球差校正扫描透射电子显微镜(scanning transmission electron microscope,STEM)是一种重要的微观结构表征手段.然而,由于电子束和样品漂移等问题,极大影响了 STEM图像的质量和后续分析.针对上述问题,本文引入机器学习,改进了原子识别的方法,并在此基础上进行了元素分类;另外,针对单张STEM图像,在原子识别的基础上,提出了快速非线性漂移校正的方法,解决了以往漂移校正方法依赖较多数据的问题,此方法适用于辐照敏感材料的漂移校正,显著提高了 STEM图像的解析效率.
Atomic identification and nonlinear drift correction based on machine learning
Spherical aberration-corrected scanning transmission electron microscopy(STEM)is a crucial tool for characterizing microscale structures.However,issues such as electron beam and sample drift can significantly affect the quality of STEM images and subsequent analysis.To address these challenges,this paper introduced a machine learning approach to improve atomic identification,followed by elemental classification.Additionally,a rapid nonlinear drift correction method for a single STEM image was proposed,building upon atomic identification.This method overcomed the previous data-dependency issue in drift correction and was applicable for drift correction in radiation-sensitive materials.It significantly enhanced the resolution efficiency of STEM images.
nonlinear drift correctionatomic identificationmachine learningtransmission electron microscopy
万方数据
维普
