PCA-based neural network modeling of MBE-grown HfO2 thin film characteristics

Y. D. Ko; J. H. Lee; K. E. Kweon; T. H. Moon; J. M. Myoung; I. Yun

doi:10.1109/EDSSC.2005.1635235

PCA-based neural network modeling of MBE-grown HfO₂ thin film characteristics

Y. D. Ko, J. H. Lee, K. E. Kweon, T. H. Moon, J. M. Myoung, I. Yun

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

In this paper, the neural network based modeling for the HfO₂ thin film characteristics, such as the accumulation capacitance and the hysteresis index, grown by metal organic molecular beam epitaxy was investigated. In order to build the process model, the error back-propagation neural network was carried out and the X-ray diffraction data were used to analyze the characteristic variation for the different process conditions and predict the response models for the electrical characteristics. Principal component analysis was selected to reduce the dimension of the data sets. The compressed data were then used in the neural network. Those 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.

Original language	English
Title of host publication	2005 IEEE Conference on Electron Devices and Solid-State Circuits, EDSSC
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	181-184
Number of pages	4
ISBN (Print)	0780393392, 9780780393394
DOIs	https://doi.org/10.1109/EDSSC.2005.1635235
Publication status	Published - 2005 Jan 1
Event	2005 IEEE Conference on Electron Devices and Solid-State Circuits, EDSSC - Howloon, Hong Kong Duration: 2005 Dec 19 → 2005 Dec 21

Publication series

Name	2005 IEEE Conference on Electron Devices and Solid-State Circuits, EDSSC

Other

Other	2005 IEEE Conference on Electron Devices and Solid-State Circuits, EDSSC
Country/Territory	Hong Kong
City	Howloon
Period	05/12/19 → 05/12/21

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials

Access to Document

10.1109/EDSSC.2005.1635235

Cite this

Ko, Y. D., Lee, J. H., Kweon, K. E., Moon, T. H., Myoung, J. M., & Yun, I. (2005). PCA-based neural network modeling of MBE-grown HfO₂ thin film characteristics. In 2005 IEEE Conference on Electron Devices and Solid-State Circuits, EDSSC (pp. 181-184). Article 1635235 (2005 IEEE Conference on Electron Devices and Solid-State Circuits, EDSSC). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EDSSC.2005.1635235

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abstract = "In this paper, the neural network based modeling for the HfO2 thin film characteristics, such as the accumulation capacitance and the hysteresis index, grown by metal organic molecular beam epitaxy was investigated. In order to build the process model, the error back-propagation neural network was carried out and the X-ray diffraction data were used to analyze the characteristic variation for the different process conditions and predict the response models for the electrical characteristics. Principal component analysis was selected to reduce the dimension of the data sets. The compressed data were then used in the neural network. Those 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.",

author = "Ko, {Y. D.} and Lee, {J. H.} and Kweon, {K. E.} and Moon, {T. H.} and Myoung, {J. M.} and I. Yun",

year = "2005",

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doi = "10.1109/EDSSC.2005.1635235",

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booktitle = "2005 IEEE Conference on Electron Devices and Solid-State Circuits, EDSSC",

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note = "2005 IEEE Conference on Electron Devices and Solid-State Circuits, EDSSC ; Conference date: 19-12-2005 Through 21-12-2005",

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Ko, YD, Lee, JH, Kweon, KE, Moon, TH, Myoung, JM & Yun, I 2005, PCA-based neural network modeling of MBE-grown HfO₂ thin film characteristics. in 2005 IEEE Conference on Electron Devices and Solid-State Circuits, EDSSC., 1635235, 2005 IEEE Conference on Electron Devices and Solid-State Circuits, EDSSC, Institute of Electrical and Electronics Engineers Inc., pp. 181-184, 2005 IEEE Conference on Electron Devices and Solid-State Circuits, EDSSC, Howloon, Hong Kong, 05/12/19. https://doi.org/10.1109/EDSSC.2005.1635235

PCA-based neural network modeling of MBE-grown HfO₂ thin film characteristics. / Ko, Y. D.; Lee, J. H.; Kweon, K. E. et al.
2005 IEEE Conference on Electron Devices and Solid-State Circuits, EDSSC. Institute of Electrical and Electronics Engineers Inc., 2005. p. 181-184 1635235 (2005 IEEE Conference on Electron Devices and Solid-State Circuits, EDSSC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - In this paper, the neural network based modeling for the HfO2 thin film characteristics, such as the accumulation capacitance and the hysteresis index, grown by metal organic molecular beam epitaxy was investigated. In order to build the process model, the error back-propagation neural network was carried out and the X-ray diffraction data were used to analyze the characteristic variation for the different process conditions and predict the response models for the electrical characteristics. Principal component analysis was selected to reduce the dimension of the data sets. The compressed data were then used in the neural network. Those 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.

AB - In this paper, the neural network based modeling for the HfO2 thin film characteristics, such as the accumulation capacitance and the hysteresis index, grown by metal organic molecular beam epitaxy was investigated. In order to build the process model, the error back-propagation neural network was carried out and the X-ray diffraction data were used to analyze the characteristic variation for the different process conditions and predict the response models for the electrical characteristics. Principal component analysis was selected to reduce the dimension of the data sets. The compressed data were then used in the neural network. Those 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.

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Ko YD, Lee JH, Kweon KE, Moon TH, Myoung JM , Yun I. PCA-based neural network modeling of MBE-grown HfO₂ thin film characteristics. In 2005 IEEE Conference on Electron Devices and Solid-State Circuits, EDSSC. Institute of Electrical and Electronics Engineers Inc. 2005. p. 181-184. 1635235. (2005 IEEE Conference on Electron Devices and Solid-State Circuits, EDSSC). doi: 10.1109/EDSSC.2005.1635235