Abstract
The environment within the Semiconductor Industry is one that is made up of rapidly changing technologies, complicated product groups, extensive production hours. The following points have become crucial issues for industrial application and academic research within the Semiconductor Industry: methods by which accurate processing parameters may be provided for engineers to confirm to, within an automation system during mass production; methods by which abnormal wafers may be instantly tracked and analyzed, based on the problems/defects of each individual case; methods by which obtained data may be appropriately processed, by which affected products may be managed through a report chart, and by which yield loss of products may be lessened. This study will focus on the Apriori Algorithm used in Data Mining Technologies, and will incorporate working knowledge of the Semiconductor Industry, to search for, and to develop the solution used to track existing problems within the fabrication process. The Decision Tree analysis method will also be used to categorize and differentiate possible problem sources. This study will also configure an initial prototype of a data mining application system to be used within the Semiconductor fabrication process. The Data Mining application system produced from combining this prototype with the automation system currently used for Semiconductor Fabs will be set forth as a feasible example in this study, and comprehensive data will be filtered, calculated, and modeling, based upon data mining methods and procedures. Research results of this study will be classified and organized, and a goal for future studies will be examined.