Prediction of radio frequency power effect on silicon nitride deposition using a genetic algorithm based neural network

Deposition rate of silicon nitride films was modelled using a neural network in conjunction with Box—Wilson experimental design. The films were deposited by a plasma enhanced chemical vapour deposition system. A neural network model was constructed and tested with 33 and 12 experiments respectively. Model prediction performance was significantly improved by applying genetic algorithm to training factor optimisation. To qualitatively estimate deposition mechanisms with the parameters, several plots were generated from the model and emphasis was placed on the investigation of the radio frequency power effect on the deposition rate under various plasma conditions. An increase in the deposition rate with increasing power was ascribed to more Si deposition near the reaction surface. The SiH₄ effect was the most complex depending on the powers. In contrast, the power effect was insensitive to the variations in N₂ flow rate. The power effect at high pressure was attributed to increased concentration of Si radicals in the gas phase. A comparison with a refractive index model facilitated to infer deposition mechanisms.