Abstract
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.
Abstract (translated)
Deep生成模型在布局模式生成现有文献中占据主导地位。然而，将合法性保障留给不可理解神经网络在一些应用中可能会有问题。在本文中，我们提出了 \tool{DiffPattern} 来生成可靠的布局模式。 \tool{DiffPattern} 通过一种离散扩散模型引入了一种新颖的多样化拓扑生成方法。然后，使用一个白盒模式评估方法，根据预期的设计规则生成合法模式。我们对多个基准设置的实验结果表明， \tool{DiffPattern} 显著优于现有基准模型，并能够合成可靠的布局模式。
URL
https://arxiv.org/abs/2303.13060
