• Journal of Mechanical Science and Technology
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• 제19권3호
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• pp.820-825
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• 2005

This paper proposes non-destructive ESPI technique to evaluate inside defects of semiconductor package quantitatively. Inspection system consists of ESPI system, thermal loading system and adiabatic chamber. The technique has high feasibility in non-destructive testing of semiconductor and gives solutions to the drawbacks in previous technique, time-consuming and the difficulty of quantitative evaluation. In result, most of defects are classified in delamination, from which it is inferred to the insufficiency of adhesive strength between layers and nonhomogeneous heat spread. The $90\%$ of tested samples have a delamination defect started at the around of the chip which may be related to heat spread design.

• 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.

• 비파괴검사학회지
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• 제21권6호
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• pp.598-606
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• 2001

최근 전자장치의 고기능화에 따라서 반도체 장치의 고집적화는 물론 반도체 패키지의 박형화 추세에 있다. 이러한 반도체가 장치에 실장된 후에도 안정된 성능을 발휘할 수 있는지 여부에 대한 신뢰성을 보장하기 위해 조립 완료된 반도체 패키지에 대한 preconditioning 시험을 수행하게 된다. 또한 preconditioning 시험 전후에 초음파 주사 현미경을 이용한 검사를 실시함으로써 반도체 패키지에 대한 들뜸이나 패키지 크랙과 같은 내부 결함의 존재 여부를 알아보게 된다. 본 논문에서는 반도체 내부의 결함 유무를 효과적으로 검사할 수 있는 초음파를 이용한 신뢰성 평가 방법과 절차를 제시하고, preconditioning 시험 과정에서 수행되는 시험법을 통해 패키지 내부 결함을 야기하는 가장 중요한 요인이라 할 수 있는 수분에 의한 고장 메커니즘을 분명히 함으로써 반도체 패키지에 대한 일련의 고장 분석 및 신뢰성 평가 방법을 정립하고자 하였다.

• 한국공작기계학회논문집
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• 제15권6호
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• pp.82-87
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• 2006

Availability of defect test algorithm that recognizes exact and standardized defect information in order to fundamentally resolve generated defects in industrial sites by giving artificial intelligence to SAT(Scanning Acoustic Tomograph), which previously depended on operator's decision, to find various defect information in a semiconductor package, to decide defect pattern, to reduce personal errors and then to standardize the test process was verified. In order to apply the algorithm to the lately emerging Neural Network theory, various weights were used to derive results for performance advancement plans of the defect test algorithm that promises excellent field applicability.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 추계학술대회
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• pp.1442-1447
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• 2003

Computer is a very powerful machine which is widely using for data processing, DB construction, peripheral device control, image processing etc. Consequently, many researches and developments have progressed for high performance processing unit, and other devices. Especially, the core units such as semiconductor parts are rapidly growing so that high-integration, high-performance, microminiat turization is possible. The packaging in the semiconductor industry is very important technique to de determine the performance of the system that the semiconductor is used. In this paper, the inspection of the inner defects such as delamination, void, crack, etc. in the semiconductor packages is studied. ESPI which is a non-contact, non-destructive, and full-field inspection method is used for the inner defect inspection and its results are compared with that of C-Scan method.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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• pp.345-350
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• 2002

In this study, researchers classifying the artificial flaws in semiconductor packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method fur entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, the pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 추계학술대회 논문집
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• pp.721-726
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• 2005

In this study, researchers classifying the artificial flaws in semiconductor. packages are performed by pattern recognition technology. For this purposes, image pattern recognition package including the user made software was developed and total procedure including ultrasonic image acquisition, equalization filtration, binary process, edge detection and classifier design is treated by Backpropagation Neural Network. Specially, it is compared with various weights of Backpropagation Neural Network and it is compared with threshold level of edge detection in preprocessing method for entrance into Multi-Layer Perceptron(Backpropagation Neural network). Also, the pattern recognition techniques is applied to the classification problem of defects in semiconductor packages as normal, crack, delamination. According to this results, it is possible to acquire the recognition rate of 100% for Backpropagation Neural Network.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2005년도 춘계학술대회 논문집
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• pp.91-96
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• 2005

In this study, researchers developed the estimative algorithm for artificial defects in semiconductor packages and performed it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Backpropagation Neural Network. Self-Organizing Map and Backpropagation Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages: Crack, Delamination and Normal. According to the results, we were confirmed that estimative algorithm was provided the recognition rates of $75.7\%$ (for Crack) and $83_4\%$ (for Delamination) and $87.2\%$ (for Normal).

• 한국통신학회논문지
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• 제31권12C호
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• pp.1157-1164
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• 2006

본 논문에서는 마이크로 BGA의 패키지와 볼의 정확한 위치와 사이즈를 측정하기 위한 방법을 제안하였다. 정확하게 BGA의 결함을 찾아내기 위해, 패키지와 볼의 위치를 찾아내는데 중점을 두었다. 라벨링한 후, 특징 파라미터를 이용하여 패키지와 볼 성분만을 검출하였다. 패키지 부분을 검출한 후, 패키지에 대한 정보를 입력 파라미터로 사용하여 사각형 모델로 패키지의 사이즈를 측정하였다. 또한 볼 부분을 검출한 후, 볼 부분에 대한 정보를 입력 파라미터로 사용하여 원형 모델로 볼의 위치와 지름을 측정하였다. 실제 길이를 측정하기 위하여 landmark에 근거한 calibration을 수행하였으며 SEM으로 볼을 측정한 데이터를 기준으로 측정치와 비교하였다. 위의 실험으로부터 제안 기법에 의한 볼의 반지름 측정값의 정확도가 평균 94%가 되는 사실을 확인하였다.

• 한국융합학회논문지
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• 제8권11호
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• pp.15-20
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• 2017

최근 IT 산업의 발전으로 다양한 분야에서 컴퓨터 융합 기술에 대한 관심이 높아지고 있다. 특히 반도체 분야에서 생산 공정에 반도체 소자의 결함을 검사하기 위하여 카메라와 컴퓨터를 융합한 비전 시스템을 많이 사용하고 있다. 이러한 영상 관련 시스템들은 데이터를 처리하는 과정에서 열화 현상이 발생하기에 주유한 요인인 잡음을 제거하기 위한 다양한 연구가 이루어지고 있다. 이에 본 논문에서는 BGA 패키지 소자를 대상으로 양산 과정에서 결함을 사전에 인식하여 불량을 검출하기 위하여 영상 데이터의 잡음제거에 많이 사용하고 있는 가우시안 필터, 미디언 필터, 평균 필터를 이용한 측정 시스템을 제안한다. 제안 시스템을 BGA 패키지 생산 공정에 적용하면 신속하게 양품과 불량을 판정할 수 있어 생산성이 향상될 것으로 기대된다.