This paper proposes to develop a data-driven via＇s depth estimator of the deep reactive ion etching process based on statistical identification of key variables.Several feature extraction algorithms are presented to reduce the high-dimensional data and effectively undertake the subsequent virtual metrology（VM） model building process.With the available on-line VM model,the model-based controller is hence readily applicable to improve the quality of a via＇s depth.Real operational data taken from a industrial manufacturing process are used to verify the effectiveness of the proposed method.The results demonstrate that the proposed method can decrease the MSE from 2.2×10^（-2） to 9×10^（-4） and has great potential in improving the existing DRIE process.
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.
