Secondary electron (SE) doping contrast in the scanning electron microscope is correlated with Fermi level pinned surfaces of Si samples prepared using HF-based wet-chemical treatment or focused ion beam (FIB) micromachining en route to quantitative dopant profiling. Using energy-resolved SE imaging techniques and finite-element analyses of surface states and surface junction potentials, we clarified the surface band-bending effects post-NH4F-treatment, consistent with brighter p-contrast from degenerately doped (>1019 cm−3) regions. In general, SE spectromicroscopy scan measurements unambiguously indicated heavy suppression of patch fields, while the empirical discovery of scan frequency-modulated contrast inversion due to Chee et al. [Springer Proceedings in Physics, 120, pp. 407–410 (2008)] is ascribable to competing fixed oxide charge and dynamic charge injection phenomena (particularly at dwell times >29 μs). Leveraging numerical simulations of electric potentials and variable-voltage experimental data, the theoretical model based on amorphization damage-mediated Fermi level pinning is elucidated for Ga+ FIB-processed site-specific doping contrast on patch field-free surfaces. This work successfully argues against the notion that doping contrast ultimately or exclusively entails patch fields or adventitious metal–semiconductor contacts.

Hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and uniform manifold approximation and projection (UMAP), two new state-of-the-art algorithms for clustering analysis, and dimensionality reduction, respectively, are proposed for the segmentation of core-loss electron energy loss spectroscopy (EELS) spectrum images. The performances of UMAP and HDBSCAN are systematically compared to the other clustering analysis approaches used in EELS in the literature using a known synthetic dataset. Better results are found for these new approaches. Furthermore, UMAP and HDBSCAN are showcased in a real experimental dataset from a core–shell nanoparticle of iron and manganese oxides, as well as the triple combination nonnegative matrix factorization–UMAP–HDBSCAN. The results obtained indicate how the complementary use of different combinations may be beneficial in a real-case scenario to attain a complete picture, as different algorithms highlight different aspects of the dataset studied.

Carbon fiber technology drives significant development in lightweight and multifunctional applications. However, the microstructure of carbon fibers is not completely understood. A big challenge is to obtain the distribution of heteroatoms, for instance nitrogen, with high spatial resolution in three dimensions. Atom probe tomography (APT) has the potential to meet this challenge, but APT of carbon fibers is still relatively unexplored. We performed APT on three types of carbon fibers, including one high modulus type and two intermediate modulus types. Here, we present the methods to interpret the complex mass spectra of carbon fibers, enhance the mass resolution, and increase the obtained analysis volume. Finally, the origin of multiple hit events and possible methods to mitigate multiple hit events are also discussed. This paper provides guidance for future APT studies on carbon fibers, and thus leads the way to a deeper understanding of the microstructure, and consequently advancements in wide applications of carbon fibers.

Electron microprobe-based quantitative compositional measurement of first-row transition metals using their L$\alpha$ X-ray lines is hampered by, among other effects, self-absorption. This effect, which occurs when a broad X-ray line is located close to a broad absorption edge, is not accounted for by matrix corrections. To assess the error due to neglecting self-absorption, we calculate the L$\alpha$ X-ray intensity emitted from metallic Fe, Ni, Cu, and Zn targets, assuming a Lorentzian profile for the X-ray line and taking into account the energy dependence of the mass absorption coefficient near the absorption edge. We find that calculated X-ray intensities depart increasingly, for increasing electron beam energy, from those obtained assuming a narrow X-ray line and a single fixed absorption coefficient (conventional approach), with a maximum deviation of $\sim$15% for Ni and of $\sim$10% for Fe. In contrast, X-ray intensities calculated for metallic Zn and Cu do not differ significantly from those obtained using the conventional approach. The implications of these results for the analysis of transition-metal compounds by electron probe microanalysis as well as strategies to account for self-absorption effects are discussed.

