Spatial defect pattern recognition on semiconductor wafers using model-based clustering and Bayesian inference

Defects on semiconductor wafers tend to cluster and the spatial defect patterns contain useful information about potential problems in the manufacturing process. This study proposes to use model-based clustering algorithms via Bayesian inferences for spatial defect pattern recognition on semiconductor wafers. These new algorithms can find the number of defect clusters as well as identify the pattern of each cluster automatically. They are capable of detecting curvilinear patterns, ellipsoidal patterns and nonuniform global defect patterns. Promising results have been obtained from simulation studies.