Parametric Variation or Defects?: Statistical Post-Processing Analysis of Wafer-Sort Data

The subject of this paper is statistical post-processing of wafer-sort test data. Statistical post-processing (SPP) has successfully separated many of the effects of defects from normal wafer-to-wafer variation. The data-driven method is used with parametric data such as IDDQ, minVDD, and others. The neighboring die are used to form an estimate of a die’s expected value. The resulting SPP residual has smaller variance than the original measurement variance and filters most of the spatial patterns that obscure data outliers from normal variation. The method is applicable to a wide variety of process parameter variation issues of concern to both test and FA communities.