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