Wafer Map Failure Pattern Recognition and Similarity Ranking for Large-Scale Data Sets

Wafer maps can exhibit specific failure patterns that provide crucial details for assisting engineers in identifying the cause of wafer pattern failures. Conventional approaches of wafer map failure pattern recognition (WMFPR) and wafer map similarity ranking (WMSR) generally involve applying raw wafer map data (i. e., without performing feature extraction). However, because increasingly more sensor data are analyzed during semiconductor fabrication, currently used approaches can be inadequate in processing large-scale data sets. Therefore, a set of novel rotation- and scale-invariant features is proposed for obtaining a reduced representation of wafer maps. Such features are crucial when employing WMFPR and WMSR to analyze large-scale data sets. To validate the performance of the proposed system, the world's largest publicly accessible data set of wafer maps was built, comprising 811 457 real-world wafer maps. The experimental results show that the proposed features and overall system can process large-scale data sets effectively and efficiently, thereby meeting the requirements of current semiconductor fabrication.