• DocumentCode
    3116580
  • Title

    Determination of process conditions of epoxy dispensing processes using a genetic algorithm based neural fuzzy networks

  • Author

    Chan, K.Y. ; Dillon, T.S. ; Ling, S.H. ; Kwong, C.K.

  • Author_Institution
    Digital Ecosyst. & Bus. Intell. Inst., Curtin Univ. of Technol., Perth, WA, Australia
  • fYear
    2011
  • fDate
    27-30 June 2011
  • Firstpage
    2253
  • Lastpage
    2260
  • Abstract
    In this paper, process conditions of epoxy dispensing processes are determined by the proposed genetic algorithm based neural fuzzy networks, which consists of two tasks: a) the approach of neural fuzzy networks, which was shown to be better than the other existing approaches, is proposed to develop models in relating between process parameters and quality characteristics for the epoxy dispensing processes; b) the approach of genetic algorithm is used to determine process parameters with respect to pre-defined quality requirements based on the developed neural fuzzy network models. The results indicate that, based on the proposed genetic algorithm based neural fuzzy network, estimated process parameters can achieve specified requirements of microchip encapsulations with high and robust qualities.
  • Keywords
    chip-on-board packaging; encapsulation; fuzzy neural nets; genetic algorithms; parameter estimation; epoxy dispensing process; genetic algorithm; microchip encapsulations; neural fuzzy network models; process conditions; process parameter estimation; Biological system modeling; Encapsulation; Genetic algorithms; Mathematical model; Modeling; Neural networks; Optimization; Epoxy dispensing process; microchip encapsulation; neural fuzzy networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems (FUZZ), 2011 IEEE International Conference on
  • Conference_Location
    Taipei
  • ISSN
    1098-7584
  • Print_ISBN
    978-1-4244-7315-1
  • Electronic_ISBN
    1098-7584
  • Type

    conf

  • DOI
    10.1109/FUZZY.2011.6007321
  • Filename
    6007321