research-article

LayouTransformer: Generating Layout Patterns with Transformer via Sequential Pattern Modeling

Authors:

Jianzhuang Liu,

Chunjing XuAuthors Info & Claims

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

Article No.: 124, Pages 1 - 9

https://doi.org/10.1145/3508352.3549350

Published: 22 December 2022 Publication History

Abstract

Generating legal and diverse layout patterns to establish large pattern libraries is fundamental for many lithography design applications. Existing pattern generation models typically regard the pattern generation problem as image generation of layout maps and learn to model the patterns via capturing pixel-level coherence, which is insufficient to achieve polygon-level modeling, e.g., shape and layout of patterns, thus leading to poor generation quality. In this paper, we regard the pattern generation problem as an unsupervised sequence generation problem, in order to learn the pattern design rules by explicitly modeling the shapes of polygons and the layouts among polygons. Specifically, we first propose a sequential pattern representation scheme that fully describes the geometric information of polygons by encoding the 2D layout patterns as sequences of tokens, i.e., vertexes and edges. Then we train a sequential generative model to capture the long-term dependency among tokens and thus learn the design rules from training examples. To generate a new pattern in sequence, each token is generated conditioned on the previously generated tokens that are from the same polygon or different polygons in the same layout map. Our framework, termed LayouTransformer, is based on the Transformer architecture due to its remarkable ability in sequence modeling. Comprehensive experiments show that our LayouTransformer not only generates a large amount of legal patterns but also maintains high generation diversity, demonstrating its superiority over existing pattern generative models.

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Cited By

Wang QZhang XWong MYoung E(2024)ControLayout: Conditional Diffusion for Style-Controllable and Violation-Fixable Layout Pattern GenerationProceedings of the Great Lakes Symposium on VLSI 202410.1145/3649476.3658770(511-515)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3649476.3658770
Chen YWu YWang JWu THe XYu JGeng HDe V(2024)LLM-HD: Layout Language Model for Hotspot Detection with GDS Semantic EncodingProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3658479(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3658479
Wang ZShen YYao XZhao WBai YFarnia FYu BDe V(2024)ChatPattern: Layout Pattern Customization via Natural LanguageProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3657361(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3657361
Show More Cited By

Index Terms

LayouTransformer: Generating Layout Patterns with Transformer via Sequential Pattern Modeling
1. Applied computing
  1. Arts and humanities
    1. Architecture (buildings)
      1. Computer-aided design
  2. Physical sciences and engineering
    1. Engineering
      1. Computer-aided design
2. Hardware
  1. Electronic design automation
    1. Physical design (EDA)
      1. Partitioning and floorplanning
      2. Physical synthesis
  2. Very large scale integration design
    1. VLSI design manufacturing considerations

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

October 2022

1467 pages

ISBN:9781450392174

DOI:10.1145/3508352

Conference Chair:
Tulika Mitra
National University of Singapore
,
Program Chairs:
Evangeline Young
The Chinese University of Hong Kong
,
Jinjun Xiong
University at Buffalo (UB)

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGDA: ACM Special Interest Group on Design Automation

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IEEE-EDS: Electronic Devices Society
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 December 2022

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ICCAD '22

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ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design

October 30 - November 3, 2022

California, San Diego

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Overall Acceptance Rate 457 of 1,762 submissions, 26%

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Cited By

Wang QZhang XWong MYoung E(2024)ControLayout: Conditional Diffusion for Style-Controllable and Violation-Fixable Layout Pattern GenerationProceedings of the Great Lakes Symposium on VLSI 202410.1145/3649476.3658770(511-515)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3649476.3658770
Chen YWu YWang JWu THe XYu JGeng HDe V(2024)LLM-HD: Layout Language Model for Hotspot Detection with GDS Semantic EncodingProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3658479(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3658479
Wang ZShen YYao XZhao WBai YFarnia FYu BDe V(2024)ChatPattern: Layout Pattern Customization via Natural LanguageProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3657361(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3657361
Zheng SMa YYu BWong MDe V(2024)EMOGen: Enhancing Mask Optimization via Pattern GenerationProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3655680(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3655680
Mahmoud AEl-Kharashi MSalama C(2024)Generating Synthetic Layout Test Patterns using Deep LearningJournal of Electronic Testing: Theory and Applications10.1007/s10836-024-06138-240:5(603-614)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1007/s10836-024-06138-2
Yu B(2023)Machine Learning in EDA: When and How2023 ACM/IEEE 5th Workshop on Machine Learning for CAD (MLCAD)10.1109/MLCAD58807.2023.10299822(1-6)Online publication date: 10-Sep-2023
https://doi.org/10.1109/MLCAD58807.2023.10299822
Wang ZShen YZhao WBai YChen GFarnia FYu B(2023)DiffPattern: Layout Pattern Generation via Discrete Diffusion2023 60th ACM/IEEE Design Automation Conference (DAC)10.1109/DAC56929.2023.10248009(1-6)Online publication date: 9-Jul-2023
https://doi.org/10.1109/DAC56929.2023.10248009
Li KYe W(2023)A generative adversarial active learning method for mechanical layout generationNeural Computing and Applications10.1007/s00521-023-08751-235:26(19315-19335)Online publication date: 26-Jun-2023
https://dl.acm.org/doi/10.1007/s00521-023-08751-2

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