发表机构
University of Connecticut; Tufts University; University of Minnesota Twin Cities（康涅狄格大学; 塔夫茨大学; 明尼苏达大学双城分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对物理设计中可布线性估计问题，提出CLDRoute框架，将其作为条件生成问题，用物理感知条件和特定任务潜变量建模，支持样本推理，在CircuitNet 2.0上取得较好实验结果，能生成预期结果及其不确定性。
AI 中文摘要
物理设计期间准确的可布线性估计对于减少昂贵的布线后迭代很重要。先前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个拥塞或DRC结果。我们将可布线性估计公式化为条件生成问题，将路由拥塞和DRC违规都建模为空间结构化的可布线性字段。我们的框架CLDRoute使用物理感知条件和特定任务的潜变量建模来处理拥塞和DRC映射的不同特征。这使得我们的方法支持基于样本的推理，为同一输入设计生成均值预测和空间不确定性估计。在CircuitNet 2.0（N28）上，我们的方法在DRC违规生成方面，SSIM为0.9678，MAE为0.0028以及TopK@1%为0.3494；对于拥塞生成，SSIM为0.9031，MAE为0.0286以及NZ-Pearson为0.3692。总体而言，我们的框架通过生成预期结果及其不确定性，在布局时提供了更实际的可布线性视图。
英文摘要
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.
