  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.
