Extraction of wafer-level defect density distributions to improve yield prediction

Abstract

Defect density distributions play an important role in process control and yield prediction. To improve yield prediction we present a methodology to extract wafer-level defect density distributions better reflecting such chip-to-chip defect density variations that occur in reality. For that, imaginary wafermaps are generated for a variety of different chip areas to calculate a yield-to-area dependency. Based on these calculations a micro density distribution (MDD) will be determined for each wafer that reflects the degree of defect clustering. The single MDD's per wafer may be summarized to also provide a total defect density distribution per lot or any other sample size. Furthermore, the area needed for defect inspection may be reduced to just a fraction of each wafer which reduces time and costs of data collection and analysis.