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.

Growth of silicon nitride film by controlling plasma ion energy and duty ratio at room temperature

“…Plasma enhanced chemical vapor deposition (PECVD) systems have been widely used to deposit SiN films in SiH 4 -NH 3 [1,2], SiH 4 -N 2 [3,4], and SiH 4 -N 2 -NH 3 [5,6] discharges as well as other materials [7]. Due to a reduction of particles in the gas phase, a high deposition rate, and a reduced substrate heating, pulsed (P) plasma-applied deposition has been used for SiN deposition [4,8,9].…”