• 전기전자학회논문지
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• 제18권2호
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• pp.234-239
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• 2014

This paper proposes a new automatic detection method to inspect mura defects on display film surface using morphological image processing and labeling. This automatic detection method for mura defects on display films comprises 3 phases of preprocessing with morphological image processing, Gabor filtering, and labeling. Since distorted results could be obtained with the presence of non-uniform illumination, preprocessing step reduces illumination components using morphological image processing. In Gabor filtering, mura images are created with binary coded mura components using Gabor filters. Subsequently, labeling is a final phase of finding the mura defect area using the difference between large mura defects and values in the periphery. To evaluate the accuracy of the proposed detection method, detection rate was assessed by applying the method in 200 display film samples. As a result, the detection rate was high at about 95.5%. Moreover, the study was able to acquire reliable results using the Semu index for luminance mura in image quality inspection.

• Industrial Engineering and Management Systems
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• 제8권3호
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• pp.148-154
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• 2009

In recent days, large-sized flat-panel display (FPD) has been increasingly applied to computer monitors and TVs. Mura defects, appearing as low contrast or non-uniform brightness region, sometimes occur in manufacturing of the Thin-Film Transistor Liquid-Crystal Displays (TFT-LCD). Implementation of automatic Mura inspection methods is necessary for TFT-LCD production. Various existing Mura detection methods based on regression diagnostics, surface fitting and data transformation have been presented with good performance. This paper proposes an efficient Mura detection method that is based on a regression diagnostics using studentized residuals for automatic Mura inspection of FPD. The input image is estimated by a linear model and then the studentized residuals are calculated for filtering Mura regions. After image dilation, the proposed threshold is determined for detecting the non-uniform brightness region in TFT-LCD by means of monitoring the every pixel in the image. The experimental results obtained from several test images are used to illustrate the effectiveness and efficiency of the proposed method for Mura detection.

• Journal of Information Display
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• 제12권3호
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• pp.121-124
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• 2011

The mechanism for lattice-shaped mura defects was proposed by characterizing the electro-optic properties of liquid crystal (LC), which showed different transmission properties between the normal and mura defect areas. An increase in the mura defect rate was observed when the dotted LC in the one drop filling (ODF) was exposed for a longer time. The dotted LC droplet at the edge evaporated more rapidly than that in the center. This resulted in a higher concentration of polar singles at the edge of the dotted LC droplet, leading to a higher ${\Delta}n$ value and higher transmittance. This implies that the reductio of the exposure time of the dotted LC to air plays a critical role in decreasing the occurrence of lattice-shaped mura defects in ODF.

• 방송공학회논문지
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• 제18권4호
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• pp.525-534
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• 2013

최근 다양한 형태의 TFT-LCD의 수요가 증가함에 따라 LCD 생산 과정에서 얼룩결함을 검사하는 기술에 대한 관심이 높아지고 있다. 본 논문에서는 가버 웨이블렛 변환(Gabor wavelet transform) 및 이산 코사인 변환(Discrete Cosine Transform, DCT)을 이용한 TFT-LCD 패널의 얼룩(mura)을 자동으로 검출하는 방법을 제안한다. 제안한 방법은 DCT 변환 기반의 TFT-LCD 패널 영상의 참조 패널 영상을 생성한다. 원 영상과 생성된 참조 패널 영상에 대해서 실수 가버 웨이블렛 변환(real gabor wavelet transform)을 적용하여 패널 영상에 포함되어 있는 얼룩 결함을 검출하는데 방해가 되는 텍스쳐 정보를 제거하고 변환 영상간의 차영상을 이용하여 제거 결함 영역을 추출한다. 추출된 영역에 대해서는 정량적 평가 과정을 통해 보다 정확한 얼룩 검출을 수행한다. 실험결과는 제안한 방법이 기존의 방법에 비해 보다 정확하고 효율적으로 얼룩을 검출하는 것을 보여준다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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• pp.347-350
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• 2003

In this paper, a new automated defects detection method for a TFT-LCD panel is presented. An input image is preprocessed to lessen small abnormal noises and non-uniformity of the image. The adaptive multi-thresholds are used to detect Muras, which are the major defects occurred on TFT-LCD panels. Those are determined adaptively depending on the brightness and the brightness distribution of a local block. For the synthetic images and real Mura images, the proposed algorithm can effectively detect Muras in a reasonable time.

