Adaptive network-based fuzzy inference model of plasma enhanced chemical vapor deposition process

In this study, a prediction model of plasma enhanced chemical deposition (PECVD) data was constructed by using an adaptive network-based fuzzy inference system (ANFIS). The PECVD process was characterized by means of a Box Wilson statistical experiment. The film characteristics modeled are deposition rate and stored charge. The prediction performance of ANFIS models was evaluated as a function of training factors, including the step-size, type of membership functions, and normalization factor of inputs-output pairs. The effects of each training factor were sequentially optimized. The root mean square errors of optimized deposition rate and charge models were 11.94 angstrom/min and 1.37 x 10(12)/cm(2), respectively. Compared to statistical regression models, ANFIS models yielded an improvement of more than 20%. This indicates that ANFIS can effectively capture nonlinear plasma dynamics.