To be productive and profitable in a modern semiconductor fabrication environment, large amounts of manufacturing data must be collected, analyzed, and maintained. This includes data collected from in- and off-line wafer inspection systems and from the process equipment itself. This data is increasingly being used to design new processes, control and maintain tools, and to provide the information needed for rapid yield learning and prediction. Because of increasing device complexity, the amount of data being generated is outstripping the yield engineer's ability to effectively monitor and correct unexpected trends and excursions. The 1997 SIA National Technology Roadmap for Semiconductors highlights a need to address these issues through "automated data reduction algorithms to source defects from multiple data sources and to reduce defect sourcing time." SEMATECH and the Oak Ridge National Laboratory have been developing new strategies and technologies for providing the yield engineer with higher levels of assisted data reduction for the purpose of automated yield analysis. In this article, we will discuss the current state of the art and trends in yield management automation.