Data Mining using Genetic Programming for Construction of a Semiconductor Manufacturing Yield Rate Prediction System

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@Article{Li:2006:JIM,

• author = "Te-Sheng Li and Cheng-Lung Huang and Zong-Yuan Wu",
• title = "Data Mining using Genetic Programming for Construction of a Semiconductor Manufacturing Yield Rate Prediction System",
• journal = "Journal of Intelligent Manufacturing",
• year = "2006",
• volume = "17",
• number = "3",
• pages = "355--361",
• month = jun,
• keywords = "genetic algorithms, genetic programming, Data mining, Feature selection, Yield prediction, Semiconductor manufacturing",
• DOI = "doi:10.1007/s10845-005-0008-7",
• abstract = "he complexity of semiconductor manufacturing is increasing due to the smaller feature sizes, greater number of layers, and existing process reentry characteristics. As a result, it is difficult to manage and clarify responsibility for low yields in specific products. This paper presents a comprehensive data mining method for predicting and classifying the product yields in semiconductor manufacturing processes. A genetic programming (GP) approach, capable of constructing a yield prediction system and performing automatic discovery of the significant factors that might cause low yield, is presented. Comparison with the results then is performed using a decision tree induction algorithm. Moreover, this research illustrates the robustness and effectiveness of this method using a well-known DRAM fab's real data set, with discussion of the results.",

}

Genetic Programming entries for Te-Sheng Li Cheng-Lung Huang Zong-Yuan Wu

Citations