Deep generative models dominate the existing literature in layout pattern generation. However, leaving the guarantee of legality to an inexplicable neural network could be problematic in several applications. In this paper, we propose \tool{DiffPattern} to generate reliable layout patterns. \tool{DiffPattern} introduces a novel diverse topology generation method via a discrete diffusion model with compute-efficiently lossless layout pattern representation. Then a white-box pattern assessment is utilized to generate legal patterns given desired design rules. Our experiments on several benchmark settings show that \tool{DiffPattern} significantly outperforms existing baselines and is capable of synthesizing reliable layout patterns.

翻译：深度生成模型主导了布局模式生成的现有文献。然而，在一些应用中，将合法性保证留给一个难以解释的神经网络可能存在问题。在本文中，我们提出了一种名为 \tool{DiffPattern} 的布局模式生成算法，以生成可靠的布局模式。 \tool{DiffPattern} 引入了一种新颖的离散扩散模型来生成多样化的拓扑结构，并采用高效布局模式表征来评估生成的合法性。我们在几种基准设置上的实验表明，\tool{DiffPattern} 显著优于现有基线，并能够合成可靠的布局模式。