Institute of Advanced Machines and Design, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea.
Department of Mechanical Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea.
Small. 2022 Jan;18(3):e2103779. doi: 10.1002/smll.202103779. Epub 2021 Nov 27.
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.
原子力显微镜（AFM）是最受欢迎的成像和特性分析方法之一，适用于广泛的纳米级材料系统。然而，由于其光栅扫描的方法，使用 AFM 进行高分辨率成像通常会导致扫描效率低下。在这里，我们提出了一种系统的数据采集和准备方法，结合基于深度学习的图像超分辨率技术，实现了快速、准确的 AFM 特性分析。该方法在测量结构化 DNA 组装体的几何和机械特性方面的应用表明，在不显著降低准确性的情况下，AFM 成像时间可以减少约十倍。通过迁移学习策略，可以根据特定目标样本的需求对其进行高效定制。
