Hierarchical indices to detect equipment condition changes with high dimensional data for semiconductor manufacturing
During semiconductor manufacturing process, massive and various types of interrelated equipment data are automatically collected for fault detection and classification. Indeed, unusual wafer measurements may reflect a wafer defect or a change in equipment conditions. Early detection of equipment condition changes assists the engineer with efficient maintenance. This study aims to develop hierarchical indices for equipment monitoring. For efficiency, only the highest level index is used for real-time monitoring. Once the index decreases, the engineers can use the drilled down indices to identify potential root causes. For validation, the proposed approach was tested in a leading semiconductor foundry in Taiwan. The results have shown that the proposed approach and associated indices can detect equipment condition changes after preventive maintenance efficiently and effectively.
KeywordsEquipment condition Tool health Preventive maintenance (PM) Fault detection and classification (FDC) Real-time monitoring Manufacturing intelligence Semiconductor manufacturing
This research was partially supported by National Science Council, Taiwan (NSC100-2628-E-007-017-MY3; NSC102-2622-E-007-013), Taiwan Semiconductor Manufacturing Company (100A0259JC), and National Tsing Hua University under the Toward World-Class University Project (101N2074E1). The authors deeply appreciate constructive comments and suggestions from the anonymous reviewers as well as the supports and inputs of domain experts including Dr. Yi-Chun Chen and Dr. Chun-Ju Wang.
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