School of Aerospace Engineering, Xiamen University, Xiamen 361005, China.
College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
Anal Chem. 2022 Mar 29;94(12):5041-5047. doi: 10.1021/acs.analchem.1c05056. Epub 2022 Mar 16.
Atomic force microscopy (AFM) provides unprecedented insight into surface topography research with ultrahigh spatial resolution at the subnanometer level. However, a slow scanning rate has to be employed to ensure the image quality, which will largely increase the accumulated sample drift, thereby, resulting in the low fidelity of the AFM image. In this paper, we propose a fast imaging method which performs a complete fast Raster scanning and a slow μ-path subsampling together with a deep learning algorithm to rapidly produce an AFM image with high quality and small drift. A supervised convolutional neural network (CNN) model is trained with the slow μ-path subsampled data and its counterpart acquired with fast Raster scan. The fast speed acquired AFM image is then inputted to the well-trained CNN model to output the high quality one. We validate the reliability of this method using a silicon grids sample and further apply it to the fast imaging of a vanadium dioxide thin film. The results demonstrate that this method can largely improve the imaging speed up to 10.3 times with state-of-the-art imaging quality, and reduce the sample drift by 8.9 times in the multiframe AFM imaging of the same area. Furthermore, we prove that this method is also applicable to other scanning imaging techniques such as scanning electrochemical microscopy.
原子力显微镜（AFM）以亚纳米级的超高空间分辨率提供了对表面形貌研究的前所未有的深入了解。然而，为了确保图像质量，必须采用缓慢的扫描速率，这将大大增加样品漂移的累积，从而导致 AFM 图像的低保真度。在本文中，我们提出了一种快速成像方法，该方法将完整的快速光栅扫描与慢速 μ 路径亚采样以及深度学习算法相结合，以快速生成高质量和小漂移的 AFM 图像。使用慢速 μ 路径亚采样数据及其与快速光栅扫描获得的对应数据来训练有监督的卷积神经网络（CNN）模型。然后将快速获取的 AFM 图像输入到训练有素的 CNN 模型中，以输出高质量的图像。我们使用硅网格样品验证了该方法的可靠性，并进一步将其应用于钒氧化物薄膜的快速成像。结果表明，与最先进的成像质量相比，该方法可以大大提高成像速度，高达 10.3 倍，并在相同区域的多帧 AFM 成像中减少 8.9 倍的样品漂移。此外，我们证明该方法也适用于其他扫描成像技术，如扫描电化学显微镜。
