General Resolution Enhancement Method in Atomic Force Microscopy Using Deep Learning. Issue 2 (9th November 2018)

• Record Type: Journal Article
• Authors: Liu, Yue; Sun, Qiaomei; Lu, Wanheng; Wang, Hongli; Sun, Yao; Wang, Zhongting; Lu, Xin; Zeng, Kaiyang
• Is Part Of: Advanced theory and simulations. Volume 2:Issue 2(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-11-09
• Subjects: atomic force microscopy -- deep learning -- multi‐materials network -- nanoscale topography -- super‐resolution; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.201800137
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: Here, a resolution enhancement method is developed for post‐processing 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 a high‐resolution topography image from a 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. Abstract : A resolution enhancement method for post‐processing of atomic force microscopy (AFM) images is developed. This method is based on deep learning neural networks. In this study, a very deep convolution neural network is developed to derive a high‐resolution topography image from a low‐resolution topography image. The AFM measured images from various materials are then tested. .