Earticle

A image defect detecting vision system for the automatic optical inspection of wafer has been developed. For the successful detection of various kinds of defects, the performance of two threshold selection methods are compared and the improved Otsu method is adopted so that it can handle both unimodal and bimodal distributions of the histogram equally well. An automatic defect detection software for practical use was developed with the function of detection of ROI, fast thresholding and area segmentation. Finally each defect pattern in the wafer is classified and grouped into one of user-defined defect categories and more than 14 test wafer samples are tested for the evaluation of detection and classification accuracy in the inspection system.

This study is a duralumin and applying heat to the STS-wide measurement range is used, the surface temperature by using the temperature of studying the non-destructive testing of a new paradigm to estimate the position and size of a structure defect purpose. STS and duralumin which has a structure defect is applied a heat by a heater. Its difference of STS and duralumin surface temperature is measured using IR thermography. The estimated result of the STS and duralumin experiment and that of theoretical analysis of PDE are compared and analyzed to diagnose the STS and duralumin defect. Moreover, this study can save time and money and improve accuracy contrast to the existing ultrasonic NDE experiment. In addition, the new paradigm of NDT/NDE by reverse-engineering is going to be valid if the data of thermal analysis and temperature distribution from the specifications of many materials is accumulated and verified. In this study, the average surface temperature of the STS in the same heating condition was 4.53 ° K higher than that of duralumin and both of the surface temperature showed an inflection point in the defect 2.5mm.The maximum surface temperature difference was formed on the 2.5mm, and the study proves its reliability because the average surface temperature of the STS and duralumin was 0.74°K, 0.45°K higher than the theoretical surface temperature.

I propose an algorithm to detect defects in the production of wire mesh using computer image processing. The process is explained as follows, First reading consecutive frames coming through the camera, then the preprocessing process is performed. Second calculate the absolute difference between the two images to distinguish the moving wire mesh from the unnecessary background image. Third based on the past moving data of the welded wire mesh, predict and track future movement. As a result of observing the samples of some defective welded wire mesh products, it was confirmed that the horizontal line of the defective wire mesh had a higher height value of the tracked wire netting. Therefore it is possible to judge whether there is a defect or not at the same time without any additional process to judge. Finally, shear strength test were performed on the welds determined to be normal products by the algorithm proposed in this paper, so that I could verify the reliability and validity of the proposed algorithm.

There are shortcomings in the currently used traditional CCD imaging system which can automatically recognize banknote image defect, such as the need to manually extract the defect characteristics and low accuracy rate of detection results. This paper briefly introduced the advantage of convolution Neural Network (CNN) in image classification and designed a image defect identification method based on convolutional neural network (CNN). The experimental results on data sets show that the identification accuracy rate of this method is 95.6%, which is significantly better than traditional identification method.

GGAC is an improvement based on geodesic active contour model (GAC). GGAC model is a widely used method for image segmentation. But, it will be difficult to achieve satisfactory segmentation results to the texture, uneven structure, edge particles, weak edge and other features of the wood surface image. Therefore, the author proposes a segmentation method that integrates the improved Canny edge detection result integrated into the improved GGAC model redrawing boundary stop function, and uses the improved variational level set method to achieve the numerical solution. The algorithm has reduced the choice sensitivity to the initial contour and enhanced the scalability, which can make the profile curve converge to defect edges more rapidly, avoid the local optimum, and improve segmentation effects of weak edges and uneven image. The results are clearer, more consistent and real-time. It has provided a more effective way to segment the wood surface defects, and broadened the application scope of Canny operator and an improved geodesic active contour model.

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Gebode defect, that can accurately be treated surgical repair, is defined as a true communication between left ventricle and right atrium. A 74-year-old woman with a worsening history of ortophnea and peripheral edema was hospitalised. A communication between right atrium and left ventricle was diagnosed using transeusophageal echocardiography. The defect was repaired and mitral valve was replaced with a biologic valve. It would be beter to tailor surgical strategy for each case with atrioventricular canal defect after preoperative transeusophageal echocardiography and peroperative direct sight.

