Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28173
Title: An orthogonal array based genetic algorithm for developing neural network based process models of fluid dispensing
Authors: Kwong, CK 
Chan, KY
Aydin, ME
Fogarty, TC
Issue Date: 2006
Source: International journal of production research, 2006, v. 44, no. 22, p. 4815-4836
Abstract: Fluid dispensing is a popular process in the semiconductor manufacturing industry, commonly being used in die-bonding as well as microchip encapsulation of electronic packaging. Modelling the fluid dispensing process is important to understanding the process behaviour as well as determining the optimum operating conditions of the process for a high-yield, low-cost and robust operation. In this paper, an approach to integrating neural networks with a modified genetic algorithm is presented to model the fluid dispensing process for electronic packaging. The modified genetic algorithm is proposed by incorporating the crossover operator with an orthogonal array. We compare the modified genetic algorithm with the standard genetic algorithm. The results indicate that a better quality encapsulation can be obtained based on the modified genetic algorithm.
Keywords: Fluid dispensing
Genetic algorithms
Neural networks
Orthogonal array
Publisher: Taylor & Francis
Journal: International journal of production research 
ISSN: 0020-7543
EISSN: 1366-588X
DOI: 10.1080/00207540600620880
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