Optics and Precision Engineering, Volume. 33, Issue 9, 1434(2025)

Rapid and high-precision detection on surface defects of Micro LED

Tianyuan ZHAO1,2, Dengfeng DONG1,2、*, Guoming WANG1,2, Bo WANG1, and Weihu ZHOU1,2
Author Affiliations
  • 1Institute of Microelectronics, Chinese Academy of Sciences, Beijing00029, China
  • 2University of Chinese Academy of Sciences, Beijing100049, China
  • show less
    References(20)

    [1] JAMES SINGH K, HUANG Y M, AHMED T et al. Micro-LED as a promising candidate for high-speed visible light communication[J]. Applied Sciences, 10, 7384(2020).

    [2] WU T, SHER C W, LIN Y et al. Mini-LED and micro-LED: promising candidates for the next generation display technology[J]. Applied sciences, 8, 1557(2018).

    [3] RAHMAN M ABD AL, MOUSAVI A. A review and analysis of automatic optical inspection and quality monitoring methods in electronics industry[J]. Ieee Access, 8, 183192-183271(2020).

    [4] 苏昊, 李文豪, 李俊龙. 晶圆级Micro-LED芯片检测技术研究进展[J]. 液晶与显示, 38, 582-594(2023).

         SU H, LI W H, LI J L et al. Recent progress of wafer level Micro-LED chip inspection technology[J]. Chinese Journal of Liquid Crystals and Displays, 38, 582-594(2023).

    [5] WENG W H, TSAI C Y, HUNG C Y et al. Development of an adaptive template for fast detection of lithographic patterns of light-emitting diode chips[J]. The International Journal of Advanced Manufacturing Technology, 117, 3297-3321(2021).

    [6] ZHU G, LIU Y, MING R et al. Mass transfer, detection and repair technologies in micro-LED displays[J]. Science China Materials, 65, 2128-2153(2022).

    [7] LI Z, WEI X, HASSABALLAH M et al. A One‐Stage Deep Learning Model for Industrial Defect Detection[J]. Advanced Theory and Simulations, 6, 2200853(2023).

    [8] CHEN S H, TSAI C C. SMD LED chips defect detection using a YOLOv3-dense model[J]. Advanced engineering informatics, 47, 101255(2021).

    [9] CHEN M, CHEN J, LI C et al. Defect detection of MicroLED with low distinction based on deep learning[J]. Optics and Lasers in Engineering, 173, 107924(2024).

    [10] ZHONG Z, LI C, CHEN M et al. Micro LED defect detection with self-attention mechanism-based neural network[J]. Digital Signal Processing, 149, 104474(2024).

    [12] LIU W, ANGUELOV D, ERHAN D et al. Ssd: Single shot multibox detector[C]. The Netherlands, 21-37(14).

    [13] LIN T Y, GOYAL P, GIRSHICK R et al. Focal loss for dense object detection[C], 2980-2988(2017).

    [14] LIU S, HUANG D. Receptive field block net for accurate and fast object detection[C], 385-400(2018).

    [15] QI Z, ZHANG M, LI J et al. Improved retinanet-based defect detection for engine parts[C], 7717-7722(2023).

    [16] CHEN Y, DAI X, LIU M et al. Dynamic convolution: Attention over convolution kernels[C], 11030-11039(2020).

    [17] HOU Q, ZHOU D, FENG J. Coordinate attention for efficient mobile network design[C], 13713-13722(2021).

    [18] ZHENG Z, WANG P, LIU W et al. Distance-IoU loss: Faster and better learning for bounding box regression[C], 34, 12993-13000(2020).

    [19] TONG Z, CHEN Y, XU Z et al. Wise-IoU: bounding box regression loss with dynamic focusing mechanism[J]. Arxiv Preprint, 2023.

    Tools

    Get Citation

    Copy Citation Text

    Tianyuan ZHAO, Dengfeng DONG, Guoming WANG, Bo WANG, Weihu ZHOU. Rapid and high-precision detection on surface defects of Micro LED[J]. Optics and Precision Engineering, 2025, 33(9): 1434

    Download Citation

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

    Category:

    Received: Dec. 17, 2024

    Accepted: --

    Published Online: Jul. 22, 2025

    The Author Email: Dengfeng DONG (dongdengfeng@ime.ac.cn)

    DOI:10.37188/OPE.20253309.1434

    Topics