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 | |
| Publication status | Published - 2006 Jan 1 |
| 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 |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Theoretical Computer Science
Cite this
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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. / 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
TY - GEN
T1 - PCA-based neural network modeling using the photoluminescence data for growth rate of ZnO thin films fabricated by pulsed laser deposition
AU - Lee, Jung Hwan
AU - Ko, Young Don
AU - Jeong, Min Chang
AU - Myoung, Jae Min
AU - Yun, Ilgu
PY - 2006/1/1
Y1 - 2006/1/1
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=33745876535&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33745876535&partnerID=8YFLogxK
U2 - 10.1007/11760191_160
DO - 10.1007/11760191_160
M3 - Conference contribution
SN - 3540344829
SN - 9783540344827
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1099
EP - 1104
BT - Advances in Neural Networks - ISNN 2006
PB - Springer Verlag
ER -