Use of a neural network to link AFM etch patterns to plasma parameters

Kim, Bygunwhan; Kwon, Sanghee

doi:10.1016/j.vacuum.2007.03.009

Cited by 1 publication

(1 citation statement)

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“…Kim combined AFM, the nano-oxidation technique, and BPN to establish three models for an etching process. The models had increased accuracy in predicting process information [ 24 ]. Khanmohammadi used BPN to predict the average particle size of titanium dioxide by infrared reflectance spectroscopy [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction Surface Morphology of Nanostructure Fabricated by Nano-Oxidation Technology

et al. 2015

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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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Section: Introductionmentioning

confidence: 99%