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Automatic inspection of salt-and-pepper defects in OLED panels using image processing and control chart techniques

Author

Listed:
  • Jueun Kwak

    (Yonsei University)

  • Ki Bum Lee

    (Yonsei University)

  • Jaeyeon Jang

    (Yonsei University)

  • Kyong Soo Chang

    (Samsung Display Co., Ltd.)

  • Chang Ouk Kim

    (Yonsei University)

Abstract

In the manufacture of flat display panels, salt-and-pepper defects are caused by a malfunction in the chemical process. The defects are characterized by the dispersion of many black and white pixels in the display panels; these pixels are difficult to detect with conventional automatic fault detection methods that specialize in recognizing certain shapes, such as line or mura defects (stains). This study proposes a simple but high-performance salt-and-pepper defect detection method. First, the background image of the original image is generated using the mean filter in the spatial domain to create a noise image, which is the subtraction of the two images. A binary image is then obtained from the noise image to count the defective pixels, and a statistical control chart that monitors the number of defective pixels identifies the panel defects. Two experiments were conducted with images collected from an organic light-emitting diode inspection process, and the proposed method showed excellent performance with respect to classification accuracy and processing time.

Suggested Citation

  • Jueun Kwak & Ki Bum Lee & Jaeyeon Jang & Kyong Soo Chang & Chang Ouk Kim, 2019. "Automatic inspection of salt-and-pepper defects in OLED panels using image processing and control chart techniques," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1047-1055, March.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:3:d:10.1007_s10845-017-1304-8
    DOI: 10.1007/s10845-017-1304-8
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    References listed on IDEAS

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    1. Te-Hsiu Sun & Fang-Cheng Tien & Fang-Chih Tien & Ren-Jieh Kuo, 2016. "Automated thermal fuse inspection using machine vision and artificial neural networks," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 639-651, June.
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    Cited by:

    1. Aslı Çelik & Ayhan Küçükmanisa & Aydın Sümer & Aysun Taşyapı Çelebi & Oğuzhan Urhan, 2022. "A real-time defective pixel detection system for LCDs using deep learning based object detectors," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 985-994, April.

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