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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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Autores principales: Huang, Jen-Ching, Chang, Ho, Kuo, Chin-Guo, Li, Jeen-Fong, You, Yong-Chin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5458819/
https://www.ncbi.nlm.nih.gov/pubmed/28793721
http://dx.doi.org/10.3390/ma8125468
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author Huang, Jen-Ching
Chang, Ho
Kuo, Chin-Guo
Li, Jeen-Fong
You, Yong-Chin
author_facet Huang, Jen-Ching
Chang, Ho
Kuo, Chin-Guo
Li, Jeen-Fong
You, Yong-Chin
author_sort Huang, Jen-Ching
collection PubMed
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.
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spelling pubmed-54588192017-07-28 Prediction Surface Morphology of Nanostructure Fabricated by Nano-Oxidation Technology Huang, Jen-Ching Chang, Ho Kuo, Chin-Guo Li, Jeen-Fong You, Yong-Chin Materials (Basel) Article 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. MDPI 2015-12-04 /pmc/articles/PMC5458819/ /pubmed/28793721 http://dx.doi.org/10.3390/ma8125468 Text en © 2015 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huang, Jen-Ching
Chang, Ho
Kuo, Chin-Guo
Li, Jeen-Fong
You, Yong-Chin
Prediction Surface Morphology of Nanostructure Fabricated by Nano-Oxidation Technology
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 Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5458819/
https://www.ncbi.nlm.nih.gov/pubmed/28793721
http://dx.doi.org/10.3390/ma8125468
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