Development of a virtual metrology for high-mix TFT-LCD manufacturing processes

Abstract

Nowadays, TFT-LCD manufacturing has become a very complex process, in which many different products being manufactured with many different tools. The ability to predict the quality of product in such a high-mix system is critical to developing and maintaining a high yield. In this paper, a statistical method is proposed for building a virtual metrology model from a number of products using a high-mix manufacturing process. Stepwise regression is used to select “key variables" that really affect the quality of the products. Multivariate analysis of covariance is also proposed for simultaneously applying the selected variables and product effect. This framework provides a systematic method of building a processing quality prediction system for a high-mix manufacturing process. The experimental results show that the proposed quality prognostic system can not only estimate the critical dimension accurately but also detect potentially faulty glasses.