Abstract
The manufacturing technology in the Thin Films Transistor-Liquid Crystal Display (TFT-LCD) industry has entered a new generation. Due to the short product cycle time, companies with the ability of maximizing the efficiency to improve yield during new product pilot run are most likely to have the key competitive advantage. However, many hidden factors in the advanced manufacturing processes could cause defects on the panel and result in low product quality. Simply relying on the domain knowledge or rules of thumb is unable to clarify the root causes of quality problems effectively. This study applies data mining techniques for the detection of large-sized TFT-LCD Frame Mura defects issue, and proposes a general procedure for attribute selection. Four data mining techniques, including back-propagation neural network (BPNN), decision tree (DT), support vector machine (SVM), and rough set theory (RST), are used to select the important attributes from the data to detect Frame Mura defects. In the end, we aggregate the selected frequency of each attribute to determine the relative importance of attributes and provide improvement priorities for the decision makers. The proposed process was employed to analyze the manufacturing data of a TFT-LCD company in Taiwan. The implementation results showed that five key attributes were identified from the original data and the reduced model still retained the ability to perform well in classification, which demonstrated the effectiveness of our proposed procedure.