Recognition of Plasma-Induced X-Ray Photoelectron Spectroscopy Fault Pattern Using Wavelet and Neural Network
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.
KeywordsRecognition Accuracy Continuous Wavelet Transformation BPNN Model Fault Symptom Radio Frequency Source
Unable to display preview. Download preview PDF.
- 8.Rummelhart, D.E., McClelland, J.L.: Parallel Distributed Processing. MIT Press, Cam-bridge (1986)Google Scholar