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    A genetic algorithm based knowledge discovery system for the design of fluid dispensing processes for electronic packaging

    134799_134799.pdf (214.6Kb)
    Access Status
    Open access
    Authors
    Kwong, C.
    Chan, Kit Yan
    Tsim, Y.
    Date
    2009
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Kwong, Che and Chan, Kit and Tsim, Y. 2009. A genetic algorithm based knowledge discovery system for the design of fluid dispensing processes for electronic packaging. Expert Systems with Applications. 36 (2): pp. 3829-3838.
    Source Title
    Expert Systems with Applications
    DOI
    10.1016/j.eswa.2008.02.041
    ISSN
    09574174
    Faculty
    Curtin Business School
    The Digital Ecosystems and Business Intelligence Institute (DEBII)
    School
    Digital Ecosystems and Business Intelligence Institute (DEBII)
    Remarks

    The link to the journal’s home page is: The link to the journal’s home page is: Copyright © 2009 Elsevier B.V. All rights reserved. Copyright © 2009 Elsevier B.V. All rights reserved

    URI
    http://hdl.handle.net/20.500.11937/12671
    Collection
    • Curtin Research Publications
    Abstract

    In the semiconductor manufacturing industry, fluid dispensing is a very common process used for die-bonding and microchip encapsulation in electronics packaging. Understanding the process behaviour is important as it aids in determining appropriate settings of the process parameters for a high-yield, low cost and robust operation. In this paper, a genetic algorithm (GA) based knowledge discovery system is proposed to discover knowledge about the fluid dispensing process. This knowledge is expressed in the form of rules derived from experimental data sets. As a result, appropriate parameters can be set which will be more effective with respect to the required quality of encapsulation. Rules generated by the GA based knowledge discovery system have been validated using a computational system for process optimization of fluid dispensing. The results indicate that the rules generated are useful and promising in aiding optimization of the fluid dispensing process in terms of better optimization results and shorter computational time.

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