Journal of Applied Optics, Volume. 44, Issue 5, 1022(2023)

Czochralski monocrystalline-silicon dislocation detection method based on improved YOLOv5 algorithm

Zhou YANG1, Ying CHENG1, Shijing ZHANG1, Xinyu TAO1, Xutao MO1, Sihai MA2, and Xianshan HUANG1、*
Author Affiliations
  • 1School of Science and Engineering of Mathematics and Physics, Anhui University of Technology, Ma'anshan 243002, China
  • 2Anhui Yixin Semiconductor Co.,Ltd., Hefei 231100, China
  • show less
    References(17)

    [1] LIU Shukun, LI Zhanliang, SUN Ningning. Corrosion and characterization of defects in crystalline silicon[J]. China Petroleum and Chemical Standard and Quality, 31, 43-48(2011).

    [2] GALLIEN B, BAILLY S, DUFFAR T. Comparative study of dislocation density characterizations on silicon[J]. Crystal Research and Technology, 52, 201600224.(2017).

    [3] WOO S, BERTONI M, CHOI K et al. An insight into dislocation density reduction in multicrystalline silicon[J]. Solar Energy Materials and Solar Cells, 155, 88-100(2016).

    [4] NEEDLEMAN D B, CHOI H, POWELL D M et al. Rapid dislocation-density mapping of as-cut crystalline silicon wafers[J]. Physica Status Solidi-Rapid Research Letters, 7, 1041-1044.(2013).

    [5] LI Meng, LIU Junfei. Generation principle and detection methods on straight pulling silicon crystal dislocation and micro defects[J]. Physics Bulletin, 2, 121-123(2013).

    [6] ZHOU Hongzhi, YU Gan. Research on pedestrian detection technology based on the SVM classifier trained by HOG and LTP features[J]. Future Generation Computer Systems, 125, 604-615(2021).

    [7] LI Xuan, YANG Zhou, TAO Xinyu et al. Particle image detection based on Mask R-CNN combined with edge segmentation[J]. Journal of Applied Optics, 43, 93-103(2022).

    [8] JIANG D, LI G, TAN C et al. Semantic segmentation for multiscale target based on object recognition using the improved Faster-RCNN model[J]. Future Generation Computer Systems, 123, 94-104(2021).

    [10] YU Z, SHEN Y, SHEN C. A real-time detection approach for bridge cracks based on YOLOv4-FPM[J]. Automation in Construction, 122, 103514(2021).

    [11] WANG Z, WU L, LI T et al. A smoke detection model based on improved YOLOv5[J]. Mathematics, 10, 1-13.(2022).

    [12] JUBAYER M F, SOEB MJA, PAUL M K et al. Detection of mold on the food surface using YOLOv5[J]. Current Research in Food Science, 4, 724-728(2021).

    [13] TAO Zhiyong, DU Fuyan, REN Xiaokui et al. , Defect detection of solar cells based on T-VGG[J]. Acta Energiae Solaris Sinica, 43, 145-151(2022).

    [14] FU Y Z, LI X, MA X et al. Deep-learning-based defect evaluation of mono-like cast silicon wafers[J]. Photonics, 8, 426.(2021).

    [16] NIU Z, ZHONG G, YU H. A review on the attention mechanism of deep learning[J]. Neurocomputing, 452, 48-62(2021).

    Tools

    Get Citation

    Copy Citation Text

    Zhou YANG, Ying CHENG, Shijing ZHANG, Xinyu TAO, Xutao MO, Sihai MA, Xianshan HUANG. Czochralski monocrystalline-silicon dislocation detection method based on improved YOLOv5 algorithm[J]. Journal of Applied Optics, 2023, 44(5): 1022

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Research Articles

    Received: Sep. 30, 2022

    Accepted: --

    Published Online: Mar. 12, 2024

    The Author Email: Xianshan HUANG (黄仙山)

    DOI:10.5768/JAO202344.0502002

    Topics