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In this paper, we focus on yield analysis task where engineers identify the cause of failure from wafer failure map patterns and manufacturing histories. We organize yield analysis task into the following three stages, namely, failure map pattern monitoring, failure cause identification, and failure recurrence monitoring, and incorporate machine learning and data mining technologies into each stage to support engineers’ work. The important point is that big data analysis enables comprehensive and long-term monitoring automation. We make use of fast and scalable methods of clustering and pattern mining and realize daily comprehensive monitoring with massive manufacturing data. We also apply deep learning, which has been an innovative core technology of machine learning in recent years, to classification of wafer failure map patterns, and explore its performance in detail. Finally, these machine learning and data mining techniques are integrated into an automated monitoring system with interfaces familiar to engineers to attain large yield enhancement. [ABSTRACT FROM AUTHOR]