Year: 2018
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Liu, Y., Sun, Q. M., Lu, Dr. W. H., Wang, Dr. H. L., Sun, Y., Wang, Z. T., Lu, X., Zeng, Prof. K. Y.
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2949639657