Neural network modeling of reactive ion etching using principal component analysis of optical emission spectroscopy data

In this paper, neural networks trained by the error back-propagation algorithm are used to build models of etch rate, uniformity, selectivity and anisotropy as a function of optical emission spectroscopy (OES) data in a reactive ion etching process. The material etched is benzocyclobutene (BCB), a low-k dielectric polymer, which is etched in an SF₆ and O₂ plasma in a parallel plate system. Neural network training data are obtained from a multi-way principal component analysis (MPCA) of the OES data. These data are acquired from a 2⁴ factorial experiment designed to characterize etch process variation with controllable input factors consisting of the two gas flows, RF power and chamber pressure. Evaluation of the trained neural networks is performed in terms of root mean square (RMS) error, and less than 3% prediction errors are achieved.