DiffPattern: Layout Pattern Generation via Discrete Diffusion

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 DiffPattern to generate reliable layout patterns. DiffPattern int...

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Vydané v:2023 60th ACM/IEEE Design Automation Conference (DAC) s. 1 - 6
Hlavní autori: Wang, Zixiao, Shen, Yunheng, Zhao, Wenqian, Bai, Yang, Chen, Guojin, Farnia, Farzan, Yu, Bei
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 09.07.2023
Predmet:
Computational modeling
Design automation
Law
Layout
Network topology
Neural networks
Reliability theory
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Popis
Shrnutí: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 DiffPattern to generate reliable layout patterns. 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 DiffPattern significantly outperforms existing baselines and is capable of synthesizing reliable layout patterns.
DOI:10.1109/DAC56929.2023.10248009