An integrated multi-objective immune algorithm for optimizing the wire bonding process of integrated circuits |
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Authors: | Tung-Hsu Hou Chi-Hung Su Hung-Zhi Chang |
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Affiliation: | (1) Department of Industrial Management, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliu, Yunlin, 640, Taiwan, ROC;(2) Department of Information Management, Chihlee Institute of Technology, 313, Section 1, Wunhua Road, Banciao City, Taipei County, 220, Taiwan |
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Abstract: | 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. |
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Keywords: | Multi-objective immune algorithm (MOIA) Multi-objective evolutionary algorithms (MOEA) Artificial neural networks (ANN) Wire bonding process |
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