Acta Optica Sinica, Volume. 43, Issue 23, 2315001(2023)

Lithography Hotspot Detection Based on Improved YOLOv3

Mu Lin, Fanwenqing Zeng, Xiaoxuan Liu, Fencheng Li, Jun Luo, and Yijiang Shen*
Author Affiliations
  • School of Automation, Guangdong University of Technology, Guangzhou 510006, Guangdong , China
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    References(16)

    [1] Yang H Y, Lin Y J, Yu B et al. Lithography hotspot detection: from shallow to deep learning[C], 233-238(2017).

    [2] Kim J, Fan M H. Hotspot detection on post-OPC layout using full-chip simulation-based verification tool: a case study with aerial image simulation[J]. Proceedings of SPIE, 5256, 919-925(2003).

    [3] Roseboom E, Rossman M, Chang F C et al. Automated full-chip hotspot detection and removal flow for interconnect layers of cell-based designs[J]. Proceedings of SPIE, 6521, 65210C(2007).

    [4] Yu Y T, Chan Y C, Sinha S et al. Accurate process-hotspot detection using critical design rule extraction[C], 1167-1172(2012).

    [5] Wen W Y, Li J C, Lin S Y et al. A fuzzy-matching model with grid reduction for lithography hotspot detection[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 33, 1671-1680(2014).

    [6] Cao K K, Shen H B, Yang Y W. Lithographic hotspot detection based on SVM and genetic algorithm[J]. Journal of Zhejiang University (Science Edition), 38, 41-45(2011).

    [7] Ding D, Torres J A, Pan D Z. High performance lithography hotspot detection with successively refined pattern identifications and machine learning[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 30, 1621-1634(2011).

    [8] Guo Q S, Shi Z, Zhang P Y. Lithographic hotspot detection based on faster R-CNN[J]. Microelectronics, 48, 834-838, 845(2018).

    [9] Shin M, Lee J H. Accurate lithography hotspot detection using deep convolutional neural networks[J]. Journal of Micro/Nanolithography, MEMS, and MOEMS, 15, 043507(2016).

    [10] Matsunawa T, Nojima S, Kotani T. Automatic layout feature extraction for lithography hotspot detection based on deep neural network[J]. Proceedings of SPIE, 9781, 97810H(2016).

    [11] Yang H Y, Luo L Y, Su J et al. Imbalance aware lithography hotspot detection: a deep learning approach[J]. Journal of Micro/Nanolithography, MEMS, and MOEMS, 16, 033504(2017).

    [12] Xiao Y D, Huang X Q, Liu K. Model transferability from ImageNet to lithography hotspot detection[J]. Journal of Electronic Testing, 37, 141-149(2021).

    [13] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

    [14] Zhou K B, Zhang K F, Liu J et al. An imbalance aware lithography hotspot detection method based on HDAM and pre-trained GoogLeNet[J]. Measurement Science and Technology, 32, 125008(2021).

    [15] Zhang M L, Li Y K, Yang H et al. Towards class-imbalance aware multi-label learning[J]. IEEE Transactions on Cybernetics, 52, 4459-4471(2022).

    [16] Torres J A. ICCAD-2012 CAD contest in fuzzy pattern matching for physical verification and benchmark suite[C], 349-350(2012).

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    Mu Lin, Fanwenqing Zeng, Xiaoxuan Liu, Fencheng Li, Jun Luo, Yijiang Shen. Lithography Hotspot Detection Based on Improved YOLOv3[J]. Acta Optica Sinica, 2023, 43(23): 2315001

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    Paper Information

    Category: Machine Vision

    Received: May. 5, 2023

    Accepted: Aug. 29, 2023

    Published Online: Dec. 8, 2023

    The Author Email: Shen Yijiang (yjshen@gdut.edu.cn)

    DOI:10.3788/AOS230928

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