A Novel Filtering Method to Extract Three Critical Yield Loss Components (Gross, Repeated, and Random) FIMER

This paper describes a novel filtering method (FIMER) to extract three critical yield loss components: gross yield loss from parametric problems or from clustering of defects, repeated yield loss from mask defects or from lithography margin, and random yield loss mainly from particles. It is shown by simulation that FIMER is not only superior to the conventional windowing method in extracting repeated yield loss but also accurately extracts gross yield loss and random yield loss. The simulation studies show that the three components are extracted with an error equal to or less than 5% by optimizing threshold and filter weights, which are the major parameters in FIMER.