DiffPattern: Layout Pattern Generation via Discrete Diffusion

• URL: http://arxiv.org/abs/2303.13060v1
• Date: Thu, 23 Mar 2023 06:16:14 GMT
• Title: DiffPattern: Layout Pattern Generation via Discrete Diffusion
• Authors: Zixiao Wang, Yunheng Shen, Wenqian Zhao, Yang Bai, Guojin Chen, Farzan Farnia, Bei Yu
• Abstract summary: We propose toolDiffPattern to generate reliable layout patterns. Our experiments on several benchmark settings show that toolDiffPattern significantly outperforms existing baselines.
• Score: 16.148506119712735
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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