From Alarm System Events Towards Quality Inspection of The Final Product: Application to a Semiconductor Industry

Process diagnostic and monitoring during production is a fundamental task of the control and alarm system. However, many defected products are still related to various issues of health states of production equipment. Therefore, quality inspection is a crucial step during the manufacturing process, ensuring that a product's quality is maintained or improved with a reduced or total absence of errors. The final product quality determines whether or not a product unit satisfies its intended use. In this paper, we propose a final quality inspection framework based on alarm events data. In this framework, we first transform the textual alarm data into numeric using binary scoring. Then, we reduce the dimension of the obtained numeric matrix using an appropriate alarms grouping method. After that, we apply the reduced data to learn a classifier and to make a decision. Finally, we compare several machine learning algorithms' performance in the prediction of scrap-per-lot, namely Decision Tree, Logistic Regression, K-nearest neighbors, Linear Support Vector Machine, and Multi-Layer Perceptron. The results show a satisfactory performance of the compared models that we effectively prove on a dataset collected over the whole semiconductor fabrication facility.