Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Journal of Intelligent Manufacturing
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In recent years, semiconducting transparent thin films have undergone rapid development. Today, excellent conductivity and transmittance are the qualities sought in the manufacturing of these films. Most manufacturers have the objective of enhancing both conductivity and transmittance, developing a multi-objective model for the prediction of resistivity and transmittance in semiconducting transparent thin films is essential. Taguchi analysis results indicate that among the factors influencing resistivity, radio frequency power (R. F. power) is the most significant, followed by process pressure. Among the factors influencing transmittance, target-to-substrate distance is the most significant, followed by R. F. power. This study proposed a progressive Taguchi-neural network model, combining Taguchi method with an artificial neural network for the development of a multi-objective prediction model for use with sputtered gallium zinc oxide (GZO, ZnO:Ga=97:3 wt%) semiconducting transparent thin films. Analysis results have shown that in the Stage-1 of the initial network, prediction results were ineffective due to insufficient network training examples. The refined network in the Stage-3 however, provided improved global prediction results.