This paper is proposing an Artificial Intelligence (AI) technique in solving the RF magnetron sputtering process parameter optimization problem. RF magnetron sputtering is a physical vapor deposition process which is widely used in the manufacturing of thin films. In this research, the optimization of the sputtering process parameters is to be solved computationally based on gravitational search algorithm (GSA).This study is concentrating on four process parameters of RF magnetron sputtering process, which are RF power, deposition time, oxygen flow rate and substrate temperature. As for the material, zinc oxide (ZnO) has been chosen due to its many significance characteristics. For the validation purpose, GSA performance was compared with particle swarm optimization (PSO). Based on the results, GSA has outperformed PSO in terms of the accuracy of the optimization performance, fitness value and processing time. The results showed that the AI approach in solving this nano-process parameter optimization problem has proven to be promising. This AI approach is expected to improve the trial and error method by reducing the number of experiments to be conducted in the parameter optimization process. The implementation of this computational technique could offer better time management and lower cost consumption in the thin film fabrication process. 

MITC2015-pp 304-305