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Wafer Bin Maps (WBMs) are important for yield improvement to trace root causes. The characteristic of WBMs patterns are formed by processes, so process engineers can collect clues from the patterns and correlate them with specific processes, and this can save much time and efforts in finding the root causes. However, the existing learning algorithms have the main shortage of product dependency. For this reason, this work adopted a supervised learning methodology to develop an on-line WBMs pattern recognition system that maps WBMs into 70×70 binary images to solve this issue. Furthermore, this work also proposed a learning scheme to recognize repeating failures that are usually viewed as random pattern in the existing approaches.