Using neural networks and immune algorithms to find the optimal parameters for an IC wire bonding process

The wire bonding process is the key process in an IC chip-package. It is an urgent problem for IC chip-package industry to improve the wire bonding process capability. In this study, an integration of artificial neural networks (ANN) with artificial immune systems (AIS) is proposed to optimize parameters for an IC wire bonding process. The algorithm of AIS with memory cell and suppressor cell mechanisms is developed. The back-propagation ANN is used to establish the nonlinear multivariate relationships between the wire boning parameters and responses. Then a Taguchi method is applied to identify the critical parameters of AIS. Finally, the AIS algorithm is applied to find the optimal parameters by using the output of ANN as the affinity measure. A comparison between the result of the proposed AIS and that of a genetic algorithm (GA) is conducted in this study. The comparison shows that the searching quality of the proposed AIS is more effective than the GA in finding the optimal wire bonding process parameters.