摘要(英) |
Wafer wire saw machines are used in a link of silicon wafer manufacturing, that saws wafers into individual die. However, while the machine shutdown or the sawing wire broken unexpectedly, that batch of wafers will be secondary products or wasted wafers leading to cost increase. Also, it comes up with a challenging issue - the imbalance dataset. The ratio of normal and abnormal data is 21:1. Therefore, an anomaly detection strategy is proposed, composed of three parts: representation learning methods, supervised classifiers and alarm rules. K-means clustering and autoencoders are the representation learning methods that learn normal features from normal data only, that not merely solves the imbalanced data challenge, but also helps the 4 experimental supervised classifiers: random forest, Naïve Bayes, support vector machine, extreme learning machine perform better, whereas the alarm rules help reduce false alarm. The anomaly detection strategy is evaluated on two machines from a real semiconductor silicon wafer material manufacturing company, where the catching rate is 0.57 and false alarm is 0.10. Moreover, this predictive system has been implemented and tested in production line, and we put forward the considerable engineering profiles that are highly related to the models. |
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