Recognition of plasma-induced x-ray photoelectron spectroscopy fault pattern using wavelet and neural network

Authors:
Byungwhan Kim;Sooyoun Kim;Sang Jeen Hong
Affiliations:
Department of Electronic Engineering, Sejong University, Seoul, Korea;Department of Electronic Engineering, Sejong University, Seoul, Korea;Department of Electronic Engineering, Myongji University, Yongin, Korea
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 3
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Abstract

To improve device yield and throughput, faults in plasma processing equipment should be quickly and accurately diagnosed. Despite many useful information of ex-situ sensor measurements, their applications to recognize plasma faults have not been investigated. In this study, a new technique to identify fault causes by recognizing X-ray photoelectron spectroscopy (XPS) using neural network and continuous wavelet transformation (CWT). The presented technique was evaluated with the plasma etch data. A total of 17 experiments were conducted for model construction. Model performance was investigated from the perspectives of training error, testing error, and recognition accuracy with respect to various thresholds. CWT-based BPNN models demonstrated a higher prediction accuracy of about 26%. Their advantages over pure XPS-based models were conspicuous in all three measures at small networks.