Temperature effects on deposition rate of silicon nitride films were characterized by building a neural network prediction model. The silicon nitride films were deposited by using a plasma enhanced chemical vapor deposition system and process parameter effects were systematically characterized by 2(6-1) fractional factorial experiment. The process parameters involved include a radio frequency power, pressure, temperature, SiH4, N-2, and NH3 flow rates. The prediction performance of generalized regression neural network was drastically improved by optimizing multi-valued training factors using a genetic algorithm. Several 3D plots were generated to investigate parameter effects at various temperatures. Predicted variations were experimentally validated. The temperature effect on the deposition rate was a complex function of parameters but N-2 flow rate. Larger decreases in the deposition rate with the temperature were only noticed at lower SiH4 (or higher NH3) flow rates. Typical effects of SiH4 or NH3 flow rate were only observed at higher or lower temperatures. A comparison with the refractive index model facilitated a selective choice of either SiH4 or NH3 for process optimization. (c) 2005 Published by Elsevier B.V.