research-article

Machine-learning-based hotspot detection using topological classification and critical feature extraction

Authors:

Iris Hui-Ru Jiang,

Charles ChiangAuthors Info & Claims

DAC '13: Proceedings of the 50th Annual Design Automation Conference

Article No.: 67, Pages 1 - 6

https://doi.org/10.1145/2463209.2488816

Published: 29 May 2013 Publication History

Abstract

Because of the widening sub-wavelength lithography gap in advanced fabrication technology, lithography hotspot detection has become an essential task in design for manufacturability. Current state-of-the-art works unite pattern matching and machine learning engines. Unlike them, we fully exploit the strengths of machine learning using novel techniques. By combing topological classification and critical feature extraction, our hotspot detection framework achieves very high accuracy. Furthermore, to speed up the evaluation, we verify only possible layout clips instead of full-layout scanning. After detection, we filter hotspots to reduce the false alarm. Experimental results show that the proposed framework is very accurate and demonstrates a rapid training convergence. Moreover, our framework outperforms the 2012 CAD Contest at ICCAD winner on accuracy and false alarm.

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Cited By

Jiang CSun HFeng DXie ZTan BLiu K(2025)LithoExp: Explainable Two-stage CNN-based Lithographic Hotspot Detection with Layout Defect LocalizationACM Transactions on Design Automation of Electronic Systems10.1145/3721129Online publication date: 27-Feb-2025
https://doi.org/10.1145/3721129
Ban XLiu AHe LGong L(2024)An Intrusion Detection System Using Vision Transformer for Representation LearningFrontiers in Cyber Security10.1007/978-981-99-9331-4_35(531-544)Online publication date: 4-Jan-2024
https://doi.org/10.1007/978-981-99-9331-4_35
Yun HKim EKim DPark HJun M(2023)Machine Learning for Object Recognition in Manufacturing ApplicationsInternational Journal of Precision Engineering and Manufacturing10.1007/s12541-022-00764-624:4(683-712)Online publication date: 16-Jan-2023
https://doi.org/10.1007/s12541-022-00764-6
Show More Cited By

Index Terms

Machine-learning-based hotspot detection using topological classification and critical feature extraction
1. Hardware
  1. Electronic design automation
    1. Physical design (EDA)

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cover image ACM Conferences

DAC '13: Proceedings of the 50th Annual Design Automation Conference

May 2013

1285 pages

ISBN:9781450320719

DOI:10.1145/2463209

Copyright © 2013 ACM.

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EDAC: Electronic Design Automation Consortium
SIGDA: ACM Special Interest Group on Design Automation
IEEE-CEDA

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SIGBED: ACM Special Interest Group on Embedded Systems

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Association for Computing Machinery

New York, NY, United States

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Published: 29 May 2013

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DAC '13: The 50th Annual Design Automation Conference 2013

May 29 - June 7, 2013

Texas, Austin

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

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Cited By

Jiang CSun HFeng DXie ZTan BLiu K(2025)LithoExp: Explainable Two-stage CNN-based Lithographic Hotspot Detection with Layout Defect LocalizationACM Transactions on Design Automation of Electronic Systems10.1145/3721129Online publication date: 27-Feb-2025
https://doi.org/10.1145/3721129
Ban XLiu AHe LGong L(2024)An Intrusion Detection System Using Vision Transformer for Representation LearningFrontiers in Cyber Security10.1007/978-981-99-9331-4_35(531-544)Online publication date: 4-Jan-2024
https://doi.org/10.1007/978-981-99-9331-4_35
Yun HKim EKim DPark HJun M(2023)Machine Learning for Object Recognition in Manufacturing ApplicationsInternational Journal of Precision Engineering and Manufacturing10.1007/s12541-022-00764-624:4(683-712)Online publication date: 16-Jan-2023
https://doi.org/10.1007/s12541-022-00764-6
Khan IMittal NAnsari M(2023)Applications of VLSI Design in Artificial Intelligence and Machine LearningMachine Learning for VLSI Chip Design10.1002/9781119910497.ch1(1-17)Online publication date: 23-Jun-2023
https://doi.org/10.1002/9781119910497.ch1
Liao LLi SChe YShi WWang X(2022)Lithography Hotspot Detection Method Based on Transfer Learning Using Pre-Trained Deep Convolutional Neural NetworkApplied Sciences10.3390/app1204219212:4(2192)Online publication date: 19-Feb-2022
https://doi.org/10.3390/app12042192
Alawieh MPan D(2021)ADAPT: An Adaptive Machine Learning Framework with Application to Lithography Hotspot Detection2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)10.1109/MLCAD52597.2021.9531210(1-6)Online publication date: 30-Aug-2021
https://doi.org/10.1109/MLCAD52597.2021.9531210
Tabrizi ADarav NRakai LBustany IKennings ABehjat L(2020)Eh?Predictor: A Deep Learning Framework to Identify Detailed Routing Short Violations From a Placed NetlistIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2019.291713039:6(1177-1190)Online publication date: Jun-2020
https://doi.org/10.1109/TCAD.2019.2917130
Chen YLin YGai TSu YWei YPan D(2020)Semi-Supervised Hotspot Detection with Self-Paced Multi-Task LearningIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2019.2912948(1-1)Online publication date: 2020
https://doi.org/10.1109/TCAD.2019.2912948
Jiang YYang FZhu HYu BZhou DZeng X(2019)Efficient Layout Hotspot Detection via Binarized Residual Neural NetworkProceedings of the 56th Annual Design Automation Conference 201910.1145/3316781.3317811(1-6)Online publication date: 2-Jun-2019
https://dl.acm.org/doi/10.1145/3316781.3317811
Ye WLin YLi MLiu QPan DShibuya T(2019)LithoROCProceedings of the 24th Asia and South Pacific Design Automation Conference10.1145/3287624.3288746(292-298)Online publication date: 21-Jan-2019
https://dl.acm.org/doi/10.1145/3287624.3288746
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