Process Parameters Optimization: A Design Study for TiO 2 Thin Film of Vacuum Sputtering Process

This paper proposes a procedure for process parameters design by combining both modeling and optimization methods. The proposed procedure integrates the Taguchi method, the artificial neural network (ANN), and the genetic algorithm (GA). First, the Taguchi method is applied to minimize experimental numbers and to collect experimental data representing the quality performances of a system. Next, the ANN is used to build a system model based on the data from the Taguchi experimental method. Then, the GA is employed to search for the optimal process parameters. A process parameters design for a titanium dioxide ( ) thin film in the vacuum sputtering process is studied in this paper. The quality objective is to form a smaller water contact angle on the thin-film surface. The water contact angle is 4 obtained from the system model of the proposed procedure. The process parameters obtained from the proposed procedure were used to conduct the experiment in the vacuum sputtering process for the thin film. The water contact angle given from the practical experiment is 3.93 . The difference percent is 1.75% between 4 and 3.93 . The result obtained from the system model of the proposed procedure is promising. Hence, we can conclude that the proposed procedure is a very good approach in solving the problem of the process parameters design. Note to Practitioners—This paper was motivated by the problem of finding optimal process parameters for the thin film but it also applies to other different processes that need to get better process parameters by experiments. Existing approaches have trial-and-error or experimental design methods. The two methods are timeand cost-consuming, and could not guarantee to find the good process parameters. This paper suggests a procedure by combining both modeling and optimization methods to solve this problem. We employ the Taguchi method, the ANN, and the GA to search for the optimal process parameters for a thin film in the vacuum sputtering process. The result is quite promising. In future research, we will improve the adopted methods and apply the approach to other problems for optimal process parameters design. Please feel free to contact us, if you have any questions about this approach. We will do our best to help you.