• 제어로봇시스템학회논문지
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• 제14권5호
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• pp.415-419
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• 2008

Detecting and correcting defects on LCD glasses early in the manufacturing process becomes important for panel makers to reduce the manufacturing costs and to improve productivity. Many attempts have been made and were successfully applied to detect and identify simple defects such as scratches, dents, and foreign objects on glasses. However, it is still difficult to robustly detect low-contrast defect region, called Mura or blemish area on glasses. Typically, these defect areas are roughly defined as relatively large, several millimeters of diameter, and relatively dark and/or bright region of low Signal-to-Noise Ratio (SNR) against background of low-frequency signal. The aim of this article is to present a robust algorithm to segment these blemish defects. Early 90's, a highly robust estimator, known as the Model-Fitting (MF) estimator was developed by X. Zhuang et. al. and have been successfully used in many computer vision application. Compared to the conventional Least-Square (LS) estimator the MF estimator can successfully estimate model parameters from a dataset of contaminated Gaussian mixture. Such a noise model is defined as a regular white Gaussian noise model with probability $1-\varepsilon$ plus an outlier process with probability $varepsilon$. In the sense of robust estimation, the blemish defect in images can be considered as being a group of outliers in the process of estimating image background model parameters. The algorithm developed in this paper uses a modified MF estimator to robustly estimate the background model and as a by-product to segment the blemish defects, the outliers.

• 제어로봇시스템학회논문지
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• 제15권8호
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• pp.761-771
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• 2009

Detecting defects on FPD (Flat Panel Display) color filter before the full panel is made is important to reduce the manufacturing cost. Among many types of defects, the low contrast blemish such as Suzi Mura is difficult to detect using standard CCD cameras. Even skilled inspectors in the inspection line can hardly identify such defects using bare eyes. To overcome this difficulty, point spectrometer has been used to analyze the spectrum to differentiate such defects from normal color filters. However, scanning ever increasing-size color filters by a point spectrometer takes too long time to be used in real production line. We propose a system using a spectral camera which can be viewed as a line scan camera composed of an array of point spectrometers. Three types of lighting system that exhibit different illumination spectrums are devised together with a calibration method of the proposed spectral camera system. To visualize the defect areas, various processing algorithms to identify and to enhance the small differences in spectrum between defective and normal areas are developed. Experiments shows 85% successful visualization. of real samples using the proposed system.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2004년도 Asia Display / IMID 04
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• pp.328-330
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• 2004

In order to stabilize the rubbing process for liquid crystal panels, the authors developed "the sparkling dot area ratio evaluation method". This method quantifies the fiber length dispersion of rubbing cloths, which is a major cause of mura defects. The newly developed method enables quantitative evaluation of rubbing cloths and contributes to the improvement of rubbing process uniformity.

• 한국정보과학회논문지:소프트웨어및응용
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• 제34권11호
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• pp.953-960
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• 2007

TFT-LCD(Thin Flat Transistor Liquid Crystal Display) 패널의 불량(Mura)을 정확히 검출하기 위해서는 패널 영상에 포함된 왜곡을 반드시 보정해야 한다. 일반적인 컴퓨터 비젼 시스템의 촬상영상에 대한 왜곡 보정 알고리즘은 이미 알려져 있지만, 패널 영상에만 독특하게 나타나는 비네팅(Vignetting) 효과는 패널 고유의 특성으로 인한 배경 불균일성(Background Non-uniformity)과 결합되어 기존의 알고리즘을 바로 적용하기 어려운 문제점이 있다. 영상 복원 결과의 정확도를 높이기 위해서는 비네팅 효과를 영상 배경과 분리하여 적절히 보정해주어야만 한다. 따라서, 본 논문에서는 주성분분석(Principle Component Anlaysis)을 통해 비네팅 효과를 분석하고 이를 보정하는 새로운 알고리즘을 제안한다. 불량이 포함된 175개의 영상을 대상으로 복원 실험을 수행한 결과, 왜곡 영상에 포함된 불량들의 평균 밝기 오차는 37% 에서 11% 로 내려갔으며 불량에 대한 판정 실패율도 14.8%에서 2.2%로 떨어졌음을 확인하였다.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2008년도 International Meeting on Information Display
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• pp.1269-1271
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• 2008

We find some array mura are caused by S/D bridge or channel open in 4 mask process. The gray tone PR thickness non-uniformity is the main reason of these defects. By the adjustment of exposure and dry etch parameters, S/D bridge and channel open ratio drops by 10.87%.