This project is investigating the use of information about defect clustering on wafers to make yield predictions for individual dies based on test results for neighboring dies. The hypothesis is that defects on a wafer are clustered, thus most good dies are close to other good ones and most defective dies are close to others. Clustering information can then be used for targeting particular tests to individual dies on the wafer. Simulations using wafer defect distribution data from the published literature is being employed to assess the viability of the approach. Accurate models to predict defect levels attainable are being developed and strategies for testing dies on the wafer are being determined.
