Modeling of epoxy dispensing process using a hybrid fuzzy regression approach

Kit Yan Chan, Chun Kit Kwong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)


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. Modeling the epoxy dispensing process is important because it enables us to understand the process behavior, 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 behavior and high degree of uncertainty associated with the process in a real-world environment. Previous studies of modeling 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.
Original languageEnglish
Pages (from-to)589-600
Number of pages12
JournalInternational Journal of Advanced Manufacturing Technology
Issue number1-4
Publication statusPublished - 1 Mar 2013


  • Electronic packaging
  • Epoxy dispensing
  • Evolutionary computation
  • Fuzzy regression
  • Genetic programming
  • Microchip encapsulation
  • Process modeling
  • Semiconductor manufacturing

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Control and Systems Engineering
  • Computer Science Applications
  • Software
  • Mechanical Engineering


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