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P. Jin

Bio: P. Jin is an academic researcher from Tsinghua University. The author has contributed to research in topics: Distortion & Digital image correlation. The author has an hindex of 1, co-authored 1 publications receiving 28 citations.

Papers
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Journal ArticleDOI
P. Jin1, X. Li1
TL;DR: A new method to correct drift and distortion aberrations of scanning electron microscope images, and an experiment shows that one pixel maximum correction is obtained for the employed high‐resolution electron microscopic system.
Abstract: Continuous research on small-scale mechanical structures and systems has attracted strong demand for ultrafine deformation and strain measurements. Conventional optical microscope cannot meet such requirements owing to its lower spatial resolution. Therefore, high-resolution scanning electron microscope has become the preferred system for high spatial resolution imaging and measurements. However, scanning electron microscope usually is contaminated by distortion and drift aberrations which cause serious errors to precise imaging and measurements of tiny structures. This paper develops a new method to correct drift and distortion aberrations of scanning electron microscope images, and evaluates the effect of correction by comparing corrected images with scanning electron microscope image of a standard sample. The drift correction is based on the interpolation scheme, where a series of images are captured at one location of the sample and perform image correlation between the first image and the consequent images to interpolate the drift-time relationship of scanning electron microscope images. The distortion correction employs the axial symmetry model of charged particle imaging theory to two images sharing with the same location of one object under different imaging fields of view. The difference apart from rigid displacement between the mentioned two images will give distortion parameters. Three-order precision is considered in the model and experiment shows that one pixel maximum correction is obtained for the employed high-resolution electron microscopic system.

37 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, a review of deep learning in electron microscopy is presented, with a focus on hardware and software needed to get started with deep learning and interface with electron microscopes.
Abstract: Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

59 citations

Journal ArticleDOI
TL;DR: A unified general framework to correct for the three dominant types of SEM artifacts, i.e. spatial distortion, drift distortion and scan line shifts is proposed and the potential of the framework is tested by a number of virtual experiments.
Abstract: 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.

33 citations

Journal ArticleDOI
31 Mar 2021
TL;DR: This review paper offers a practical perspective aimed at developers with limited familiarity of deep learning in electron microscopy that discusses hardware and software needed to get started with deep learning and interface with electron microscopes.
Abstract: Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

22 citations

Journal ArticleDOI
TL;DR: In this article, a direct sampling moire method (DSM) was developed for simple and fast analysis of grating distortion in high-resolution scanning electron microscope (SEM) images.

18 citations

Journal ArticleDOI
04 Aug 2017-Sensors
TL;DR: A novel material testing system that uses hierarchical designs for in-situ mechanical characterization of multiscale materials is reported, which can measure mechanical properties of materials with characteristic lengths ranging from millimeters to tens of nanometers while load capacity can vary from several hundred micronewtons to several nanonewtons.
Abstract: We report a novel material testing system (MTS) that uses hierarchical designs for in-situ mechanical characterization of multiscale materials. This MTS is adaptable for use in optical microscopes (OMs) and scanning electron microscopes (SEMs). The system consists of a microscale material testing module (m-MTM) and a nanoscale material testing module (n-MTM). The MTS can measure mechanical properties of materials with characteristic lengths ranging from millimeters to tens of nanometers, while load capacity can vary from several hundred micronewtons to several nanonewtons. The m-MTM is integrated using piezoelectric motors and piezoelectric stacks/tubes to form coarse and fine testing modules, with specimen length from millimeters to several micrometers, and displacement distances of 12 mm with 0.2 µm resolution for coarse level and 8 µm with 1 nm resolution for fine level. The n-MTM is fabricated using microelectromechanical system technology to form active and passive components and realizes material testing for specimen lengths ranging from several hundred micrometers to tens of nanometers. The system's capabilities are demonstrated by in-situ OM and SEM testing of the system's performance and mechanical properties measurements of carbon fibers and metallic microwires. In-situ multiscale deformation tests of Bacillus subtilis filaments are also presented.

16 citations