Towards identification of latent defects: Yield mining using defect characteristic model and clustering

Statistical yield modeling is used to calculate the probability of a die containing a latent defect based on its spatial relationship with other dies in its surrounding neighborhood. Previous research implements a blanket application of predictive yield mining on devices and assumes that a spatial relationship exists between killer defects screened at probe test, and latent defects screened at packaged level burn-in. This research investigates the use of the defect characteristic models as yield models to screen latent defects while taking into account wafers with defect clusters. It goes on to evaluate the pre-selected yield models in economic terms and interprets the results as either a predictive or descriptive yield model to describe and identify latent defects.