A Two-Step Framework for Predictive Maintenance of Cryogenic Pumps in Semiconductor Manufacturing
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Abstract
Semiconductor manufacturing involves many critical steps, wherein maintaining an ultra-high vacuum is mandatory. To this end, cryogenic pumps are used to create a controlled ultra-low-pressure environment through the use of cryogenic cooling. However, a sudden pump malfunction leads to contamination in the processing chamber, disrupting production. The primary focus of this study is preventing unplanned shutdowns of cryogenic pumps. The data was collected from various pump sensors also known as status variable identification (SVID) that reveals current behavior of the pump. A comprehensive framework is presented here to develop a condition monitoring and fault detection. In the proposed framework, a drift detection method is used for condition monitoring of the pump to locate gradual and abrupt drifts. Additionally, during regeneration (or maintenance) phase, intrinsic features are extracted to distinguish between normal and abnormal regeneration, achieving an accuracy of 90.91% and a precision of 66.67%. Utilizing the proposed system, cryo-pump operators can be given maintenance guidelines and warnings about potential health degradation of the pumps.
How to Cite
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Semiconductor manufacturing, cryogenic pump, anomaly detection, change detection
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