• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.457-457
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• 2000

The display module of PDP consists of a pair of fine electrode patterned panels. For example, in case of 42" PDP, thousands of electrode patterns should be placed on panel, where length, width, and height of each pattern m one meter, 50${\mu}{\textrm}{m}$, and 30${\mu}{\textrm}{m}$ respectively. And pitch between patterns is around 200${\mu}{\textrm}{m}$. Electrode patterns are frequently damaged during the production process, and thus might be broken. These breakage will result in open-circuited electrical characteristic of a pattern and/or open-circuited electrical characteristic between patterns. Therefore, inspection of pattern defects is the inevitable process to improve production yield rate of the panel. In this paper, we first review several types of PDP pattern defects which affects yield-rate of PDP. And, problems of inspecting such pattern defects by a typical inspection method is addressed. Then, a novel inspection method is proposed to overcome the difficulties, where some new components and the algorithm to detect the electrode defects are explored.ored.

• International Journal of Precision Engineering and Manufacturing
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• 제5권3호
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• pp.11-18
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• 2004

In this paper, the classification of artificial defects in semiconductor packages is studied by using pattern recognition technology. For this purpose, the pattern recognition algorithm includes the user made MATLAB code. And preprocess is made of the image process and self-organizing map, which is the input of the back-propagation neural network and the dimensionality reduction method, The image process steps are data acquisition, equalization, binary and edge detection. Image process and self-organizing map are compared to the preprocess method. Also the pattern recognition technology is applied to classify two kinds of defects in semiconductor packages: cracks and delaminations.

• 대한기계학회논문집A
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• 제20권4호
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• pp.1309-1319
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• 1996

The research for the classification of the natural defects in welding zone is performd using the neuro-pattern recognition technology. The signal pattern recognition package including the user's defined function is developed to perform the digital signal processing, feature extraction, feature selection and classifier selection, The neural network classifier and the statistical classifiers such as the linear discriminant function classifier and the empirical Bayesian calssifier are compared and discussed. The neuro-pattern recognition technique is applied to the classificaiton of such natural defects as root crack, incomplete penetration, lack of fusion, slag inclusion, porosity, etc. If appropriately learned, the neural network classifier is concluded to be better than the statistical classifiers in the classification of the natural welding defects.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.1101-1104
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• 1995

Through welding fabrication, user can feel an surficaial and capable unsatisfaction because of welded defects, Generally speaking, these are called weld defects. For checking these defects effectively without time loss effectively, weldability estimation system setup isan urgent thing for detecting whole specimen quality. In this study, by laser vision camera, catching a rawdata on welded specimen profiles, treating vision processing with these data, qualititative defects are estimated from getting these information at first. At the same time, for detecting quantitative defects, whole specimen weldability estimation is pursued by multifeature pattern recognition, which is a kind of fuzzy pattern recognition. For user friendly, by weldability estimation results are shown each profiles, final reports and visual graphics method, user can easily determined weldability. By applying these system to welding fabrication, these technologies are contribution to on-line weldability estimation.

• 한국철도학회논문집
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• 제4권3호
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• pp.79-86
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• 2001

The signal pattern recognition method by acoustic emission signal is applied to detect and classify the defects of a journal bearing in a power plant. AE signals of main defects such as overheating, wear and corrosion are obtained from a small scale model. To detect and classify the defects, AE signal pattern recognition program is developed. As the classification methods, the wavelet transformation analysis, the frequency domain analysis and time domain analysis are used. Among three analyses, the wavelet transformation analysis is most effective to detect and classify the defects of the journal bearing..

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2007년도 추계학술대회 논문집
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• pp.364-367
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• 2007

In nano-imprint lithography (NIL) process, which has shown to be a good method to fabricate polymeric patterns, several kinds of pattern defects due to thermal effects during polymer flow and mold release operation have been reported. A typical defect in NIL process with high aspect ratio and low resist thickness pattern is a resist fracture during the mold release operation. It seems due to interfacial adhesion between polymer and mold. However, in the present investigation, FEM simulation of NIL molding process was carried out to predict the defects of the polymer pattern and to optimize the process by FEA. The embossing operation in NIL process was investigated in detail by FEM. From the analytical results, it was found that the lateral flow of polymer resin and the applied pressure in the embossing operation induce the weld line and the drastic lateral strain at the edge of pattern. It was also shown that the low polymer-thickness result in the delamination of polymer from the substrate. It seems that the above phenomena cause the defects of the final polymer pattern. To reduce the defect, it is important to check the initial resin thickness.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.580-582
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• 2004

As the demand of large and high-resolution display panels is increased, the black light units (BLU) of the display devices play an important roles. In this study we'll deal with various defects of BLUs. Patterns of defects can be classified by the scratches, the non-uniform misprinting for the diffused reflection, the surface stains, spots and etc. Due to these distorted patterns the high-resolution and high-precision could be impeded. We'll propose the visual inspection system to detect various defects by pattern-matching.

• 한국복합재료학회:학술대회논문집
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• 한국복합재료학회 2001년도 춘계학술발표대회 논문집
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• pp.211-214
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• 2001

Electronic Speckle Pattern Interferometry(ESPI) is one of optical technique to measure displacement precisely, uses CCD camera to show result image in real time. General ESPI system measures in-plane or out-of-plane displacement. Shearography is one of electronic speckle pattern interferometric methods which allow full-field observation of surface displacement derivatives and it is robust in vibration. The shearography provides non-contacting technique of evaluating defects nondestructively. In this study, the shearography was used to evaluate defects in Carbon Fiber Reinforced Plastic(CFRP). Various sizes of artificial defects were embedded in various depths of woven CFRP plate. Effects due to the variation of size and depth of defects were evaluated in this study.

• 한국신뢰성학회:학술대회논문집
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• 한국신뢰성학회 2002년도 정기학술대회
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• pp.245-251
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• 2002

Electronic Speckle Pattern Interferometry(ESPI) is one of optical technique to measure displacement precisely, uses CCD camera to show result image in real time. General ESPI system measures in-plane or out-of-plane displacement. Shearography is one of electronic speckle pattern interferometric methods which allow full-field observation of surface displacement derivatives and it is robust in vibration. The shearography provides non-contacting technique of evaluating defects nondestructively In this study, the shearography was used to evaluate defects in Carbon Fiber Reinforced Plastic(CFRP). Various sizes of artificial defects were embedded in various depths of woven CFRP plate. Effects due to the variation of size and depth of defects were evaluated in this study.

• 전기학회논문지
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• 제57권2호
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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.