Abstract
Test vehicles are commonly used to understand the characteristics of a new process node. The ability to precisely identify and isolate defects is a key requirement during yield learning on these vehicles. Efficiently utilizing the fanouts in a design is critical to get a smaller pfa area. In this talk, we introduce the concept High Observability Patterns. High observability patterns target the detection of a defect in multiple patterns using multiple and different observe points. This allows the diagnostics engine to more precisely identify where the defect is and thus reducing the PFA area.