用扩散模型生成布线图的拥堵与设计规则违例，同时给出预测不确定性。
CLDRoute: Conditional Latent Diffusion for Routability Map Generation in Physical Design
- 将布线可实现性建模为条件生成任务，输出空间结构化的拥堵和违例图。
- 在CircuitNet 2.0上，违例生成SSIM达0.9678，拥堵生成MAE为0.0286。
- 支持样本推理，可同时输出均值预测与空间不确定性，适合芯片物理设计决策。
物理设计中的精确布线可实现性估计对减少昂贵的后期布线迭代至关重要。以往基于学习的方法将此任务视为确定性预测，将布局阶段特征映射到单一的拥塞或DRC结果。本文将其重新建模为条件生成问题，将布线拥塞与DRC违规建模为空间结构化的可实现性场。所提出的CLDRoute框架采用物理感知条件输入与任务特定隐变量建模，有效处理拥塞与DRC图的不同特性。该方法支持基于样本的推理，可对同一输入生成均值预测与空间不确定性估计。在CircuitNet 2.0（N28）数据集上，违例生成达到SSIM 0.9678、MAE 0.0028、TopK@1% 0.3494；拥塞生成达到SSIM 0.9031、MAE 0.0286、NZ-Pearson 0.3692。整体框架为布局阶段的可实现性提供了更实用的视图。
原文摘要 · Abstract (English)
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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