Neural network based time series modeling of optical emission spectroscopy data for fault detection in reactive ion etching

Abstract

To achieve timely and accurate fault detection, neural network-based time series modeling is applied to a reactive ion etching (RIE) process using an in-situ plasma sensor called optical emission spectroscopy (OES). OES is a wellestablished method of etch endpoint detection, but the large volume of data generated by this technique makes further analysis challenging. To alleviate this concern, principal component analysis (PCA) is adopted for dimensionality reduction of a voluminous OES data set, and the reduced data set is utilized for time series modeling and malfunction identification using neural networks. Four different RIE subsystems (RF power, chamber pressure, and two gas flow systems) were considered, and multiple degrees of potential faults were tested. The time series neural networks (TSNNs) are trained to forecast future process conditions, and those forecasts are compared to established baselines. Satisfying results are achieved, demonstrating the potential of this technique for real-time fault detection and diagnosis.