Model-based clustering for integrated circuit yield enhancement

This paper studies the defect data analysis method for semiconductor yield enhancement. Given the defect locations on a wafer, the local defects generated from the assignable causes are classified from the global defects generated from the random causes by model-based clustering, and the clustering methods can identify the characteristics of local defect clusters. The information obtained from this method can facilitate process control, particularly, root-cause analysis. The global defects are modeled by the spatial non-homogeneous Poisson process, and the local defects are modeled by the bivariate normal distribution or by the principal curve.