• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.7
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• pp.1295-1297
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• 2008

Large-sized flat-panel displays are widely used for PC monitors and TV displays. In this paper, frequency domain pre-filter algorithms are presented for detection of defects in large-sized Thin Film Transistor-Liquid Crystal Display(TFT-LCD) panel surfaces. Frequency analysis with 1-D, 2-D FFT methods for extract the periodic patterns of lattice structures in TFT-LCD is performed. To remove this patterns, frequency domain band-stop filters were used for eliminating specific frequency components. In order to acquire only defected images, 2-D inverse FFT methods to inverse transform of frequency domain images were used.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.784-793
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• 2012

Defects of TFT-LCD is detected by thresholding the difference image between the input image and template one because LCD panel has its inherent patterns. However, the pitch corresponding to pattern period is gradually changed according to the distance from the center of camera due to geometric distortion of camera characteristics. This paper presents a method to detect defects through comparing the pitch area with neighbor pitch areas where the perspective transform is performed with the extracted features to correct the distortion. The experimental results show that the performance of the proposed method is very effective for real data.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.852-859
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• 2009

Normally, to extract the defect in TFT-LCD inspection system, the image is obtained by using line scan camera or area scan camera which is achieved by CCD or CMOS sensor. Because of the limited dynamic range of CCD or CMOS sensor as well as the effect of the illumination, these images are frequently degraded and the important features are hard to decern by a human viewer. In order to overcome this problem, the feature vectors in the image are obtained by using the average intensity difference between defect and background based on the weber's law and the standard deviation of the background region. The defect detection method uses non-linear SVM (Supports Vector Machine) method using the extracted feature vectors. The experiment results show that the proposed method yields better performance of defect classification methods over conveniently method.

• Journal of Korea Multimedia Society
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• v.15 no.4
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• pp.439-448
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• 2012

TFT-LCD panel images have non-uniform brightness, noise signal and defect signal. It is hard to divide defect signal because of non-uniform brightness and noise signal, so various divide methods have being developed. In this paper, we suggest method to divide defective regions on TFT-LCD panel image by estimating a menas of two different size of windows, which is suggested by Eikvil et al., and using difference of them. But in this method, the size of detectable defects is restricted by the size of window, hence it has inefficient problem that the size of window have to increase to divide a large defect region. To solve this problem we suggest an algorithm which can divide various size of defects, by using Multi-scale and restrict a detectable size of defects in each scale. To prove an efficiency of suggested algorithm, we show that resulting images of real TFT-LCD panel images and an artificial image with various defects.

• Journal of Korea Multimedia Society
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• v.18 no.12
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• pp.1519-1527
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• 2015

TFT-LCD automatic defect inspection system for detecting defects in place of the visual tester does pre-processing, candidate defect pixel detection, and recognition and classification through a blob analysis. An over-detection result of defects acts as an undue burden of blob analysis for recognition and classification. In this paper, we propose defect detection method based on the histogram distribution modeling of TFT-LCD image to minimize over-detection of candidate defective pixels. Primary defect candidate pixels are detected estimating the skewness of the luminance distribution histogram of the background pixels. Based on the detected defect pixels, the defective pixels other than noise pixels are detected using the distribution histogram model of the local area. Experimental results confirm that the proposed method shows an excellent defect detection result on the image containing the various types of defects and the reduction of the degree of over-detection as well.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.2
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• pp.307-313
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• 2008

In this paper, we propose a novel defects inspection algorithm for TFT-LCD panels. We first compensate the distorted image caused by the camera distortion and the uneven illumination environment using the least squares method and the bezier surface. We find a starting point of each pattern for restricting each pattern. A clean image is compared to each pattern to find defects using modified PCSR-G algorithm. The simulation example shows that our algorithm not only inspects the defects well, but also is robust to the 1-pixel error.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.1
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• pp.19-27
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• 2007

In this paper, we develope and implement a TFT-LCD cell defects detection algorithm using morphology. To detect the bright line or dark line defects and the bright pixel or dark pixel defects of the TFT-LCD cells, we determine the shape of the morphology operators considering the shape characteristics of the TFT-LCD sub pixels. Using dilation, erosion, and the subtraction operators, we extract gray level defects information. Then, we apply the optimal threshold method which shows the best results in terms of several criteria. Finally, we determine the defects using labelling method. From various experiments using TFT-LCD panels, the proposed algorithm shows superior results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1803-1811
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• 2011

We have analyzed the electrical defect detection mechanism using low energy microcolumn on the TFT substrate for TFT-LCD. In this study, we have acquired the SEM images of the various pixel defects for 7-inch TFT substrate by scanning of low energy electron beam in the high vacuum chamber. Futhermore, we have interpreted the defect detection mechanism through the correlations between the SEM images and electrical behaviors of the defective pixels. As a result, we obtained consistent results as the follows. We can confirm that the SEM images using low energy electron beam are significantly affected by the space charge effect.

• Journal of Korea Multimedia Society
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• v.19 no.3
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• pp.633-641
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• 2016

Automatic defect inspection system is composed of the step in the pre-processing, defect candidate detection, and classification. Polarizing films containing various defects should be minimized over-detection for classifying defect blobs. In this paper, we propose a defect detection algorithm using a skewness of histogram for minimizing over-detection. In order to detect up defects with similar to background pixel, we are used the characteristics of the local region. And the real defect pixels are distinguished from the noise using the probability density function. Experimental results demonstrated the minimized over-detection by utilizing the artificial images and real polarizing film images.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.149-150
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• 2007

In this paper, we propose a novel defects inspection algorithm for TFT-LCD panels. We first compensate the distorted image caused by the camera distortion and the uneven illumination environment using the least squares method and the bezier surface. We find a starting point of each pattern. The reference frame, made by subtract method using several clean patterns, is compared to each pattern to find defects. The simulation example shows that our algorithm not only inspects the defects well, but also is robust to the 1-pixel error.