Qualitative modeling of silica plasma etching using neural network

Abstract

An etching of silica thin film is qualitatively modeled by using a neural network. The process was characterized by a 23 full factorial experiment plus one center point, in which the experimental factors and ranges include 100–800 W radio-frequency source power, 100–400 W bias power and gas flow rate ratio CHF3/CF4. The gas flow rate ratio varied from 0.2 to 5.0. The backpropagation neural network (BPNN) was trained on nine experiments and tested on six experiments, not pertaining to the original training data. The prediction ability of the BPNN was optimized as a function of the training parameters. Prediction errors are 180 Å/min and 1.33, for the etch rate and anisotropy models, respectively. Physical etch mechanisms were estimated from the three-dimensional plots generated from the optimized models. Predicted response surfaces were consistent with experimentally measured etch data. The dc bias was correlated to the etch responses to evaluate its contribution. Both the source power (plasma density) and bias power (ion directionality) strongly affected the etch rate. The source power was the most influential factor for the etch rate. A conflicting effect between the source and bias powers was noticed with respect to the anisotropy. The dc bias played an important role in understanding or separating physical etch mechanisms.