An integrated multi-objective immune algorithm for optimizing the wire bonding process of integrated circuits

Optimization of the wire bonding process of an integrated circuit (IC) is a multi-objective optimization problem (MOOP). In this research, an integrated multi-objective immune algorithm (MOIA) that combines an artificial immune algorithm (IA) with an artificial neural network (ANN) and a generalized...

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Bibliographic Details
Published in:Journal of intelligent manufacturing Vol. 19; no. 3; pp. 361 - 374
Main Authors: Hou, Tung-Hsu, Su, Chi-Hung, Chang, Hung-Zhi
Format: Journal Article
Language:English
Published: Boston Springer US 01.06.2008
Springer Nature B.V
Subjects:
Algorithms
Back propagation
Biology
Business and Management
Control
Fuzzy sets
Genetic algorithms
Immune system
Immunology
Input output
Integrated approach
Integrated circuits
Machine learning
Machines
Manufacturing
Mechatronics
Neural networks
Optimization
Packaging
Pareto optimum
Processes
Production
Robotics
Studies
Wire
Taiwan
Multi-objective immune algorithm (MOIA)
Wire bonding process
Multi-objective evolutionary algorithms (MOEA)
Artificial neural networks (ANN)
ISSN:0956-5515, 1572-8145
Online Access:Get full text
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Summary:Optimization of the wire bonding process of an integrated circuit (IC) is a multi-objective optimization problem (MOOP). In this research, an integrated multi-objective immune algorithm (MOIA) that combines an artificial immune algorithm (IA) with an artificial neural network (ANN) and a generalized Pareto-based scale-independent fitness function (GPSIFF) is developed to find the optimal process parameters for the first bond of an IC wire bonding. The back-propagation ANN is used to establish the nonlinear multivariate relationships between the wire boning parameters and the multi-responses, and is applied to generate the multiple response values for each antibody generated by the IA. The GPSIFF is then used to evaluate the affinity for each antibody and to find the non-dominated solutions. The “Error Ratio” is then applied to measure the convergence of the integrated approach. The “Spread Metric” is used to measure the diversity of the proposed approach. Implementation results show that the integrated MOIA approach does generate the Pareto-optimal solutions for the decision maker, and the Pareto-optimal solutions have good convergence and diversity performance.
Bibliography:SourceType-Scholarly Journals-1
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ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-008-0088-2