지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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In the PCB(Printed Circuit Board) manufacturing industry, the yield is an important management factor because it affects the product cost and quality significantly. In real situation, it is very difficult to ensure a high yield in a manufacturing shop because products called chips are made through hundreds of nano-scale manufacturing processes. Therefore, in order to improve the yield, it is necessary to analyze main fault process parameters and equipment parameters that cause a low PCB yield. This study extracts the critical equipment parameters that affect the yield through mutual analysis of equipment parameters(x) and the process parameters(y) generated in the processes of PCB manufacturing line. By using the correlation coefficients, the process parameters while minimizing the redundancy between equipment parameters were applied to extract the huristic algorithm critical equipment parameters that have the greatest impact. Also, by applying the GRNN, critical equipment parameters it was confirmed that affect the process parameters. We demonstrated that the test results reflected the real situation of a PCB manufacturing line.
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참고문헌 (14)
참고문헌 신청
Cook, R. D / 1977 / Detection of influential observations in linear regression / Technometrics 19:15~18
De Maesschalck, R. / 2000 / The mahalanobis distance / Chemometrics and Intelligent Laboratory Systems 50(1):1~18
Estevez, P. A. / 2009 / Normalized mutual information feature selection / IEEE Transactions on Neural Networks 20(2):189~201
Guyon, I. / 2003 / An introduction to variable and feature selection / The Journal of Machine Learning Research 3:1157~1182
Ma, M. D. / 2010 / Fault detection based on statistical multivariate analysis and microarray visualization / IEEE Transactions on Industrial Informatics 6(1):18~24
De Maesschalck, R. / 2000 / The mahalanobis distance / Chemometrics and Intelligent Laboratory Systems 50(1):1~18
Estevez, P. A. / 2009 / Normalized mutual information feature selection / IEEE Transactions on Neural Networks 20(2):189~201
Guyon, I. / 2003 / An introduction to variable and feature selection / The Journal of Machine Learning Research 3:1157~1182
Ma, M. D. / 2010 / Fault detection based on statistical multivariate analysis and microarray visualization / IEEE Transactions on Industrial Informatics 6(1):18~24
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