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

doi: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

Jung Hwan Lee, Young Don Ko, Min Chang Jeong, Jae Min Myoung, Ilgu Yun

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Advances in Neural Networks - ISNN 2006
Subtitle of host publication	Third International Symposium on Neural Networks, ISNN 2006, Proceedings - Part III
Publisher	Springer Verlag
Pages	1099-1104
Number of pages	6
ISBN (Print)	3540344829, 9783540344827
DOIs	https://doi.org/10.1007/11760191_160
Publication status	Published - 2006
Event	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China Duration: 2006 May 28 → 2006 Jun 1

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3973 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
Country	China
City	Chengdu
Period	06/5/28 → 06/6/1

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/11760191_160

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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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title = "PCA-based neural network modeling using the photoluminescence data for growth rate of ZnO thin films fabricated by pulsed laser deposition",

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.",

author = "Lee, {Jung Hwan} and Ko, {Young Don} and Jeong, {Min Chang} and Myoung, {Jae Min} and Ilgu Yun",

note = "Copyright: Copyright 2015 Elsevier B.V., All rights reserved.; 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks ; Conference date: 28-05-2006 Through 01-06-2006",

year = "2006",

doi = "10.1007/11760191_160",

language = "English",

isbn = "3540344829",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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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 proceeding › Conference 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