Modeling of epoxy dispensing process using a hybrid fuzzy regression approach

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  author =       "Kit Yan Chan and C. K. Kwong",
  title =        "Modeling of epoxy dispensing process using a hybrid
                 fuzzy regression approach",
  journal =      "The International Journal of Advanced Manufacturing
  year =         "2013",
  volume =       "65",
  number =       "1-4",
  pages =        "589--600",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, Fuzzy
                 regression, Epoxy dispensing, Microchip encapsulation,
                 Electronic packaging, Process modelling, Semiconductor
  language =     "English",
  publisher =    "Springer-Verlag",
  ISSN =         "0268-3768",
  URL =          "",
  DOI =          "doi:10.1007/s00170-012-4202-4",
  size =         "12 pages",
  abstract =     "In the semiconductor manufacturing industry, epoxy
                 dispensing is a popular process commonly used in
                 die-bonding as well as in microchip encapsulation for
                 electronic packaging. Modelling the epoxy dispensing
                 process is important because it enables us to
                 understand the process behaviour, as well as determine
                 the optimum operating conditions of the process for a
                 high yield, low cost, and robust operation. Previous
                 studies of epoxy dispensing have mainly focused on the
                 development of analytical models. However, an
                 analytical model for epoxy dispensing is difficult to
                 develop because of its complex behaviour and high
                 degree of uncertainty associated with the process in a
                 real-world environment. Previous studies of modelling
                 the epoxy dispensing process have not addressed the
                 development of explicit models involving high-order and
                 interaction terms, as well as fuzziness between process
                 parameters. In this paper, a hybrid fuzzy regression
                 (HFR) method integrating fuzzy regression with genetic
                 programming is proposed to make up the deficiency. Two
                 process models are generated for the two quality
                 characteristics of the process, encapsulation weight
                 and encapsulation thickness based on the HFR,
                 respectively. Validation tests are performed. The
                 performance of the models developed based on the HFR
                 outperforms the performance of those based on
                 statistical regression and fuzzy regression.",
  bibsource =    "OAI-PMH server at",
  oai =          "",

Genetic Programming entries for Kit Yan Chan Che Kit Kwong