Defect clustering viewed through generalized Poisson distribution
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abstract
As integrated circuits (IC) continue to grow in area and density, one of the issues of concern has been that of overcoming the presence of defect clusters by incorporating an adequate amount of redundancy in the designs. Consequently, the issues of including the distribution of clusters and their sizes in IC yield models have gained prime importance. In the past, clustering has conventionally been viewed as arising from defect density variations between the chips across a wafer and from one wafer to another. However, when chips become larger, more than one cluster can be expected to lie on a single chip and, as a consequence, defect density variations may be apparent not between the chips but within the chips. It is shown in this paper that generalized Poisson distributions can provide effective yield models in situations as above. A yield model based on generalized double Poisson distribution is presented. 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. The model is simple and facilitates direct implementation of multilevel or hierarchical redundancy in regular VLSI/WSI designs. Finally, comparisons of yield predictions by various models for wafer maps with different spatial properties are reported. 1992 IEEE
