Prediction of etching results and etching stabilization by applying principal component regression to emission spectra during in-situ cleaning

A method to predict etching results by analyzing plasma emission spectra during in-situ cleaning was investigated, where the plasma emission spectra indicate the surface condition of etching reactor walls. Plasma–wall interaction was evaluated by using both principal component regression of plasma emission spectra and attenuated-total-reflection Fourier-transform infrared spectroscopy. We found that differences in the amount of silicon oxide deposition on the reactor wall affected radical composition in the plasma during in-situ cleaning and consequently affected the etching results. Etching result predictions using the plasma spectra corresponded very well to the etching result measurements, which are used to improve etching stability.