On Defect-Level Estimation and the Clustering Effect

This paper presents a review of existing defect level models and introduces a new defect level model that accounts for the fault clustering effect. The model uses generalized negative binomial statistics to model the probability distribution of the number of faults in a chip. This analysis shows that clustering, in addition to naturally increasing the yield, also raises the detection probability and therefore lowers the defect level. By accounting for clustering, the new model predicts a less stringent fault coverage requirement than other models.