research-article

A2-ILT: GPU accelerated ILT with spatial attention mechanism

Authors:

Martin D. F. Wong,

Evangeline F. Y. YoungAuthors Info & Claims

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

Pages 967 - 972

https://doi.org/10.1145/3489517.3530579

Published: 23 August 2022 Publication History

Abstract

Inverse lithography technology (ILT) is one of the promising resolution enhancement techniques (RETs) in modern design-for-manufacturing closure, however, it suffers from huge computational overhead and unaffordable mask writing time. In this paper, we propose A2-ILT, a GPU-accelerated ILT framework with spatial attention mechanism. Based on the previous GPU-accelerated ILT flow, we significantly improve the ILT quality by introducing spatial attention map and on-the-fly mask rectilinearization, and strengthen the robustness by Reinforcement-Learning deployment. Experimental results show that, comparing to the state-of-the-art solutions, A2-ILT achieves 5.06% and 11.60% reduction in printing error and process variation band with a lower mask complexity and superior runtime performance.

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Cited By

Liang XYang HLiu KYu BMa YDe V(2024)CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement LearningProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3656254(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3656254
Zhang XZheng SChen GZhu BXu HYu BDe V(2024)Fracturing-aware Curvilinear ILT via Circular E-beam Mask WriterProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3655926(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3655926
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
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cover image ACM Conferences

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

July 2022

1462 pages

ISBN:9781450391429

DOI:10.1145/3489517

General Chair:
Rob Oshana
NXP

Copyright © 2022 ACM.

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Published: 23 August 2022

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DAC '22: 59th ACM/IEEE Design Automation Conference

July 10 - 14, 2022

California, San Francisco

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

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Cited By

Liang XYang HLiu KYu BMa YDe V(2024)CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement LearningProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3656254(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3656254
Zhang XZheng SChen GZhu BXu HYu BDe V(2024)Fracturing-aware Curvilinear ILT via Circular E-beam Mask WriterProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3655926(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3655926
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
Xu HQi PTang FMa RLiang HHuang ZWen X(2024)SwinT-ILT: Swin Transformer embedding end-to-end mask optimization modelJournal of Micro/Nanopatterning, Materials, and Metrology10.1117/1.JMM.23.1.01320123:01Online publication date: 1-Jan-2024
https://doi.org/10.1117/1.JMM.23.1.013201
Chen GWang ZYu BPan DWong M(2024)Ultrafast Source Mask Optimization via Conditional Discrete DiffusionIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.336140043:7(2140-2150)Online publication date: Jul-2024
https://doi.org/10.1109/TCAD.2024.3361400
Zhu HJiang XShu DCheng XHou BYou H(2024)A Review of DNN and GPU in Optical Proximity Correction2024 2nd International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA62518.2024.10617556(703-709)Online publication date: 10-May-2024
https://doi.org/10.1109/ISEDA62518.2024.10617556
Huang CTung HFeng YHsu HLiu HLin AYu P(2024)A Novel Optical Proximity Correction Machine Learning Model Using a Single-Flow Convolutional Feedback Networks With Customized AttentionIEEE Access10.1109/ACCESS.2024.349481612(165979-165991)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3494816
Zheng SYang HZhu BYu BWong MOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)LithoBenchProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667437(30243-30254)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667437
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
Zhu BZheng SYu ZChen GMa YYang FYu BWong M(2023)L2O-ILT: Learning to Optimize Inverse Lithography TechniquesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.332316443:3(944-955)Online publication date: 10-Oct-2023
https://dl.acm.org/doi/10.1109/TCAD.2023.3323164
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