DiffPattern: Layout Pattern Generation via Discrete Diffusion
This addresses the need for legal layout pattern generation in applications like circuit design, though it appears incremental as it builds on existing diffusion models.
The paper tackles the problem of generating reliable layout patterns by proposing DiffPattern, which uses a discrete diffusion model with a lossless representation and white-box assessment to ensure legality, and it significantly outperforms existing baselines in experiments.
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.