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15 July 2003 Neural network based time series modeling of optical emission spectroscopy data for fault detection in reactive ion etching
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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.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sang Jeen Hong and Gary Stephen May "Neural network based time series modeling of optical emission spectroscopy data for fault detection in reactive ion etching", Proc. SPIE 5041, Process and Materials Characterization and Diagnostics in IC Manufacturing, (15 July 2003); https://doi.org/10.1117/12.485230
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