Validation and evaluation for defect-kill-rate and yield estimation models in semiconductor manufacturing

Abstract

This study evaluates the accuracy of two alternative models for the kill rate of different visual defects and the yield estimation by using large amount of practical inspection data (140 6-inch wafers containing 70 560 dies) in semiconductor manufacturing. One model assumed that the visual defects are randomly distributed on a wafer die; another model considered that clustering visual defects may occur on a wafer die. The results show that two models are both capable of predicting the yield precisely. The results also suggest that the model assuming clustering visual defects on a die is more accurate based on the analysis of Taguchi's signal-to-noise (SN) ratio. The same practices can be implemented with other types of wafer such as 8 inch and 12 inch. Precise prediction for the kill rate of different types of visual defects and the yield in the long supply of semiconductor industry is critical since the requirement of material and the reserved capacity can be largely reduced.