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. A simple and practical way of accelerating atomic force microscopy (AFM) characterization enabled by a deep‐learning‐based image super‐resolution method combined with the data acquisition and preparation process is developed. Its application to measuring the geometrical and mechanical properties of DNA assemblies reveals that around a tenfold reduction of time in AFM characterization can be achieved without significant loss of accuracy.