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Materials 2015, 8(12), 8437-8451; doi:10.3390/ma8125468

Prediction Surface Morphology of Nanostructure Fabricated by Nano-Oxidation Technology

1
Department of Mechanical Engineering, Tungnan University, New Taipei City 22202, Taiwan
2
Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, Taiwan
3
Department of Industrial Education, National Taiwan Normal University, Taipei 10610, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Teen-Hang Meen
Received: 9 September 2015 / Revised: 30 November 2015 / Accepted: 30 November 2015 / Published: 4 December 2015
(This article belongs to the Special Issue Selected Papers from ICASI 2015)
View Full-Text   |   Download PDF [4651 KB, uploaded 4 December 2015]   |  

Abstract

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. View Full-Text
Keywords: atomic force microscopy (AFM); nano-oxidation; diamond-like carbon (DLC); back propagation neural network (BPN) atomic force microscopy (AFM); nano-oxidation; diamond-like carbon (DLC); back propagation neural network (BPN)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Huang, J.-C.; Chang, H.; Kuo, C.-G.; Li, J.-F.; You, Y.-C. Prediction Surface Morphology of Nanostructure Fabricated by Nano-Oxidation Technology. Materials 2015, 8, 8437-8451.

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