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http://dx.doi.org/10.5351/KJAS.2021.34.1.099

A case study on the application of process abnormal detection process using big data in smart factory  

Nam, Hyunwoo (Department of Applied Statistics, Gachon University)
Publication Information
The Korean Journal of Applied Statistics / v.34, no.1, 2021 , pp. 99-114 More about this Journal
Abstract
With the Fourth Industrial Revolution based on new technology, the semiconductor manufacturing industry researches various analysis methods such as detecting process abnormalities and predicting yield based on equipment sensor data generated in the manufacturing process. The semiconductor manufacturing process consists of hundreds of processes and thousands of measurement processes associated with them, each of which has properties that cannot be defined by chemical or physical equations. In the individual measurement process, the actual measurement ratio does not exceed 0.1% to 5% of the target product, and it cannot be kept constant for each measurement point. For this reason, efforts are being made to determine whether to manage by using equipment sensor data that can indirectly determine the normal state of each step of the process. In this study, the Functional Data Analysis (FDA) was proposed to define a process abnormality detection process based on equipment sensor data and compensate for the disadvantages of the currently applied statistics-based diagnosis method. Anomaly detection accuracy was compared using machine learning on actual field case data, and its effectiveness was verified.
Keywords
smart factory; semiconductor manufacturing process; equipment sensor data; abnormality detection process; functional data analysis;
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