• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.259-264
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• 2009

High level quality control is required for mobile TFT-LCD modules which are frequently used for fine observation. However, quantitative quality control is difficult. Defect inspection using naked eyes makes irregular inspection results. This paper developed desk type defect inspection equipment for mobile TFT-LCD modules using the same inspection criterion with that of naked eyes. From experiments using this equipments, possibilities of standardization in defect inspection equipment for mobile TFT-LCD modules are presented.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.5
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• pp.432-437
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• 2008

This paper proposes a defect cell extraction algorithm for TFT-LCD auto-repair system. Auto defect search algorithm and automatic defect cell extraction method are very important for TFT-LCD auto repair system. In the previous literature[1], we proposed an automatic visual inspection algorithm of TFT-LCD. Based on the inspected information(defect size and defect axis, if defect exists) by the automatic search algorithm, defect cells should be extracted from the input image for the auto repair system. For automatic extraction of defect cells, we used a novel block matching algorithm and a simple filtering process in order to find a given reference point in the LCD cell. The proposed defect cell extraction algorithm can be used in all kinds of TFT-LCD devices by changing a stored template which includes a given reference point. Various experimental results show the effectiveness of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.5
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• pp.444-452
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• 2008

This paper presents an overall image processing approach of defect inspection of patterned TFT-LCD panels for the real manufacturing process. A prototype of AOI(Automatic Optical Inspection) system which is composed of air floating stage and multi line scan cameras is developed. Adjacent pattern comparison algorithm is enhanced and used for pattern elimination to extract defects in the patterned image of TFT-LCD panels. New region merging algorithm which is based on border expansion is proposed to identify defects from the pattern eliminated defect image. Experimental results show that a developed AOI system has acceptable performance and the proposed algorithm reduces environmental effects and processing time effectively for applying to the real manufacturing process.

• Proceedings of the Safety Management and Science Conference
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• 2004.05a
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• pp.283-289
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• 2004

The number of pixel defects including bright and black dots on a panel is one of the critical factors determining the quality of TFT-LCD. Since pixel defects on the TFT-LCD panels are sometimes unavoidable, manufacturers have to inspect the panels so that any panel with an unacceptable number of defects will not be delivered to the buyers. However, the buyers demand for the manufacturers to meet different pixel defects tolerances (acceptable number of pixel defects on a TFT-LCD panel) around central(tight) and peripheral(loose) inspection zones. The disagreement in quality standard among different buyers also cause confusions in screening non-confirmative products and unstable yield of production. Few research has focused on the effects of defect locations on a TFT-LCD panel on their detection probabilities and the rational division of defect inspection zones. In this research, experiments were conducted to find the detection probabilities of black dot defects with respect to their varying locations on a TFT-LCD. It is proposed a rational division of inspection zone on a TFT-LCD panel on the basis of detection probabilities of the defects. With these division of inspection zones and the mean defect detection probability within each zone, it is expected to establish a more reasonable pixel defects tolerances.

• Journal of Broadcast Engineering
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• v.18 no.4
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• pp.525-534
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• 2013

Recently, mura defect inspection techniques are receiving attention in LCD production procedure since demands of TFT-LCD are growing. In this paper, we propose an automatic mura defect inspection method using gabor wavelet transform and DCT. First, we generate a reference panel image using DCT based method. For original panel image and generated reference panel image, we apply a gabor wavelet transform to eliminate texture information in images. Then, we extract mura defect regions from the difference image between gabor wavelet transform image of original panel and generated reference panel image. Finally, all mura defect regions are quantified to detect accurate mura defects. Experimental results show that our method is more accurate and efficient than previous methods.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1783-1786
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• 2003

In this paper, an automated line-defect detection method for TFT-LCD panel is presented. A DFB(Directional Filter Bank) and line-projection method are used to find line-defect which is one of the major defects occurred in TFT-LCD panel. The experimental results show that the proposed algorithm gave promising results for applying automated inspection technique for TFT-LCD panel.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08a
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• pp.286-289
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• 2007

We have developed an automated circuit defect inspection and repair method that can be used to improve the yield ratio of TFT-LCD. The method focuses on correcting resist patterns after the development process to ensure shape regularity. We built a prototype system and confirmed that the method is valid.

• Journal of Korea Multimedia Society
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• v.15 no.2
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• pp.204-214
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• 2012

This paper proposes an image segmentation for a vision-based automated defect inspection system on surface image of TFT-LCD(Thin Film Transistor Liquid Crystal Display) panels. TFT-LCD images have non-uniform brightness, which is hard to finding defective regions. Although there are several methods or proposed algorithms, it is difficult to divide the defect with high reliability because of non-uniform properties in the image. Kamel and Zhao disclosed a method which based on logical stage algorithm for segmentation of graphics and character. This method is a one of the local segmentation method that has a advantage. It is that characters and graphics are well segmented in an image which has non-uniform property. As TFT-LCD panel image has a same property, so this paper proposes new algorithm to segment regions of defects based on Kamel and Zhao's algorithm. Our algorithm has an advantage that there are a few ghost objects around the defects. We had experiments to prove performance in real TFT-LCD panel images, and comparing with the FFT(Fast Fourier Transform) method which is used a bandpass filter.

• Journal of Korea Multimedia Society
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• v.13 no.8
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• pp.1115-1127
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• 2010

In order to increase the productivity in manufacturing TFT-LCD(thin film transistor-liquid crystal display), it is essential to classify defects that occur during the production and make an appropriate decision on whether the product with defects is scrapped or not. The decision mainly depends on classifying the defects accurately. In this paper, we present an effective classification method for film defects acquired in the panel production line by analyzing the intensity distribution and shape feature of the defects. We first generate a binary image for each defect by separating defect regions from background (non-defect) regions. Then, we extract various features from the defect regions such as the linearity of the defect, the intensity distribution, and the shape characteristics considering intensity, and construct a referential image database that stores those feature values. Finally, we determine the type of a defect by matching a defect image with a referential image in the database through the matching cost function between the two images. To verify the effectiveness of our method, we conducted a classification experiment using defect images acquired from real TFT-LCD production lines. Experimental results show that our method has achieved highly effective classification enough to be used in the production line.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.615-621
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• 2009

TFT-LCD image consists of ununiform background, random noises and target defect signal components. Defects in TFT-LCD have some intensity variations compared to background region. It is sometimes difficult for human inspectors to figure out. In this paper, we propose multi-level threshold scheme for detection of the real defect using probability density function with Parzen Window. The experimental results show that the proposed algorithms produce promising results and can be applied to automated inspection systems for finding defects in the TFT-LCD image.