Automatic classification of spatial signatures on semiconductor wafermaps

This paper describes Spatial Signature Analysis (SSA), a cooperative research project between SEMATECH and Oak Ridge National Laboratory for automatically analyzing and reducing semiconductor wafermap defect data to useful information. Trends toward larger wafer formats and smaller critical dimensions have caused an exponential increase in the volume of visual and parametric defect data which must be analyzed and stored, therefore necessitating the development of automated tools for wafer defect analysis. Contamination particles that did not create problems with 1 micron design rules can now be categorized as killer defects. SSA is an automated wafermap analysis procedure which performs a sophisticated defect clustering and signature classification of electronic wafermaps. This procedure has been realized in a software system that contains a signature classifier that is user-trainable. Known examples of historically problematic process signatures are added to a training database for the classifier. Once a suitable training set has been established, the software can automatically segment and classify multiple signatures form a standard electronic wafermap file into user-defined categories. It is anticipated that successful integration of this technology with other wafer monitoring strategies will result in reduced time-to-discovery and ultimately improved product yield