Prediction Surface Morphology of Nanostructure Fabricated by Nano-Oxidation Technology
Atomic force microscopy (AFM) was used for visualization of a nano-oxidation technique performed on diamond-like carbon (DLC) thin film. Experiments of the nano-oxidation technique of the DLC thin film include those on nano-oxidation points and nano-oxidation lines. The feature sizes of the DLC thin...
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MDPI AG
2015-12-01
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| Series: | Materials |
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| Online Access: | http://www.mdpi.com/1996-1944/8/12/5468 |
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| author | Jen-Ching Huang Ho Chang Chin-Guo Kuo Jeen-Fong Li Yong-Chin You |
| author_facet | Jen-Ching Huang Ho Chang Chin-Guo Kuo Jeen-Fong Li Yong-Chin You |
| author_sort | Jen-Ching Huang |
| collection | DOAJ |
| description | Atomic force microscopy (AFM) was used for visualization of a nano-oxidation technique performed on diamond-like carbon (DLC) thin film. Experiments of the nano-oxidation technique of the DLC thin film include those on nano-oxidation points and nano-oxidation lines. The feature sizes of the DLC thin film, including surface morphology, depth, and width, were explored after application of a nano-oxidation technique to the DLC thin film under different process parameters. A databank for process parameters and feature sizes of thin films was then established, and multiple regression analysis (MRA) and a back-propagation neural network (BPN) were used to carry out the algorithm. The algorithmic results are compared with the feature sizes acquired from experiments, thus obtaining a prediction model of the nano-oxidation technique of the DLC thin film. The comparative results show that the prediction accuracy of BPN is superior to that of MRA. When the BPN algorithm is used to predict nano-point machining, the mean absolute percentage errors (MAPE) of depth, left side, and right side are 8.02%, 9.68%, and 7.34%, respectively. When nano-line machining is being predicted, the MAPEs of depth, left side, and right side are 4.96%, 8.09%, and 6.77%, respectively. The obtained data can also be used to predict cross-sectional morphology in the DLC thin film treated with a nano-oxidation process. |
| first_indexed | 2024-04-13T11:58:46Z |
| format | Article |
| id | doaj.art-e85edfd99a6b45979f71c6042da0d6ca |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-04-13T11:58:46Z |
| publishDate | 2015-12-01 |
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| spelling | doaj.art-e85edfd99a6b45979f71c6042da0d6ca2022-12-22T02:47:51ZengMDPI AGMaterials1996-19442015-12-018128437845110.3390/ma8125468ma8125468Prediction Surface Morphology of Nanostructure Fabricated by Nano-Oxidation TechnologyJen-Ching Huang0Ho Chang1Chin-Guo Kuo2Jeen-Fong Li3Yong-Chin You4Department of Mechanical Engineering, Tungnan University, New Taipei City 22202, TaiwanGraduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, TaiwanDepartment of Industrial Education, National Taiwan Normal University, Taipei 10610, TaiwanDepartment of Industrial Education, National Taiwan Normal University, Taipei 10610, TaiwanGraduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, TaiwanAtomic force microscopy (AFM) was used for visualization of a nano-oxidation technique performed on diamond-like carbon (DLC) thin film. Experiments of the nano-oxidation technique of the DLC thin film include those on nano-oxidation points and nano-oxidation lines. The feature sizes of the DLC thin film, including surface morphology, depth, and width, were explored after application of a nano-oxidation technique to the DLC thin film under different process parameters. A databank for process parameters and feature sizes of thin films was then established, and multiple regression analysis (MRA) and a back-propagation neural network (BPN) were used to carry out the algorithm. The algorithmic results are compared with the feature sizes acquired from experiments, thus obtaining a prediction model of the nano-oxidation technique of the DLC thin film. The comparative results show that the prediction accuracy of BPN is superior to that of MRA. When the BPN algorithm is used to predict nano-point machining, the mean absolute percentage errors (MAPE) of depth, left side, and right side are 8.02%, 9.68%, and 7.34%, respectively. When nano-line machining is being predicted, the MAPEs of depth, left side, and right side are 4.96%, 8.09%, and 6.77%, respectively. The obtained data can also be used to predict cross-sectional morphology in the DLC thin film treated with a nano-oxidation process.http://www.mdpi.com/1996-1944/8/12/5468atomic force microscopy (AFM)nano-oxidationdiamond-like carbon (DLC)back propagation neural network (BPN) |
| spellingShingle | Jen-Ching Huang Ho Chang Chin-Guo Kuo Jeen-Fong Li Yong-Chin You Prediction Surface Morphology of Nanostructure Fabricated by Nano-Oxidation Technology Materials atomic force microscopy (AFM) nano-oxidation diamond-like carbon (DLC) back propagation neural network (BPN) |
| title | Prediction Surface Morphology of Nanostructure Fabricated by Nano-Oxidation Technology |
| title_full | Prediction Surface Morphology of Nanostructure Fabricated by Nano-Oxidation Technology |
| title_fullStr | Prediction Surface Morphology of Nanostructure Fabricated by Nano-Oxidation Technology |
| title_full_unstemmed | Prediction Surface Morphology of Nanostructure Fabricated by Nano-Oxidation Technology |
| title_short | Prediction Surface Morphology of Nanostructure Fabricated by Nano-Oxidation Technology |
| title_sort | prediction surface morphology of nanostructure fabricated by nano oxidation technology |
| topic | atomic force microscopy (AFM) nano-oxidation diamond-like carbon (DLC) back propagation neural network (BPN) |
| url | http://www.mdpi.com/1996-1944/8/12/5468 |
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