Use of Neural Network to Characterize Temperature Effects on Deposition Rate of PECVD-Silicon Nitride Thin Films
Temperature effects on the 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 include a radio frequency power, pressure, temperature, SiH4, N2, and NH3 flow rates. The prediction performance of generalized regression neural network (GRNN) was considerably improved by optimizing multi-valued training factors using a genetic algorithm (GA). Compared to the conventional model, GA-GRNN model demonstrated an improvement of more than 70 %. Several 3-D plots were generated to interpret temperature effects at various plasma conditions. It is noticeable that typical effects of SiH4 and NH3 flow rate were observed only at higher and lower temperatures, respectively. Depending on the levels of SiH4 (or higher NH 3) flow rate, the temperature effects were quite different. Deposition mechanisms were qualitatively estimated.
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