대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)
- 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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- Pages.135-137
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- 2006
웨이블렛과 신경망을 이용한 플라즈마-유도 X-Ray Photoelectron Spectroscopy 고장 패턴의 인식
Recognition of Plasma- Induced X-Ray Photoelectron Spectroscopy Fault Pattern Using Wavelet and Neural Network
초록
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.