• 전자공학회논문지SC
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• 제47권6호
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• pp.80-85
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• 2010

현재 BGA 409 chip의 외관검사는 대부분 현미경을 이용한 육안검사로 이루어지고 있다. 그러나 인간의 시력에 의존하여 검사하는 현재의 외관검사 방법은 검사자의 육체적, 정신적 부분에 의하여 검사 결과가 변화하기 때문에 안정적인 결과를 기대하기 어렵다. 따라서 육안검사 시 발생하는 문제점을 개선하기 위해 BGA 솔더볼 외관검사의 비전 시스템이 개발 되었고, 이는 기존의 검사 방법에 비해 BGA 409 chip의 솔더볼의 외관검사의 신뢰성과 효율성을 증가시켰다. 하지만 BGA 솔더볼의 크기가 미세하고 그 특징의 구분이 힘들어 검사의 정확도가 떨어지고 오리엔테이션 오류가 발생하였다. 이에 본 논문에서는 BGA 솔더볼 외관검사의 정확도를 향상시키기 위해 에지 검출 알고리즘의 보완과 특징들만을 비교하는 패턴매칭 기법을 제안하였으며, 또한 특징 공간 설정의 기준이 되는 기준 영역의 개선을 통해 오리엔테이션 오류의 개선을 제안하였다. 즉, 본 논문에서는 기존의 비전 시스템의 정확도와 오리엔테이션 오류를 개선하는 방법을 제안함으로써 BGA 솔더볼 외관검사의 정확도를 향상시켜 결과적으로 BGA 솔더볼 외관검사의 에러율을 줄이고 검사 속도의 향상 등 기존의 외관검사 방법에 비해 향상된 검사 결과를 획득하였다.

• Journal of Electrical Engineering and information Science
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• 제2권4호
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• pp.59-65
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• 1997

In this paper, a general fuzzy syntactic method for recognition of flaw patterns and for the measurement of flaw characteristic parameters for a non-destructive inspections signal, called eddy-current, is presented. Solutions are given to the subtasks of primitive pattern selection, signal to symbol transformation, pattern grammar formulation, and event-synchronous flaw pattern extraction based on the grammars. Fuzzy attribute grammars are used as the model for the pattern grammar because of their descriptive power in the face of uncertain constraints caused by nose or distortion in the signal waveform, due to their ability to handle syntactic as well as semantic information. This approach has been implemented and the performance of eh resultant system has been evaluated using a library of law patterns obtained from steam generator tubes in nuclear power plants by an eddy current-based non-destructive inspection method.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.25-30
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• 2018

Pattern classification system is often an important component of intelligent systems. In this paper, we present a pattern classification system consisted of the feature selection module, knowledge base construction module and decision module. We introduce a feature impact evaluation selection method based on fuzzy cluster analysis considering computational approach and generalization capability of given data characteristics. A fuzzy neural network, OFUN-NET based on unsupervised learning data mining technique produces knowledge base for representative clusters. 240 blemish pattern images are prepared and applied to the proposed system. Experimental results show the feasibility of the proposed classification system as an automating defect inspection tool.

• 반도체디스플레이기술학회지
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• 제20권3호
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• pp.45-50
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• 2021

The adjustment of lens magnification can make the degree of precision in pattern measurement be improved, but several problems such as high cost, smaller field of view and stage error accumulation are followed. In this paper, a method for precisely measuring patterns is proposed based on gradient transition vector, in order to solve these problems. The performance of our method is evaluated using pattern images with several directions. Also, it is compared with previous methods based on edge and gray-level moment. It is judged that the proposed method outperforms consistent pattern width results, and so could be applied to automation processes for measurement and inspection of precise and complexed patterns in IT, BT industry products.

