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 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.

[1]  Li Pheng Khoo,et al.  Solving the assembly configuration problem for modular products using an immune algorithm approach , 2003 .

[2]  C. J. Wu,et al.  Application of immune algorithm to optimal switching operation for distribution-loss minimisation and loading balance , 2003 .

[3]  Kalyanmoy Deb,et al.  MULTI-OBJECTIVE FUNCTION OPTIMIZATION USING NON-DOMINATED SORTING GENETIC ALGORITHMS , 1994 .

[4]  Ta-Cheng Chen,et al.  Immune algorithms-based approach for redundant reliability problems with multiple component choices , 2005, Comput. Ind..

[5]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[6]  John W. Fowler,et al.  A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines , 2003, Comput. Oper. Res..

[7]  Kurt Hornik,et al.  FEED FORWARD NETWORKS ARE UNIVERSAL APPROXIMATORS , 1989 .

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  Chandrasekharan Rajendran,et al.  A multi-objective simulated-annealing algorithm for scheduling in flowshops to minimize the makespan and total flowtime of jobs , 2005, Eur. J. Oper. Res..

[10]  Shinn-Ying Ho,et al.  Intelligent evolutionary algorithms for large parameter optimization problems , 2004, IEEE Trans. Evol. Comput..

[11]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[12]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[13]  Gary B. Lamont,et al.  Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art , 2000, Evolutionary Computation.

[14]  Dong Hwa Kim,et al.  Intelligent control of nonlinear power plant using immune algorithm based multiobjective optimization , 2004, IEEE International Conference on Networking, Sensing and Control, 2004.

[15]  Alice E. Smith,et al.  Multi-objective tabu search using a multinomial probability mass function , 2006, Eur. J. Oper. Res..

[16]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[17]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[18]  Yu-Lung Lo,et al.  Integrated Taguchi method and neural network analysis of physical profiling in the wirebonding process , 2002 .

[19]  Sang-Yong Jung,et al.  Multisolution optimization of permanent magnet linear synchronous motor for high thrust and acceleration operation , 1999, IEEE International Electric Machines and Drives Conference. IEMDC'99. Proceedings (Cat. No.99EX272).

[20]  P. Hajela,et al.  Immune network simulations in multicriterion design , 1999 .

[21]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[22]  Zbigniew Michalewicz,et al.  Evolutionary Computation 2 , 2000 .