Automatic inspection of salt-and-pepper defects in OLED panels using image processing and control chart techniques
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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.
KeywordsAutomated visual inspection Flat panel display Salt-and-pepper defect Image processing technique Statistical control chart
This work was supported in part by Samsung Display Co., Ltd., South Korea, by the Technology Innovation Program (10045913, Development of Big Data-Based Analysis and Control Platform for Semiconductor Manufacturing Plants) funded by the Ministry of Trade, Industry & Energy (MOTIE, South Korea), by the National Research Foundation (NRF) of Korea Grant funded by the Korean government (MSIP) (NRF-2016R1A2B4008337), and by the Global PhD Fellowship Program through the NRF of Korea funded by the Ministry of Education (NRF-2015H1A2A1031081).
- Burrus, C. S., Gopinath, R. A., & Guo, H. (1997). Introduction to wavelets and wavelet transforms: A primer. Upper Saddle River, NJ: Prentice Hall.Google Scholar
- Fan, S. S., & Chuang, Y. (2010). Automatic detection of mura defect in TFT-LCD based on regression diagnostics. Pattern Recognition Letters, 31(15), 2397–2404. doi:10.1016/j.patrec.2010.07.013.
- Lissner, I., & Urban, P. (2010). How perceptually uniform can a hue linear color space be? In 18th Color and imaging conference (Vol. 2010, No. 1, pp. 97–102). Society for Imaging Science and Technology.Google Scholar
- Lu, R. S. (2016). Surface defect inspection of TFT-LCD panels based on 1D Fourier method. In Seventh international symposium on precision mechanical measurements (pp. 990308–990308). International Society for Optics and Photonics.Google Scholar
- Ryu, J.-S., Oh, J.-H., Kim, J.-G., Koo, T. K., & Park, K.-H. (2004). TFT-LCD panel blob-Mura inspection using the correlation of wavelet coefficients. In TENCON 2004. IEEE Region 10 Conference (Vol. A, pp. 219–222). IEEE.Google Scholar