Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference (한국전기전자재료학회:학술대회논문집)

The Korean Institute of Electrical and Electronic Material Engineers (한국전기전자재료학회)
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Process Modeling for $HfO_2$ Thin Films using Neural Networks

$HfO_2$ 박막 특성에 대한 신경망 모델링

  • Kweon, Kyoung-Eun (Dept. of Electrical and Electronic Engineering, Yonsei Univ.) ;
  • Lee, Jung-Hwan (Dept. of Electrical and Electronic Engineering, Yonsei Univ.) ;
  • Ko, Young-Don (Dept. of Electrical and Electronic Engineering, Yonsei Univ.) ;
  • Moon, Tae-Hyoung (Dept, of Metallugical Engineering, Yonsei Univ.) ;
  • Myoung, Jae-Min (Dept, of Metallugical Engineering, Yonsei Univ.) ;
  • Yun, Il-Gu (Dept. of Electrical and Electronic Engineering, Yonsei Univ.)
  • 권경은 (연세대학교 전기전자공학과) ;
  • 이정환 (연세대학교 전기전자공학과) ;
  • 고영돈 (연세대학교 전기전자공학과) ;
  • 문태형 (연세대학교 금속공학과) ;
  • 명재민 (연세대학교 금속공학과) ;
  • 윤일구 (연세대학교 전기전자공학과)
  • Published : 2005.07.07
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Abstract

In this paper, Latin Hypercube Sampling based the neural network model for the electrical characteristics of $HfO_2$ thin films grown by metal organic molecular beam epitaxy was investigated. The accumulation capacitance and the hysteresis index are extracted to be the main responses to examine the characteristics of $HfO_2$ thin films. X-ray diffraction was used to analyze the characteristic variation for the different process conditions. The initial weights and biases are selected by Latin Hypercube Sampling method. This modeling methodology can allow us to optimize the process recipes and improve the manufacturability.

Keywords