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Correction of Scanning Electron Microscope Imaging Artifacts in a Novel Digital Image Correlation Framework

机译:新型数字图像相关框架中扫描电子显微镜成像伪像的校正

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摘要

The combination of digital image correlation (DIC) and scanning electron microscopy (SEM) enables to extract high resolution full field displacement data, based on the high spatial resolution of SEM and the sub-pixel accuracy of DIC. However, SEM images may exhibit a considerable amount of imaging artifacts, which may seriously compromise the accuracy of the displacements and strains measured from these images. The current study proposes a unified general framework to correct for the three dominant types of SEM artifacts, i.e. spatial distortion, drift distortion and scan line shifts. The artifact fields are measured alongside the mechanical deformations to minimize the artifact induced errors in the latter. To this purpose, Integrated DIC (IDIC) is extended with a series of hierarchical mapping functions that describe the interaction of the imaging process with the mechanics. A new IDIC formulation based on these mapping functions is derived and the potential of the framework is tested by a number of virtual experiments. The effect of noise in the images and different regularization options for the artifact fields are studied. The error in the mechanical displacement fields measured for noise levels up to 5% is within the usual DIC accuracy range for all the cases studied, while it is more than 4 pixels if artifacts are ignored. A validation on real SEM images at three different magnifications confirms that all three distortion fields are accurately captured. The results of all virtual and real experiments demonstrate the accuracy of the methodology proposed, as well as its robustness in terms of convergence.
机译:基于SEM的高空间分辨率和DIC的亚像素精度,数字图像相关(DIC)和扫描电子显微镜(SEM)的结合可提取高分辨率的全场位移数据。但是,SEM图像可能会显示大量的成像伪像,这可能会严重损害从这些图像测量的位移和应变的准确性。当前的研究提出了一个统一的通用框架,以纠正SEM伪影的三种主要类型,即空间畸变,漂移畸变和扫描线偏移。在机械变形旁边测量伪影场,以最大程度地减少伪影引起的误差。为此,集成式DIC(IDIC)扩展了一系列的层次映射功能,这些功能描述了成像过程与力学之间的相互作用。推导了基于这些映射函数的新IDIC公式,并通过大量虚拟实验测试了该框架的潜力。研究了图像中噪声的影响以及伪影场的不同正则化选项。在所研究的所有情况下,针对高达5%的噪声水平测得的机械位移场中的误差均在常规DIC精度范围内,而如果忽略伪影,则其误差将超过4个像素。在三个不同放大倍数下对真实SEM图像的验证确认了所有三个畸变场均已被准确捕获。所有虚拟和真实实验的结果都证明了所提出方法的准确性,以及在收敛性方面的鲁棒性。

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