Comparison of PCA-based neural network models using the screening of X-ray diffraction data for MOMBE-grown HfO2 thin film characteristics

Young Don Ko, Hwan Lee Jung, Moon Ho Ham, Jaejin Jang, Jae Min Myoung, Ilgu Yun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, the principal component analysis based neural network process models of the HfO2 thin films are investigated. The input process parameters are extracted by analyzing the process conditions and the accumulation capacitance and the hysteresis index are extracted to be the main responses to examine the characteristics of the HfO2 dielectric films. Here, the screened X-ray diffraction data are used to analyze the characteristic variation for the different process conditions and predict the crystallinity-based the response models for the electrical characteristics. For the data screening, principal component analysis was carried out to reduce the dimension of two types of XRD data that are compressed into a small number of principal components. The compressed data are trained using the neural networks. The results show that the physical or material properties can be predicted by the models using the large dimension of the data.

Original languageEnglish
Title of host publicationINES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings
Pages115-120
Number of pages6
DOIs
Publication statusPublished - 2007 Dec 1
EventINES 2007 - 11th International Conference on Intelligent Engineering Systems - Budapest, Hungary
Duration: 2007 Jun 292007 Jul 1

Publication series

NameINES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings

Other

OtherINES 2007 - 11th International Conference on Intelligent Engineering Systems
CountryHungary
CityBudapest
Period07/6/2907/7/1

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Systems Engineering

Cite this

Ko, Y. D., Jung, H. L., Ham, M. H., Jang, J., Myoung, J. M., & Yun, I. (2007). Comparison of PCA-based neural network models using the screening of X-ray diffraction data for MOMBE-grown HfO2 thin film characteristics. In INES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings (pp. 115-120). [4283683] (INES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings). https://doi.org/10.1109/INES.2007.4283683