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

• URL: http://arxiv.org/abs/2607.16674v1
• Date: Sat, 18 Jul 2026 07:10:42 GMT
• Title: CLDRoute: Conditional Latent Diffusion for Routability Map Generation in Physical Design
• Abstract summary: Prior learning-based methods treat routability estimation as deterministic prediction, mapping placement-stage features to a single congestion or DRC outcome.<n>We formulate routability estimation as a conditional generation problem, where both routing congestion and DRC violations are modeled as spatially structured routability fields.<n>Our framework provides a more practical view of routability at placement by generating both the expected outcome and its uncertainty.
• Score: 20.750852836136012
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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