The leakage current in a CMOS image sensor (CIS) can have various origins. Leakage current investigations have focused on such things as cobalt-salicide, source and drain scheme, and shallow trench isolation (STI) profile. However, there have been few papers examining the effects on leakage current of nitride stringers that are formed by gate sidewall etching. So this study reports the results of a series of experiments on the effects of a nitride stringer on real display images. Different step heights were fabricated during a STI chemical mechanical polishing process to form different nitride stringer sizes, arsenic and boron were implanted in each fabricated photodiode, and the doping density profiles were analyzed. Electrons that moved onto the silicon surface caused the dark leakage current, which in turn brought up the speckle defect on the display image in the CIS.

This Paper is to aim the microdefect evaluation of semiconductor Package into a quantitative from NDI's image processing of ultrasonic wave. Accordingly, for the detection of delamination between the Joining condition of boundary microdefect of semiconductor packaga the result from sampling original image, histogramming, binary image or image processing of multinumerloal value is such as the follows. ([) The least limitation from the microdefect detection of the semiconductor package by surveying high ultrasonic wave seems to be about 0.8 $\mu\textrm{m}$ in degree. (2) A result of applying the image processing of multinumerical value to the semiconductor package it was possible to devide the Category into the effectiveness.

본 연구에서는 웨이퍼의 자동광학검사를 위한 결점 검출 비전 시스템을 개발하였다. 성공적 결점 검출을 위해 몇 가지 이진화 방법을 비교하였고, bimodal과 unimodal 분포에 모두 좋은 결과를 나타낸 개선된 Otsu 방법을 선택하였다. 빠르고 정확한 임계값 계산을 위해 ROI 추출기능을 개발하였으며 최종적으로 웨이퍼의 검출 패턴은 정의된 기준에 따라 영상 분류되었고 성능평가를 위해 14개 이상의 웨이퍼 영상으로 테스트하였다.

The semiconductor, which is precision product, requires many inspection processes. The surface conditions of the semiconductor chip affect on the functions of the semiconductors. The defects of the chip surface are cracks or voids. As general inspection method requires many inspection procedure, the inspection system which searches immediately and precisely the defects of the semiconductor chip surface is required. We suggest the detection algorithm for inspecting the surface defects of the semiconductor surface. The proposed algorithm first regards the semiconductor surface as random texture and point spread function, and secondly presents the character of texture by linear estimation theorem. This paper assumes that the gray level of each pixel of an image is estimated from a weighted sum of gray levels of its neighbor pixels by linear estimation theorem. The weight coefficients are determined so that the mean square error is minimized. The obtained estimation window(two-dimensional estimation window) characterizes the surface texture of semiconductor and is used to discriminate the defects of semiconductor surface.

TFT-LCD 영상에서 결함은 일반적으로 배경과 비교하여 밝기 차이가 큰 특징을 사용하여 검출된다. 본 논문에서는 휘도 차에 따른 결함 가능성이 높은 순으로 결함을 검출하는 순차적 결함 검출 방법을 제안한다. 제안한 방법은 배경과의 휘도 차가 큰 결함은 정확히 검출하고, 배경과의 휘도 차가 작은 결함인 한도결함도보다 신뢰있게 검출하여 과검출을 최소화할 수 있다. 실험을 통해 제안한 방법은 배경과의 휘도 차가 큰 결함뿐만 아니라 한도결함에 대해서도 우수한 검출 결과를 나타냄을 확인하였다.

In TFT-LCD panel images, defects are typically detected by using a large difference in the brightness compared to the background. In this paper, we propose a sequential defect detection algorithm according to defect possibility caused by difference of brightness. By using this method, pixels with high defect probabilities are preferentially detected and defects with a large brightness difference are accurately detected. Also, limited defects with a small brightness difference is detected more reliably, eventually minimizing the degree of over-detection. We have experimentally confirmed that our proposed method showed an excellent detection result for detecting limited defects as well as defects with a large brightness difference.

