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 and Optimization for Characteristics of ZnO Thin Films using Neural Networks and Genetic Algorithms

신경망과 유전 알고리즘을 이용한 광소자용 ZnO 박막 특성 공정 모델링 및 최적화

  • Ko, Young-Don (Dept. of Electrical and Electronic Engineering, Yonsei University) ;
  • Kang, Hong-Seong (Dept. of Electrical and Electronic Engineering, Yonsei University) ;
  • Jeong, Min-Chang (Dept, of Metallugical Engineering, Yonsei University) ;
  • Lee, Sang-Yeol (Dept. of Electrical and Electronic Engineering, Yonsei University) ;
  • Myoung, Jae-Min (Dept, of Metallugical Engineering, Yonsei University) ;
  • Yun, Il-Gu (Dept. of Electrical and Electronic Engineering, Yonsei University)
  • 고영돈 (연세대학교 전기전자공학과) ;
  • 강홍성 (연세대학교 전기전자공학과) ;
  • 정민창 (연세대학교 금속공학과) ;
  • 이상렬 (연세대학교 전기전자공학과) ;
  • 명재민 (연세대학교 금속공학과) ;
  • 윤일구 (연세대학교 전기전자공학과)
  • Published : 2004.07.05
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Abstract

The process modeling for the growth rate in pulsed laser deposition(PLD)-grown ZnO thin films is investigated using neural networks(NNets) and the process recipes is optimized via genetic algorithms(GAs). D-optimal design is carried out and the growth rate is characterized by NNets based on the back-propagation(BP) algorithm. GAs is then used to search the desired recipes for the desired growth rate. The statistical analysis is used to verify the fitness of the nonlinear process model. This process modeling and optimization algorithms can explain the characteristics of the desired responses varying with process conditions.

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