Product Quality Prediction using Alarm data : Application to the Semiconductor manufacturing process

In the complex manufacturing processes, high quantity of products might be rejected. This can be due to the no detected failures. To evaluate the processing of manufacturing steps, alarms are setting off to indicate failures. However, industrial plant operators often receive many more alarms than they can manage, which include correlation. A poor alarm system may cause nuisance alarms and thus alarm floods, which reduces the ability of operators to take actions. This paper aims to identify unnecessary alarms within a large amount of event data. We prove the equivalence between similarity approaches in case of sparse binary data. The second purpose of this paper is the product quality prediction based on historical alarm events by using a regularized regression method. To demonstrate the effectiveness of these tools and their utility in the product quality prediction, we present an industrial case study based on alarm and scrap data collected from a semiconductor manufacturing process. Application results show the practicality and utility of the proposed methodology for both alarm management and product quality prediction.