Use of neural network to characterize a low pressure temperature effect on refractive property of silicon nitride film deposited by PECVD

Using a neural network, a refractive index (RI) of silicon nitride film was predicted as a function of process parameters, including radio frequency (RF) power, pressure, substrate temperature, and SiH₄, NH₃, and N₂ flow rates. The film was deposited by a plasma-enhanced chemical vapor deposition (PECVD) system. The PECVD process was characterized by a 2^6-1 fractional factorial experiment. Particular emphasis was placed on examining temperature effects at low pressure. Model prediction accuracy was optimized as a function of training factors. Predicted parameter effects were experimentally validated. Plots generated from an optimized model were used to qualitatively estimate deposition mechanisms. It is noticeable that under various plasma conditions, the RI varied little with the temperature. The temperature effect was extremely sensitive to the pressure level. Enhanced ion bombardment at high temperatures yielded a Si-rich film. Effect of each gas was little affected by the temperature. The SiH₄ flow rate played the most significant role in determining the RI at low pressure.