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자기조직화 지도를 이용한 반도체 패키지 내부결함의 패턴분류 알고리즘 개발
김재열,윤성운,김훈조,김창현,송경석,양동조 한국공작기계학회 2002 한국공작기계학회 추계학술대회논문집 Vol.2002 No.-
In this study, researchers developed the est algorithm for artificial defects in the semic packages and performed to it by pattern recogn technology. For this purpose, this algorithm was i that researcher made software with matlab. The so consists of some procedures including ultrasonic acquisition, equalization filtering, self-organizi backpropagation neural network, self-organizing ma backpropagation neural network are belong to metho neural networks. And the pattern recognition tech has applied to classify three kinds of detective pa semiconductor packages, that is, crack, delaminat normal. According to the results, it was found estimative algorithm was provided the recognition r 75.7% (for crack) and 83.4% (for delamination) 87.2%(for normal).
자기조직화 지도를 이용한 반도체 패키지 내부결함의 패턴분류 알고리즘 개발
김재열,윤성운,김훈조,김창현,양동조,송경석 한국공작기계학회 2003 한국생산제조학회지 Vol.12 No.2
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).