본 논문에서는 X-Ray 영상에서 용접한 부분의 기공이나 균열 등의 결함 영역을 추출하는 새로운 방법을 제안한다. 제안된 방법은 세라믹 X-Ray 영상에서 비등방성 확산 필터를 적용하여 영상의 잡음을 제거하고, 수직 및 수평 히스토그램을 각각 적용하여 용접 영역을 추출한 후, 최소 자승법을 적용하여 배경 밝기를 제거하고, 사다리꼴 형태의 Fuzzy Stretching기법을 적용하여 명암 값을 강조하여 결함 영역과 그 외의 영역간의 명암 대비를 강조한다. 그리고 Fuzzy C_Means 알고리즘을 적용하여 결함 영역을 세분화한 후, Fuzzy C_Means을 적용하여 생성된 클러스터들의 중심 명암 값을 이용하여 ${\alpha}_-cut$을 설정한 후에 임계구간을 구하고 영상을 이진화하여 최종적으로 결함 영역을 추출한다. 제안된 방법의 결함 추출 성능을 확인하기 위하여 세라믹 X-Ray 영상을 대상으로 실험한 결과, 기존의 방법보다 결함 영역이 정확히 추출되는 것을 확인할 수 있었다.

In this paper, we propose an automatic display defect detection system using image processing. The existing inspection by operators has the disadvantage that human errors may occur due to the operator's skill level, fatigue, etc., and that standardization and quantification are difficult. It also has disadvantages such as the limited inspection speed and the cost of the operator's education. The proposed system automatically detects various display defects through image processing algorithms. It was developed based on the Jetson Nano, one of the most popular SBCs (Single Board Computers), and it has a conveyor belt to automatically moves the display to the inspection position. By providing a human machine interface (HMI), the operator can check the inspection information in real time, and control the inspection flow. By storing the inspection results as a log file, the operator can check the inspection results at any time, such as the time taken to perform each algorithm and the location of the detected defects. In addition, a multi-threaded structure was adopted. The camera's operations and inspection algorithms are executed in parallel in different threads, which can shorten the inspection time compared to the system based on a single-threaded structure. The experimental results prove the defect detection capability of the system and the efficiency of the inspection time.

본 논문에서는 TFT-LCD에 사용되는 편광 필름(polarized film)의 제작 과정 중 최종 단계에서 수행되는 필름의 결함 검출 및 결함 유형을 판정하기 위한 필름 결함 검출 시스템(Film Defect Inspection System: FDIS)을 설계하고 이를 구현하였다. 제안한 시스템은 영상 세그멘테이션 기법을 이용하여 편광 필름 영상으로부터 결함을 검출하였고, 검출된 결함의 영상을 분석하여 결함 유형을 판정할 수 있도록 설계되었다. 결함 유형의 판정은 결함 영역의 형태적 특성 및 질감(texture) 등의 특징을 추출하여 템플리트(template) 데이타베이스에 저장된 기준(reference) 결함 영상과 비교함으로써 수행된다. FDIS를 이용한 실험 결과, 테스트 영상에서 모든 결함 영역을 빠른 시간 안에 (평균 0.64초), 정확히 검출하였으며(Precision 1.0, Recall 1.0), 결함 유형을 판정하는 실험에서도 평균 Precision 0.96, Recall 0.95로 정확도가 매우 높은 것을 관찰할 수 있었다. 또한 회전 변형을 적용한 경우의 결함 유형 검출 실험에서도 평균 Precision 0.95, Recall 0.89로 제안한 기법이 회전 변환에 대하여 견고함을 보여 주었다.

In this paper, we design and implement the Film Defect Inspection System (FDIS) that detects film defects and determines their types which can be used for producing polarized films of TFT-LCD. The proposed system is designed to detect film defects from polarized film images using image segmentation techniques and to determine defect types through the image analysis of detected defects. To determine defect types, we extract features such as shape and texture of defects, and compare those features with corresponding features of referential images stored in a template database. Experimental results using FDIS show that the proposed system detects all defects of test images effectively (Precision 1.0, Recall 1.0) and efficiently (within 0.64 second in average), and achieves the considerably high correctness in determining defect types (Precision 0.96 and Recall 0.95 in average). In addition, our system shows the high robustness for rotated transformation of images, achieving Precision 0.95 and Recall 0.89 in average.

In this paper an improved morphological algorithm for directional defect detection is proposed, where the defect is parallel to the texture image. The algorithm is based on obtaining the background image while removing the defect by comparing every directional morphological result with max or min except that of defect. The defect can of defect and the background image. For a computer simulation, it is shown that the proposed method has better performance than the conventional algorithm.