Modeling of epoxy dispensing process using a hybrid fuzzy regression approach

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{chan:2013:IJAMT,

• 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 Technology",
• 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 manufacturing",
• language = "English",
• publisher = "Springer-Verlag",
• ISSN = "0268-3768",
• URL = "http://espace.library.curtin.edu.au:80/R?func=dbin-jump-full&local_base=gen01-era02&object_id=185726",
• 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 espace.library.curtin.edu.au",
• oai = "oai:espace.library.curtin.edu.au:185726",

}

Genetic Programming entries for Kit Yan Chan Che Kit Kwong

Citations