Defect clustering viewed through generalized Poisson distribution
It is shown that generalized double Poisson distributions provide a good basis for yield models when moderate spatial heterogeneity exists between chips of larger sizes, or when defects are almost randomly distributed. The model includes the average number and size of clusters as its parameters. On being tested with simulated as well as actual wafer particle maps, the model gave a significance level >0.95 in most of the cases. This model is simple and facilitates direct implementation of multilevel or hierarchical redundancy in regular VLSI/WSI designs. The strength of the proposed model lies in its simplicity and its ability to provide a physical explanation of the clustering process through its parameters. The model reflects the effects of the competition which can occur among defects in a cluster during wafer processing. Comparisons of yield predictions by various models for wafer maps with different spatial properties are reported. >