Neural-Network-Based Sensor Fusion of Optical Emission and Mass Spectroscopy Data for Real-Time Fault Detection in Reactive Ion Etching
Hong, S.J.
May, G.S.
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Aug. 2005
Volume: 52,
Issue: 4
On page(s): 1063- 1072
ISSN: 0278-0046
Digital Object Identifier: 10.1109/TIE.2005.851663
Current Version Published: 2005-08-01
Abstract
To achieve timely and accurate fault detection in reactive ion etching, neural networks (NNs) have been applied for the fusion of data generated by two in-situ sensors: optical emission spectroscopy (OES) and residual gas analysis (RGA). While etching is performed, OES and RGA data are simultaneously collected in real time. Several pre-determined, statistically significant wavelengths (for OES data) and atomic masses (for RGA signals) are monitored. These data are subsequently used for training NN-based time series models of process behavior. Such models, referred to herein as time series NNs (TSNNs), are realized using multilayered perceptron NNs. Results indicate that the TSNNs not only predict process parameters of interest, but also efficiently perform as sensor fusion of the in-situ sensor data.
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