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

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

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.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - ISNN 2006
Subtitle of host publicationThird International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III
PublisherSpringer Verlag
Pages1099-1104
Number of pages6
ISBN (Print)3540344829, 9783540344827
DOIs
Publication statusPublished - 2006 Jan 1
Event3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China
Duration: 2006 May 282006 Jun 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3973 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
CountryChina
CityChengdu
Period06/5/2806/6/1

Fingerprint

Pulsed Laser Deposition
Network Modeling
Photoluminescence
Pulsed laser deposition
Thin Films
Neural Networks
Neural networks
Thin films
Optimal Experimental Design
D-optimal
Backpropagation algorithms
Nonlinear Process
Back-propagation Algorithm
Multivariate Data
Film growth
Process Modeling
Design of experiments
Process Model
Fitness
Statistical Analysis

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Lee, J. H., Ko, Y. D., Jeong, M. C., Myoung, J. M., & Yun, I. (2006). PCA-based neural network modeling using the photoluminescence data for growth rate of ZnO thin films fabricated by pulsed laser deposition. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III (pp. 1099-1104). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3973 LNCS). Springer Verlag. https://doi.org/10.1007/11760191_160
Lee, Jung Hwan ; Ko, Young Don ; Jeong, Min Chang ; Myoung, Jae Min ; Yun, Ilgu. / PCA-based neural network modeling using the photoluminescence data for growth rate of ZnO thin films fabricated by pulsed laser deposition. Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Springer Verlag, 2006. pp. 1099-1104 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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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.",
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Lee, JH, Ko, YD, Jeong, MC, Myoung, JM & Yun, I 2006, PCA-based neural network modeling using the photoluminescence data for growth rate of ZnO thin films fabricated by pulsed laser deposition. in Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3973 LNCS, Springer Verlag, pp. 1099-1104, 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks, Chengdu, China, 06/5/28. https://doi.org/10.1007/11760191_160

PCA-based neural network modeling using the photoluminescence data for growth rate of ZnO thin films fabricated by pulsed laser deposition. / Lee, Jung Hwan; Ko, Young Don; Jeong, Min Chang; Myoung, Jae Min; Yun, Ilgu.

Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Springer Verlag, 2006. p. 1099-1104 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3973 LNCS).

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

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AB - 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.

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Lee JH, Ko YD, Jeong MC, Myoung JM, Yun I. PCA-based neural network modeling using the photoluminescence data for growth rate of ZnO thin films fabricated by pulsed laser deposition. In Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III. Springer Verlag. 2006. p. 1099-1104. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/11760191_160