Incorporating ANNs and statistical techniques into achieving process analysis in TFT-LCD manufacturing industry

Abstract

The ability to improve yield is an important competitiveness determinant for thin-film transistor-liquid crystal displays (TFT-LCD) factories. Until now, few studies were proposed to address the related issues for process analysis in TFT-LCD industry. Therefore, the information (e.g. the domain knowledge or the parameter effect) or the improvement chance hidden from process analysis will be frequently omitted. That is, the yield or yield loss model construction, the critical manufacturing processes (or layers) and the clustering effect based on the abnormal position (or defect) on TFT-LCD glasses will became the important issues to be addressed in TFT-LCD industry. In this study, we proposed an integrated procedure incorporating the data mining techniques, e.g. artificial neural networks (ANNs) and stepwise regression techniques, to achieve the construction of yield loss model, the effect analysis of manufacturing process and the clustering analysis of abnormal position (or it can be viewed as defect) for TFT-LCD products. Besides, an illustrative case owing to TFT-LCD manufacturer at Tainan Science Park in Taiwan will be applied to verifying the rationality and feasibility of our proposed procedure.