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：深度生成模型在布局模式生成的现有文献中占据主导地位。然而，将合法性的保证交给一个不可解释的神经网络，在若干应用中可能存在问题。本文提出工具DiffPattern，用于生成可靠的布局模式。DiffPattern通过一种计算高效的无损布局模式表示，基于离散扩散模型引入了一种新颖的多样化拓扑生成方法。随后，利用白盒模式评估，根据所需设计规则生成合法模式。我们在多个基准测试上的实验表明，DiffPattern显著优于现有基线方法，并且能够合成可靠的布局模式。