Recognition of Plasma- Induced X-Ray Photoelectron Spectroscopy Fault Pattern Using Wavelet and Neural Network

웨이블렛과 신경망을 이용한 플라즈마-유도 X-Ray Photoelectron Spectroscopy 고장 패턴의 인식

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 faultshave 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 totalof 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.