Correcting nonlinear drift distortion of scanning probe and scanning transmission electron microscopies from image pairs with orthogonal scan directions

Ophus, Colin; Ciston, Jim; Nelson, Cindy

doi:10.1016/j.ultramic.2015.12.002

Cited by 191 publications

(166 citation statements)

References 18 publications

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“…Beam damage, drift distortion, and scan distortion are inherent issues that hinder quantitative interpretation of scanning transmission electron microscopy (STEM) imaging [1][2][3][4][5][6]. Beam damage occurs when the electron beam used to form the image transfers a critical amount of energy to the sample being examined, potentially causing damage or otherwise changing the subject of the experiment.…”

Section: Introductionmentioning

confidence: 99%

“…Also, there has been a recent resurgence of interest in applying methods to correct scan and drift distortions in STEM using frame averaging [2][3][4][5][6]. However, the success of these methods raises the question of whether the scan itself can be improved to eliminate some of the distortions during data acquisition rather than by post-processing.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic scan control in STEM: spiral scans

Sang

Lupini

Unocic

et al. 2016

Adv Struct Chem Imag

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Scanning transmission electron microscopy (STEM) has emerged as one of the foremost techniques to analyze materials at atomic resolution. However, two practical difficulties inherent to STEM imaging are: radiation damage imparted by the electron beam, which can potentially damage or otherwise modify the specimen and slow-scan image acquisition, which limits the ability to capture dynamic changes at high temporal resolution. Furthermore, due in part to scan flyback corrections, typical raster scan methods result in an uneven distribution of dose across the scanned area. A method to allow extremely fast scanning with a uniform residence time would enable imaging at low electron doses, ameliorating radiation damage and at the same time permitting image acquisition at higher frame-rates while maintaining atomic resolution. The practical complication is that rastering the STEM probe at higher speeds causes significant image distortions. Non-square scan patterns provide a solution to this dilemma and can be tailored for low dose imaging conditions. Here, we develop a method for imaging with alternative scan patterns and investigate their performance at very high scan speeds. A general analysis for spiral scanning is presented here for the following spiral scan functions: Archimedean, Fermat, and constant linear velocity spirals, which were tested for STEM imaging. The quality of spiral scan STEM images is generally comparable with STEM images from conventional raster scans, and the dose uniformity can be improved.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic scan control in STEM: spiral scans

Sang

Lupini

Unocic

et al. 2016

Adv Struct Chem Imag

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“…High precision STEM results in images in which random noise is suppressed well below the scattering generated by a single atom, and the position of atomic columns in the image can be determine to within <1 pm [1]. Other related approaches yield similar results [2,3]. This paper presents two applications of high precision STEM imaging to the structurally complex materials with a varying number of degrees of freedom in the atomic structure.…”

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confidence: 95%

Applications of High Precision STEM Imaging to Structurally Complex Materials

et al. 2017

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Aberration corrected electron microscopy has made Ångstrom resolution imaging routine in labs across the world. One of the next frontiers is improving the quantitative reliability of data about materials we can extract from aberration-corrected images. We have developed a methodology based on non-rigid registration of a series of short exposure time STEM images which we call high precision STEM [1]. High precision STEM results in images in which random noise is suppressed well below the scattering generated by a single atom, and the position of atomic columns in the image can be determine to within <1 pm [1]. Other related approaches yield similar results [2,3]. This paper presents two applications of high precision STEM imaging to the structurally complex materials with a varying number of degrees of freedom in the atomic structure.The first example is high precision STEM imaging of single La vacancies (VLa) in LaMnO3. We treat vacancies as defects with known atomic structure, derived from density functional theory calculations. As a result, the unknown parameters are the atomic coordinates of the missing La atom. Using high precision STEM, we can image VLa both from the reduction in the intensity of the atomic column containing the vacancy and from the displacement of the neighboring La-containing columns in toward the vacancy. For relatively thin TEM sample, ~10 nm thick, the visibility of a single VLa is well above the ~1% intensity uncertainty in high precision STEM for any depth of the vacancy. In three dimensions, the neighbor La displacements are 20-30 pm, but only a maximum of 8 pm survives the two dimensional projection and probe channeling of the STEM imaging process, and when the VLa is near the exit surface, the displacements in the image are nearly zero. Both the visibility and the displacements depend on the depth of the VLa through channeling of the electron probe, but the depth dependencies are not the same. Therefore, by combining information about the visibility and displacements, we can determine the depth of the vacancy, in addition the two dimensional position available directly from the image. Figure 1 shows an example high precision STEM image of LaMnO3, with the probability of selected column containing a vacancy superimposed. The probability is evaluated by comparing the experimental visibility and displacements to a library of simulations using a Bayesian model. The library of simulations give the probability of observing a particular visibility and set of displacements, given a vacancy at a particular depth and an estimate of experimental errors. Bayes' theorem lets us find the inverse probability, the probability that a vacancy exists at a particular depth, given a set of observations.The second example is high precision STEM imaging combined with computational structural refinement to solve the structure of nanoparticles. Nanoparticles have many more degrees of freedom, due to the need to specify the shape of the particle and the position of atoms on the surface which generally do n...

