Session Abstract

Yield losses causes are usually represented as a mix of factors including process systematic problems, random defectivity and systematic marginalities. The first two components were known as dominant effects in earlier technology nodes, and have been faced and discussed widely in the past. Systematic marginalities instead are often represented as the overlap of two sliding windows. The first one is known as the "process window" which is subject to "natural" process variations or spreads. The second is instead ruled by the product margins and performances. In this context, finding significant correlations is the key problem toward yield learning. The yield learning process works on multiple data sets with potentially huge data volumes, sometimes in presence of incomplete information. Statistical and Data Mining methods are known to be able to effectively deal with this kind of problems.