical view of routability at placement by generating both the expected outcome and its uncertainty.

Problem

• routability estimation
• physical design
• congestion prediction
• DRC violation
• uncertainty quantification

Innovation

• Conditional Latent Diffusion
• Routability Estimation
• Uncertainty Quantification
• Physical Design
• Generative Modeling