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“…Computer vision techniques can be applied for automated detection and segmentation of structures in 2-and 3-D micrographs and 3-D reconstruction, as well as for deriving quantitative data from electron microscopy data [1][2][3].3-D reconstruction of one-dimensional crystal defects called dislocations reveals key information about their network geometry and dominant interaction mechanisms [4]. Conventional tomography techniques in transmission electron microscopy (TEM) usually use a tilt-series of projections to retrieve the 3-D structure of many objects, including dislocation lines, through different reconstruction schemes [5].…”

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confidence: 99%

mentioning

confidence: 99%

Computer Vision Techniques Applied to the Reconstruction of the 3-D Structure Dislocations

Oveisi¹,

Zanet

Fua

et al. 2017

Microsc Microanal

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Computer-vision, nowadays well integrated into electron microscopy sciences, is becoming an essential implement for the advancement of this field, in particular to take the most advantage of the cutting-edge electron microscopes. Computer vision techniques can be applied for automated detection and segmentation of structures in 2-and 3-D micrographs and 3-D reconstruction, as well as for deriving quantitative data from electron microscopy data [1][2][3].3-D reconstruction of one-dimensional crystal defects called dislocations reveals key information about their network geometry and dominant interaction mechanisms [4]. Conventional tomography techniques in transmission electron microscopy (TEM) usually use a tilt-series of projections to retrieve the 3-D structure of many objects, including dislocation lines, through different reconstruction schemes [5]. The linear shape of dislocations can be incorporated as an important prior knowledge to lower the number of images that are needed for a reliable reconstruction.In this contribution, we integrate several computational technologies including machine learning, segmentation methodologies, epipolar geometry, and triangulation to develop an efficient method for multi-view 3-D reconstruction of dislocation lines. We use state-of-the-art convolutional neural networks, specifically a U-Net, to first delineate dislocations and subsequently finding corresponding points as optical flow [6]. 3-D reconstruction is performed using an affine camera model estimated from point correspondences.The strength of this method is experimentally demonstrated on the reconstruction of dislocations using bright-field, weak-beam and high-angle annular dark-field S/TEM images. As demonstrated in figure 1, dislocation lines were accurately traced in the 2-D BF-STEM images of a heteroepitaxial gallium nitride (GaN) membrane. Our 3-D reconstruction algorithm employed well-established computer vision techniques to match contours across images and to infer their 3-D shape. Finally, the 3-D configuration of these dislocations was reconstructed using these two images whose viewing directions are separated by only 3.2° [7]. The algorithm also takes the specificity of the setup into account by introducing customized smoothing techniques based on Gaussian filters of different sizes to overcome the nonhomogeneous nature of the noise along different axes. The experiments that have been performed on real and synthetic data assert the approach is able to reach a significant precision in the 3-D reconstruction of dislocations lines even using a single pair of stereo images with a stereo tilt angle that spans from a few to some tens of degrees [7][8][9].

show abstract

Correcting nonlinear drift distortion of scanning probe and scanning transmission electron microscopies from image pairs with orthogonal scan directions

Cited by 191 publications

References 18 publications

Dynamic scan control in STEM: spiral scans

Dynamic scan control in STEM: spiral scans

Applications of High Precision STEM Imaging to Structurally Complex Materials

Computer Vision Techniques Applied to the Reconstruction of the 3-D Structure Dislocations

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