Monitoring of plasma processing chamber using ion energy analyser and time series neural network

Abstract

A new technique to monitor plasma processes is presented. An autocorrelated neural time series (A-NTS) network was used to model ion energy distribution (IED). The ion energy data were collected during a deposition of silicon nitride films in a SiH₄–NH₃ inductively coupled plasma. A backpropagation neural network was used to build IED model. Prediction performance of A-NTS model was evaluated as a function of training tolerance as well as a detection sensitivity. The A-NTS models demonstrated detection sensitivities high enough to detect plasma faults. Maximum sensitivity of A-NTS models obtained was more than 55% for all fault cases. Optimised A-NTS models yielded the prediction errors of 1·41 and 2·18% for 80 and 60% IED respectively. The presented technique can be applied to monitor any kinds of plasma faults and is expected effective particularly to those faults sensitive to ion bombardment variations.