research-article

DeePattern: Layout Pattern Generation with Transforming Convolutional Auto-Encoder

Authors:

Bei YuAuthors Info & Claims

DAC '19: Proceedings of the 56th Annual Design Automation Conference 2019

Article No.: 148, Pages 1 - 6

https://doi.org/10.1145/3316781.3317795

Published: 02 June 2019 Publication History

Abstract

VLSI layout patterns provide critic resources in various design for manufacturability researches, from early technology node development to back-end design and sign-off flows. However, a diverse layout pattern library is not always available due to long logic-to-chip design cycle, which slows down the technology node development procedure. To address this issue, in this paper, we explore the capability of generative machine learning models to synthesize layout patterns. A transforming convolutional auto-encoder is developed to learn vector-based instantiations of squish pattern topologies. We show our framework can capture simple design rules and contributes to enlarging the existing squish topology space under certain transformations. Geometry information of each squish topology is obtained from an associated linear system derived from design rule constraints. Experiments on 7nm EUV designs show that our framework can more effectively generate diverse pattern libraries with DRC-clean patterns compared to a state-of-the-art industrial layout pattern generator.

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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
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
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cover image ACM Conferences

DAC '19: Proceedings of the 56th Annual Design Automation Conference 2019

June 2019

1378 pages

ISBN:9781450367257

DOI:10.1145/3316781

Copyright © 2019 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
IEEE-CEDA

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New York, NY, United States

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Published: 02 June 2019

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DAC '19

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DAC '19: The 56th Annual Design Automation Conference 2019

June 2 - 6, 2019

NV, Las Vegas, USA

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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62nd ACM/IEEE Design Automation Conference

June 22 - 26, 2025

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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
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
Li KGao YLou S(2024)A domain knowledge-informed design space exploration methodology for mechanical layout designJournal of Engineering Design10.1080/09544828.2024.234782035:9(1125-1152)Online publication date: 4-May-2024
https://doi.org/10.1080/09544828.2024.2347820
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: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s10836-024-06138-2
Yang HRen HTakahashi A(2023)Enabling Scalable AI Computational Lithography with Physics-Inspired ModelsProceedings of the 28th Asia and South Pacific Design Automation Conference10.1145/3566097.3568361(715-720)Online publication date: 16-Jan-2023
https://dl.acm.org/doi/10.1145/3566097.3568361
Weisbuch FSchatz J(2023)Test pattern generation by optimization of the feature space signature38th European Mask and Lithography Conference (EMLC 2023)10.1117/12.2675601(20)Online publication date: 5-Oct-2023
https://doi.org/10.1117/12.2675601
Zhang QXu XZhang YLu WLi Y(2023)CompressKey—Near Lossless Layout Compression and Encryption Using Convolutional Auto-Encoder Model and Expansion-Reduction Pattern TechniquesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.319567642:4(1030-1043)Online publication date: Apr-2023
https://doi.org/10.1109/TCAD.2022.3195676
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
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