Prediction of plasma etching using a randomized generalized regression neural network

A new empirical technique to construct predictive models of plasma etch processes is presented. This was accomplished by combining a generalized regression neural network (GRNN) and a random generator (RG). The RG played a critical role to control neuron spreads in the pattern layer. The proposed R-GRNN was evaluated with experimental plasma etch data. The etching of silica thin films was characterized by a 2³ full factorial experiment. The etch responses examined include aluminium etch rate, silica etch rate, profile angle, and DC bias. Additional test data were prepared to evaluate model appropriateness. Compared to conventional GRNN, the R-GRNN demonstrated much improved predictions of more than 40% for all etch responses. This was illustrated over statistical regression models. As a result, the proposed R-GRNN is an effective way to considerably improve the predictive ability of conventional GRNN.