Modeling and optimization of ITO/Al/ITO multilayer films characteristics using neural network and genetic algorithm
In this paper, ITO/Al/ITO multilayer films are fabricated with the variations of Al film thickness and annealing temperature. The effects of Al film thickness and annealing temperature on sheet resistance, optical transmittance, and the figure of merit are analyzed in the aid of the artificial neural network (NNet) models. In order to verify the fitness of NNet model, the root mean square error (RMSE) of training and testing data are calculated. The NNet models well represent the measured sheet resistance, optical transmittance, and the figure of merit. After NNet model is established, genetic algorithm (GA) is used to find the optimum process condition for the ITO/Al/ITO multilayer films to obtain maximum figure of merit in the design space.
ito/al/ito multilayer films neural network genetic algorithm figure of merit