Process optimization based on neural network model and orthogonal arrays

This paper presents a systematic and cost-effective approach for process optimization with minimal experimental runs. Based on the experimental design scheme of orthogonal arrays, artificial neural network is used to establish the process model. Moreover, Taguchi-genetic algorithm (TGA) is used to search for the global optimum of the fabrication conditions. The procedure starts planning and conducting the initial experiment with fewer levels. By adding experimental points selected from augmented orthogonal arrays, the process model is corrected. This step is continued until the termination condition has been reached. Then, the optimum given by Taguchi-genetic algorithm is the final solution. The proposed approach provides an effective and economical solution for process optimization.