Principal Component Analysis of Optical Emission Spectroscopy and Mass Spectrometry: Application to Reactive Ion Etch Process Parameter Estimation Using Neural Networks

We report on a simple technique that characterizes the effect of process parameters (i.e., pressure, RF power, and gas mixture) on the optical emission and mass spectra of CHF₃/O₂ plasma. This technique is sensitive to changes in chamber contamination levels (e.g., formation of Teflon–like thin–film), and appears to be a promising tool for real–time monitoring and control of reactive ion etching. Through principal component analysis, we observe that 99% of the variance in the more than 1100 optical and mass spectra channels are accounted for by the first four principal components of each sensor. Projection of the mass spectrum on its principal components suggests a strong linear relationship with respect to chamber pressure. This representation also shows that the effect of changes in thin–film levels, gas mixture, and RF power on the mass spectrum is complicated, but predictable. To model the nonlinear relationship between these process parameters and the principal component projections, a feedforward, multi–layered neural network is trained and is shown to be able to predict all process parameters from either the mass or the optical spectrum. The projections of the optical emission spectrum on its principal components suggest that optical emission spectroscopy is much more sensitive to changes in RF power than the mass spectrum, as measured by the residual gas analyzer. Model performance can be significantly improved if both the optical and mass spectrum projections are used (so called sensor fusion). Our analysis indicates that accurate estimates of process parameters and chamber conditions can be made with relatively simple neural network models which fuse the principal components of the measured optical emission and mass spectra.