• International Journal of Quality Innovation
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• 제7권3호
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• pp.58-69
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• 2006

This paper presents neural network-based recognition system for automatic light emitting diode (LED) inspection. The back-propagation neural network (BPNN) is proposed and tested. The current-voltage (I-V) characteristic data of LED from the inspection process is used for the network training and testing. This study selects 300 random samples as network training and employs 100 samples as network testing. The experimental results show that if the classification work is done well, the accuracy of recognition is 100%, and the testing speed of the proposed recognition system is almost one half faster than the traditional inspection system does. The proposed neural-network approach is successfully demonstrated by real data sets and can be effectively developed as a recognition system for a practical application purpose.

• 한국위성정보통신학회논문지
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• 제10권3호
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• pp.16-21
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• 2015

본 논문은 두 가지 영상 이벤트 신호로 제어되는 실시간 차량하부 검사 엔진에 대해 제안한다. 첫 번째 영상 이벤트는 차량 번호판 인식에 의한 과정으로 생성된 것이고, 두 번째 영상 이벤트는 차량 하부 특정 이벤트 검출에 의해 생성된 것이다. 실험 결과, 두 영상 이벤트 모두 2.8초, 1.1초로 실시간 처리에 적합하게 발생되는 것을 확인할 수 있었고, 이러한 영상 이벤트가 시스템의 제어 체계로 사용되어 후부에 연결되는 다음 대응 상황에 적합한 신호를 보내주는 것으로 확인할 수 있었다.

• 한국정밀공학회지
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• 제20권12호
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• pp.105-114
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• 2003

In this study, a dicing machine with vision system was built and an algorithm for automatic alignment was developed for dual camera system. The system had a macro and a micro inspection tool. The algorithm was formulated from geometric relations. When a wafer was put on the cutting stage within certain range, it was inspected by vision system and compared with a standard pattern. The difference between the patterns was analyzed and evaluated. Then, the stage was moved by x, y, $\theta$ axes to compensate these differences. The amount of compensation was calculated from the result of the vision inspection through the automatic alignment algorithm. The stage was moved to the compensated position and was inspected by vision for checking its result again. Accuracy and validity of the algorithm was discussed from these data.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1988년도 전기.전자공학 학술대회 논문집
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• pp.959-962
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• 1988

This paper describes an automatic visual inspection system for fabric defects based on pattern recognition techniques. The inspection for fabric defects can be separated into three sequences of operations which are the detection of fabric defects[1], the classification of figures of fabric defects, and the classification of fabric defects. Comparing projections of defect-detected images with the predefined complex, the classification accuracy of figures of fabric defects was found to be 95.3 percent. Employing the Bayes classifier using cluster shade in SGLDM and variance in decorrelation method as features, the classification accuracy of regional figure defects was found to be 82.4 percent. Finally, some experimental results for line and dispersed figures of fabric defects are included.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.389-390
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• 2006

The goal of this paper is automation of a remocon inspection process using machine vision system. This system prevents error that is occurred by physical and spirit condition of human. Also this system has been developed to raise the reliability of remocon inspection. This system has been developed only using PC, CCD Camera and Visual C++ for universal workplaces. The performance of this system is an accuracy improvement of $2{\sim}3[%]$ and a processing time reduction of about 100[ms] against existing pattern matching method.

• 반도체디스플레이기술학회지
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• 제10권1호
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• pp.113-118
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• 2011

This paper proposes computer visual inspection algorithms for PCB defects which are found in a manufacturing process. The proposed method can detect open circuit and short circuit on bare PCB without using any reference images. It performs adaptive threshold processing for the ROI (Region of Interest) of a target image, median filtering to remove noises, and then analyzes connected components of the binary image. In this paper, the connected components of circuit pattern are defined as 6 types. The proposed method classifies the connected components of the target image into 6 types, and determines an unclassified component as a defect of the circuit. The analysis of the original target image detects open circuits, while the analysis of the complement image finds short circuits. The machine vision inspection system is implemented using C language in an embedded Linux system for a high-speed real-time image processing. Experiment results show that the proposed algorithms are quite successful.