An integrated multi-objective immune algorithm for optimizing the wire bonding process of integrated circuits
| Autor: | Chi-Hung Su, Hung-Zhi Chang, Tung-Hsu (Tony) Hou |
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| Rok vydání: | 2008 |
| Předmět: |
Mathematical optimization
Wire bonding Engineering Fitness function Optimization problem Artificial neural network business.industry Process (computing) Integrated circuit Industrial and Manufacturing Engineering law.invention Artificial Intelligence law Metric (mathematics) Convergence (routing) business Algorithm Software |
| Zdroj: | Journal of Intelligent Manufacturing. 19:361-374 |
| ISSN: | 1572-8145 0956-5515 |
| DOI: | 10.1007/s10845-008-0088-2 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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