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General Resolution Enhancement Method in Atomic Force Microscopy (AFM) Using Deep Learning

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arxiv 1809.03704 v1 pith:E7TEIAJW submitted 2018-09-11 physics.data-an cond-mat.mtrl-sci

Y. Liu , Q. M. Sun , Dr. W. H. Lu , Dr. H. L. Wang , Y. Sun , Z. T. Wang , X. Lu , Prof. K. Y. Zeng This is my paper · ORCID
classification physics.data-ancond-mat.mtrl-sci
keywords methodimagesdeephigh-resolutionimagemeasuredtopographyatomic
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper develops a resolution enhancement method for post-processing the images from Atomic Force Microscopy (AFM). This method is based on deep learning neural networks in the AFM topography measurements. In this study, a very deep convolution neural network is developed to derive the high-resolution topography image from the low-resolution topography image. The AFM measured images from various materials are tested in this study. The derived high-resolution AFM images are comparable with the experimental measured high-resolution images measured at the same locations. The results suggest that this method can be developed as a general post-processing method for AFM image analysis.

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