Abstract
In this paper, the principal component analysis based neural network process models of the HfO2 thin films are investigated. The input process parameters are extracted by analyzing the process conditions and the accumulation capacitance and the hysteresis index are extracted to be the main responses to examine the characteristics of the HfO2 dielectric films. Here, the screened X-ray diffraction data are used to analyze the characteristic variation for the different process conditions and predict the crystallinity-based the response models for the electrical characteristics. For the data screening, principal component analysis was carried out to reduce the dimension of two types of XRD data that are compressed into a small number of principal components. The compressed data are trained using the neural networks. The results show that the physical or material properties can be predicted by the models using the large dimension of the data.
| Original language | English |
|---|---|
| Title of host publication | INES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings |
| Pages | 115-120 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2007 Dec 1 |
| Event | INES 2007 - 11th International Conference on Intelligent Engineering Systems - Budapest, Hungary Duration: 2007 Jun 29 → 2007 Jul 1 |
Publication series
| Name | INES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings |
|---|
Other
| Other | INES 2007 - 11th International Conference on Intelligent Engineering Systems |
|---|---|
| Country | Hungary |
| City | Budapest |
| Period | 07/6/29 → 07/7/1 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Control and Systems Engineering
Cite this
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Comparison of PCA-based neural network models using the screening of X-ray diffraction data for MOMBE-grown HfO2 thin film characteristics. / Ko, Young Don; Jung, Hwan Lee; Ham, Moon Ho; Jang, Jaejin; Myoung, Jae Min; Yun, Ilgu.
INES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings. 2007. p. 115-120 4283683 (INES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Comparison of PCA-based neural network models using the screening of X-ray diffraction data for MOMBE-grown HfO2 thin film characteristics
AU - Ko, Young Don
AU - Jung, Hwan Lee
AU - Ham, Moon Ho
AU - Jang, Jaejin
AU - Myoung, Jae Min
AU - Yun, Ilgu
PY - 2007/12/1
Y1 - 2007/12/1
N2 - In this paper, the principal component analysis based neural network process models of the HfO2 thin films are investigated. The input process parameters are extracted by analyzing the process conditions and the accumulation capacitance and the hysteresis index are extracted to be the main responses to examine the characteristics of the HfO2 dielectric films. Here, the screened X-ray diffraction data are used to analyze the characteristic variation for the different process conditions and predict the crystallinity-based the response models for the electrical characteristics. For the data screening, principal component analysis was carried out to reduce the dimension of two types of XRD data that are compressed into a small number of principal components. The compressed data are trained using the neural networks. The results show that the physical or material properties can be predicted by the models using the large dimension of the data.
AB - In this paper, the principal component analysis based neural network process models of the HfO2 thin films are investigated. The input process parameters are extracted by analyzing the process conditions and the accumulation capacitance and the hysteresis index are extracted to be the main responses to examine the characteristics of the HfO2 dielectric films. Here, the screened X-ray diffraction data are used to analyze the characteristic variation for the different process conditions and predict the crystallinity-based the response models for the electrical characteristics. For the data screening, principal component analysis was carried out to reduce the dimension of two types of XRD data that are compressed into a small number of principal components. The compressed data are trained using the neural networks. The results show that the physical or material properties can be predicted by the models using the large dimension of the data.
UR - http://www.scopus.com/inward/record.url?scp=47749107636&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=47749107636&partnerID=8YFLogxK
U2 - 10.1109/INES.2007.4283683
DO - 10.1109/INES.2007.4283683
M3 - Conference contribution
AN - SCOPUS:47749107636
SN - 1424411475
SN - 9781424411474
T3 - INES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings
SP - 115
EP - 120
BT - INES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings
ER -