• DocumentCode
    3579826
  • Title

    The Application of the Improved RBF in the Semiconductor Manufacturing System Feeding Control

  • Author

    Yuemei Sun ; Jianfeng Lu ; Chaochao Wang

  • Author_Institution
    Sch. of Electron. & Inf. Eng., Tongji Univ., Shanghai, China
  • Volume
    1
  • fYear
    2014
  • Firstpage
    241
  • Lastpage
    244
  • Abstract
    According to the complexity of the semiconductor manufacturing processes, a RBF neural network improved by GA is put forward to apply in the semiconductor production modeling and predict the line performance indexes. The improved RBF algorithm can play better role in dealing with the dynamic real-time data from the line, building predictive models to describe line dynamic behavior, and presenting future outputs based on the current inputs accurately. In this paper, the improved RBF algorithm and the traditional RBF neural network are performed respectively to realize the line modeling simulation. Finally, the simulation result shows the effectiveness of this improved algorithm.
  • Keywords
    control engineering computing; control engineering education; genetic algorithms; manufacturing systems; neural nets; production engineering computing; semiconductor industry; RBF neural network; dynamic real-time data; improved RBF application; line dynamic behavior; semiconductor manufacturing processes; semiconductor manufacturing system feeding control; semiconductor production modeling; Biological cells; Genetic algorithms; Job shop scheduling; Radial basis function networks; Real-time systems; Vectors; RBF; genetic algorithm; performance prediction; semiconductor manufacturing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Design (ISCID), 2014 Seventh International Symposium on
  • Print_ISBN
    978-1-4799-7004-9
  • Type

    conf

  • DOI
    10.1109/ISCID.2014.157
  • Filename
    7064182