대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)
- 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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- Pages.132-134
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- 2006
플라즈마 진단을 위한 Scanning Electron Microscope Image의 신경망 인식 모델
Neural Network Recognition of Scanning Electron Microscope Image for Plasma Diagnosis
초록
To improve equipment throughput and device yield, a malfunction in plasma equipment should be accurately diagnosed. A recognition model for plasma diagnosis was constructed by applying neural network to scanning electron microscope (SEM) image of plasma-etched patterns. The experimental data were collected from a plasma etching of tungsten thin films. Faults in plasma were generated by simulating a variation in process parameters. Feature vectors were obtained by applying direct and wavelet techniques to SEM Images. The wavelet techniques generated three feature vectors composed of detailed components. The diagnosis models constructed were evaluated in terms of the recognition accuracy. The direct technique yielded much smaller recognition accuracy with respect to the wavelet technique. The improvement was about 82%. This demonstrates that the direct method is more effective in constructing a neural network model of SEM profile information.