On the use of a neural network to characterize the plasma etching of SiON thin films

Abstract

Using a generalized regression neural network (GRNN), plasma etching of oxynitride thin films was modeled. The etch process was characterized by means of a statistical experiment. A genetic algorithm was employed to improve prediction performance by optimizing multiparameterized training factors. Compared to a conventional GRNN model, the constructed etch rate model demonstrated an improvement of about 60% in the prediction performance. 3-D plots were generated to qualitatively interpret etch mechanisms while validating the predictions with experimental data. In separating physical and chemical effects, both dc bias and profile angle variations were effectively utilized. The source power affected significantly the etch rate irrespective of changes in the bias power or C2F6 flow rate. For pressure variations, the etch rate was estimated to be dominated by chemical etching. The complex effect of C2F6 flow rate could be explained by dominant chemical etching or polymer deposition.