Abstract
The process modeling for the growth rate in pulsed laser deposition (PLD)-grown ZnO thin films was investigated using neural networks (NNets) based on the back-propagation (BP) algorithm and the process recipes was optimized via genetic algorithms (GAs). Two input factors were examined with respect to the growth rate as the response factor. D-optimal experimental design technique was performed and the growth rate was characterized by NNets based on the BP algorithm. GAs was then used to search the desired recipes for the desired growth rate on the process. 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.
| Original language | English |
|---|---|
| Pages (from-to) | 4061-4066 |
| Number of pages | 6 |
| Journal | Expert Systems with Applications |
| Volume | 36 |
| Issue number | 2 PART 2 |
| DOIs | |
| Publication status | Published - 2009 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
- Computer Science Applications
- Artificial Intelligence
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Modeling and optimization of the growth rate for ZnO thin films using neural networks and genetic algorithms. / Ko, Young Don; Moon, Pyung; Kim, Chang Eun; Ham, Moon Ho; Myoung, Jae Min; Yun, Ilgu.
In: Expert Systems with Applications, Vol. 36, No. 2 PART 2, 01.01.2009, p. 4061-4066.Research output: Contribution to journal › Article
TY - JOUR
T1 - Modeling and optimization of the growth rate for ZnO thin films using neural networks and genetic algorithms
AU - Ko, Young Don
AU - Moon, Pyung
AU - Kim, Chang Eun
AU - Ham, Moon Ho
AU - Myoung, Jae Min
AU - Yun, Ilgu
PY - 2009/1/1
Y1 - 2009/1/1
N2 - The process modeling for the growth rate in pulsed laser deposition (PLD)-grown ZnO thin films was investigated using neural networks (NNets) based on the back-propagation (BP) algorithm and the process recipes was optimized via genetic algorithms (GAs). Two input factors were examined with respect to the growth rate as the response factor. D-optimal experimental design technique was performed and the growth rate was characterized by NNets based on the BP algorithm. GAs was then used to search the desired recipes for the desired growth rate on the process. 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.
AB - The process modeling for the growth rate in pulsed laser deposition (PLD)-grown ZnO thin films was investigated using neural networks (NNets) based on the back-propagation (BP) algorithm and the process recipes was optimized via genetic algorithms (GAs). Two input factors were examined with respect to the growth rate as the response factor. D-optimal experimental design technique was performed and the growth rate was characterized by NNets based on the BP algorithm. GAs was then used to search the desired recipes for the desired growth rate on the process. 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.
UR - http://www.scopus.com/inward/record.url?scp=56349157402&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=56349157402&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2008.03.010
DO - 10.1016/j.eswa.2008.03.010
M3 - Article
AN - SCOPUS:56349157402
VL - 36
SP - 4061
EP - 4066
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
IS - 2 PART 2
ER -