Accelerating AFM Characterization via Deep-Learning-Based Image Super-Resolution

Young-Joo Kim; Jaekyung Lim; Do-Nyun Kim

doi:10.1002/smll.202103779

Accelerating AFM Characterization via Deep-Learning-Based Image Super-Resolution

Small. 2022 Jan;18(3):e2103779. doi: 10.1002/smll.202103779. Epub 2021 Nov 27.

Authors

Young-Joo Kim¹, Jaekyung Lim², Do-Nyun Kim^{1

2

3}

Affiliations

¹ Institute of Advanced Machines and Design, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea.
² Department of Mechanical Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea.
³ Institute of Engineering Research, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea.

PMID: 34837327
DOI: 10.1002/smll.202103779

Abstract

Atomic force microscopy (AFM) is one of the most popular imaging and characterizing methods applicable to a wide range of nanoscale material systems. However, high-resolution imaging using AFM generally suffers from a low scanning yield due to its method of raster scanning. Here, a systematic method of data acquisition and preparation combined with a deep-learning-based image super-resolution, enabling rapid AFM characterization with accuracy, is proposed. Its application to measuring the geometrical and mechanical properties of structured DNA assemblies reveals that around a tenfold reduction in AFM imaging time can be achieved without significant loss of accuracy. Through a transfer learning strategy, it can be efficiently customized for a specific target sample on demand.

Keywords: DNA nanotechnology; atomic force microscopy; deep-learning; nanomaterial characterization; super-resolution microscopy.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

DNA
Deep Learning*
Microscopy, Atomic Force / methods

Substances

DNA