| 摘要(英) |
In the manufacturing process of TFT-LCD, the flat panel ranking mostly depends
on human’s diagnosis. The output of flat panels increases because of the
establishment of new generation factories and also because of the change of the
market for more small-sized panels. As the result, more manpower is needed to handle
the diagnostic jobs, and it is the manufacturer’s desire to figure out a way to provide
an appropriate solution. The purpose of this research is to apply data mining
technologies to propose an alternative that uses less manpower and satisfies the job
requirement.
The electrical characteristics of flat panels are measured and recorded in the
manufacture process, but there are so many variables that it is not easy to find out the
causes of poor quality. In this research, the goal is to replace manpower with
automatically measured data and to create classification rules without incurring
addition cost. Several models, including C5.0 decision trees, logistic regression, and
Neural Network are considered in this study. The comparison results show that
decision tree (C5.0) can achieve a higher accurate rate in ranking the panels. To apply
the proposed model, the required manpower could be reduced, and still, satisfies the
diagnosis job for ranking panels. |
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