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Clustering Mixed Data Using Non-normal Regression Tree for Process Monitoring

Authors: Youngji Yoo, Jun Seok Kim, Cheong-Sool Park, Jun-Geol Baek, Sung-Shick Kim, Young-Hak Lee

Abstract:

In the semiconductor manufacturing process, large amounts of data are collected from various sensors of multiple facilities. The collected data from sensors have several different characteristics due to variables such as types of products, former processes and recipes. In general, Statistical Quality Control (SQC) methods assume the normality of the data to detect out-of-control states of processes. Although the collected data have different characteristics, using the data as inputs of SQC will increase variations of data, require wide control limits, and decrease performance to detect outof- control. Therefore, it is necessary to separate similar data groups from mixed data for more accurate process control. In the paper, we propose a regression tree using split algorithm based on Pearson distribution to handle non-normal distribution in parametric method. The regression tree finds similar properties of data from different variables. The experiments using real semiconductor manufacturing process data show improved performance in fault detecting ability.

Keywords: Clustering, Semiconductor, regression tree, non-normal mixed process data, Statistical Quality Control (SQC), Pearson distribution system

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1329428

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