Computational intelligence approach in optimization of a nanotechnology process

Computational intelligence has been widely adapted in various fields and has been demonstrated excellent performances in solving optimization problems. This study is proposing the implementation of gravitational search algorithm (GSA) in the parameter optimization of RF magnetron sputtering process. RF magnetron sputtering is a nanotechnology process which involves the deposition of nano-scaled atoms of a target material. The current practice of searching for the optimized parameters in the magnetron sputtering process is based on the trial and error method. However, this conventional method has been reported to be time consuming and costly. GSA is proposed to identify the most optimized parameter combination for producing the desirable zinc oxide (ZnO) thin film electrical property. GSA is a population based algorithm which is based on the Newton´s law of gravity and the law of motion. This study is concentrating on three magnetron sputtering process parameters, which are RF power, oxygen flow rate and substrate temperature. These three process parameters are among the sputtering process parameters that have been extensively studied by the researchers for the fabrication of the nanostructured ZnO thin film. The result from GSA optimization had showed that the algorithm performance was acceptable in optimizing the parameter combination from the set of parameters. Based on the GSA acceptable performance, it is expected that this technique could serve as an improvement from the traditional practice in the fabrication process.