Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11760191_160 | ISNN (2) |
Keywords | Field | DocType |
zno thin film,pl data,nonlinear process model,pulsed laser deposition,thin film growth mechanism,pca-based nnets,modeling methodology,growth rate,multivariate data,photoluminescence data,process modeling,pca-based neural network modeling,process condition,process model,statistical analysis,neural network model,thin film,neural network,back propagation | Data modeling,Computer science,Artificial intelligence,Thin film,Photoluminescence,Crystal growth,Pulsed laser deposition,Pattern recognition,Simulation,Process modeling,Optoelectronics,Principal component analysis,Growth rate | Conference |
Volume | ISSN | ISBN |
3973 | 0302-9743 | 3-540-34482-9 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jung Hwan Lee | 1 | 3 | 1.59 |
| Young-Don Ko | 2 | 12 | 4.16 |
| Min-Chang Jeong | 3 | 2 | 0.80 |
| Jae-Min Myoung | 4 | 11 | 3.29 |
| Ilgu Yun | 5 | 25 | 12.28 |