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PCA-Based Neural Network Modeling Using the Photoluminescence Data for Growth Rate of ZnO Thin Films Fabricated by Pulsed Laser Deposition

  • Jung Hwan Lee
  • Young-Don Ko
  • Min-Chang Jeong
  • Jae-Min Myoung
  • Ilgu Yun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

The process modeling for the growth rate of pulsed laser deposition (PLD)-grown ZnO thin films was investigated using neural networks (NNets) based on the back-propagation (BP) algorithm and PCA-based NNets using photoluminescence (PL) data. D-optimal experimental design was performed and the growth rate was characterized by NNets. PCA-based NNets were then carried out in order to build the model by PL data. The statistical analysis for those results was then used to verify the fitness of the nonlinear process model. Based on the results, this modeling methodology can explain the characteristics of the thin film growth mechanism varying with process conditions and the model can be analyzed and predicted by the multivariate data.

Keywords

Pulse Laser Deposition Latin Hypercube Sampling Vary Process Condition Model Growth Rate Pulse Laser Deposition Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jung Hwan Lee
    • 1
  • Young-Don Ko
    • 1
  • Min-Chang Jeong
    • 2
  • Jae-Min Myoung
    • 2
  • Ilgu Yun
    • 1
  1. 1.Semiconductor Engineering Laboratory, Department of Electrical and Electronics EngineeringYonsei UniversitySeoulKorea
  2. 2.Information and Electronic Materials Research Laboratory, Department of Materials Science and EngineeringYonsei UniversitySeoulKorea

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