Journal of the Korean Institute of Electrical and Electronic Material Engineers (한국전기전자재료학회논문지)

The Korean Institute of Electrical and Electronic Material Engineers (한국전기전자재료학회)
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Modeling of RF Sputtering Process for ZnO Thin film Deposition using Neural Network

신경회로망을 이용한 RF 스퍼터링 ZnO 박막 증착 프로세스 모델링

  • 임근영 (원광대학교 전기전자 및 정보공학부) ;
  • 이상극 (광운대학교) ;
  • 박춘배 (원광대학교 전기전자 및 정보공학부)
  • Published : 2006.07.01
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Abstract

ZnO deposition parameters are not independent and have a nonlinear and complex property. To propose a method that could verify and predict the relations of process variables, neural network was used. At first, ZnO thin films were deposited by using RF magnetron sputtering process with various conditions. Si, GaAs, and Glass were used as substrates. The temperature, work pressure, and RF power of the substrate were $50\sim500^{\circ}C$, 15 mTorr, and $180\sim210W$, respectively : the purity of the target was ZnO 4 N. Structural properties of ZnO thin films were estimated by using XRD (0002) peak intensity. The structure of neural network was a form of 4-7-1 that have one hidden layer. In training a network, learning rate and momentum were selected as 0.2, 0.6 respectively. A backpropagation neural network were performed with XRD (0002) peak data. After training a network, the temperature of substrate was evaluated as the most important parameter by sensitivity analysis and response surface. As a result, neural network could capture nonlinear and complex relationships between process parameters and predict structural properties of ZnO thin films with a limited set of experiments.

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References

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