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

Jueun Kwak, Ki Bum Lee, Jaeyeon Jang, Kyong Soo Chang, Chang Ouk Kim

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)1047-1055
Number of pages9
JournalJournal of Intelligent Manufacturing
Volume30
Issue number3
DOIs
Publication statusPublished - 2019 Mar 15

All Science Journal Classification (ASJC) codes

  • Software
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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