Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario, Canada.
Nanotechnology. 2019 Feb 15;30(7):075703. doi: 10.1088/1361-6528/aaf353. Epub 2018 Nov 23.
The determination of quantitative structure-property relations is a vital but challenging task for nanostructured materials research due to the presence of large-scale spatially varying patterns resulting from nanoscale processes such as self-assembly and nano-lithography. Focusing on nanostructured surfaces, recent advances have been made in automated quantification methods for orientational and translational order using shapelet functions, originally developed for analysis of images of galaxies, as a reduced-basis for surface pattern structure. In this work, a method combining shapelet functions and machine learning is developed and applied to a representative set of images of self-assembled surfaces from experimental characterization techniques including scanning electron miscroscopy, atomic force microscopy and transmission electron microscopy. The method is shown to be computationally efficient and able to quantify salient pattern features including deformation, defects, and grain boundaries from a broad range of patterns typical of self-assembly processes.
定量结构-性质关系的确定是纳米结构材料研究中一项至关重要但具有挑战性的任务，这是由于纳米级过程（如自组装和纳米光刻）会产生大规模的空间变化模式。本研究关注于纳米结构表面，最近开发了一种使用形状基函数（最初是为分析星系图像而开发的）的自动化量化方法，用于定向和平移有序性，作为表面图案结构的简化基础。在这项工作中，开发了一种结合形状基函数和机器学习的方法，并将其应用于一组具有代表性的自组装表面图像，这些图像来自于包括扫描电子显微镜、原子力显微镜和透射电子显微镜在内的实验特征化技术。该方法被证明具有计算效率，并且能够从典型的自组装过程的广泛图案中量化突出的图案特征，包括变形、缺陷和晶界。
