Use of a neural network to characterize the charge density of PECVD-silicon nitride films

Abstract

Silicon nitride (SiN) films were deposited using a plasma-enhanced chemical vapor deposition system (PECVD). The charge density of the SiN films was modeled using a generalized regression neural network (GRNN). The PECVD process was characterized by means of a face-centered Box Wilson experiment. The prediction performance of the GRNN model was optimized using a genetic algorithm (GA). The GA-GRNN model significantly improved the GRNN prediction performance by more than 55%. The optimized GA-GRNN model was used to investigate the effects of various parameters on the charge density. A higher charge density was obtained at higher temperatures (i.e. a lower H concentration). Increasing the pressure increased the charge density at all temperatures levels with a much stronger impact at a lower H concentration. The effects of the SiH₄ and N₂ (or NH₃) flow rates on the charge density were similar in that a higher charge density was achieved at a lower Si−N ratio (N-rich films). A considerable increase in the charge density with a radio frequency power at a lower NH₃ flow rate was attributed to the generation of more Si−H than N−H bonds.