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Fault Detection, Diagnosis, and Optimization of Wafer Manufacturing Processes utilizing Knowledge Creation

Hyeon Bae, Sungshin Kim*, Kwang-Bang Woo, Gary S. May, and Duk-Kwon Lee
International Journal of Control, Automation, and Systems, vol. 4, no. 3, pp.372-382, 2006

Abstract : The purpose of this study was to develop a process management system to manage ingot fabrication and improve ingot quality. The ingot is the first manufactured material of wafers. Trace parameters were collected on-line but measurement parameters were measured by sampling inspection. The quality parameters were applied to evaluate the quality. Therefore, preprocessing was necessary to extract useful information from the quality data. First, statistical methods were used for data generation. Then, modeling was performed, using the generated data, to improve the performance of the models. The function of the models is to predict the quality corresponding to control parameters. Secondly, rule extraction was performed to find the relation between the production quality and control conditions. The extracted rules can give important information concerning how to handle the process correctly. The dynamic polynomial neural network (DPNN) and decision tree were applied for data modeling and rule extraction, respectively, from the ingot fabrication data.

Keyword : Data mining, data model, knowledge creation, process optimization, rule extraction, wafer fabrication.

 
 
 
 
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