Three independent analysis methods were developed to investigate the distribution of solid mass in foams analyzed by X-ray tomography with effective pixel sizes larger than the thickness of the solid network (sub-pixel conditions). Validation of the methods was achieved by a comparison with the results obtained employing high-resolution tomography for the same set of foams. The foams showed different solid mass distribution, which varied from being preferentially located on the edges, with a fraction of mass in the struts nearing 0.6, to materials in which the fraction of mass in the struts was low, under 0.15. In all cases, the accuracy of the proposed approaches was greater for materials with a higher fraction of mass in the struts. The method based on deconvolution of the attenuation probability density function yielded the closest results to the high-resolution characterizations. In contrast, analysis of the solid matrix thickness distribution after watershed segmentation, and binarization of high thickness regions (struts segmentation) required normalization through macroscopic measurements and revealed higher deviations with respect to the high-resolution results. However, segmentation-based methods allowed investigation of the heterogeneity of the fraction of mass in the struts along the sample.

Visualizing how a catalyst behaves during chemical reactions using in situ transmission electron microscopy (TEM) is crucial for understanding the activity origin and guiding performance optimization. However, the sample drifts as temperature changes during in situ reaction, which weakens the resolution and stability of TEM imaging, blocks insights into the dynamic details of catalytic reaction. Herein, a Thon-ring based sample position measurement (TSPM) was developed to track the sample height variation during in situ TEM observation. Drifting characteristics for three commercially available nanochips were studied, showing large biases in aspects of shifting modes, expansion heights, as well as the thermal conduction hysteresis during rapid heating. Particularly, utilizing the TSPM method, for the first time, the gas layer thickness inside a gas-cell nanoreactor was precisely determined, which varies with reaction temperature and gas pressure in a linear manner with coefficients of ~8 nm/°C and ~50 nm/mbar, respectively. Following drift prediction of TSPM, fast oxidation kinetics of a Ni particle was tracked in real time for 12 s at 500°C. This TSPM method is expected to facilitate the functionality of automatic target tracing for in situ microscopy applications when feedback to hardware control of the microscope.

A method for three-dimensional reconstruction of objects from defocused images collected at multiple illumination directions in high-resolution transmission electron microscopy is presented. The method effectively corrects for the Ewald sphere curvature by taking into account the in-particle propagation of the electron beam. Numerical simulations demonstrate that the proposed method is capable of accurately reconstructing biological molecules or nanoparticles from high-resolution defocused images under conditions achievable in single-particle electron cryo-microscopy or electron tomography with realistic radiation doses, non-trivial aberrations, multiple scattering, and other experimentally relevant factors. The physics of the method is based on the well-known Diffraction Tomography formalism, but with the phase-retrieval step modified to include a conjugation of the phase (i.e., multiplication of the phase by a negative constant). At each illumination direction, numerically backpropagating the beam with the conjugated phase produces maximum contrast at the location of individual atoms in the molecule or nanoparticle. The resultant algorithm, Conjugated Holographic Reconstruction, can potentially be incorporated into established software tools for single-particle analysis, such as, for example, RELION or FREALIGN, in place of the conventional contrast transfer function correction procedure, in order to account for the Ewald sphere curvature and improve the spatial resolution of the three-dimensional reconstruction.

Due to less light scattering and a better signal-to-noise ratio in deep imaging, two-photon fluorescence microscopy (TPFM) has been widely used in biomedical photonics since its advent. However, optical aberrations degrade the performance of TPFM in terms of the signal intensity and the imaging depth and therefore restrict its application. Here, we introduce adaptive optics based on the genetic algorithm to detect the distorted wavefront of the excitation laser beam and then perform aberration correction to optimize the performance of TPFM. By using a spatial light modulator as the wavefront controller, the correction phase is obtained through a signal feedback loop and a process of natural selection. The experimental results show that the signal intensity and imaging depth of TPFM are improved after aberration correction. Finally, the method was applied to two-photon fluorescence lifetime imaging, which helps to improve the signal-to-noise ratio and the accuracy of lifetime analysis. Furthermore, the method can also be implemented in other experiments, such as three-photon microscopy, light-sheet microscopy, and super-resolution microscopy.

Over the last few years, a new mode for imaging in the scanning transmission electron microscope (STEM) has gained attention as it permits the direct visualization of sample conductivity and electrical connectivity. When the electron beam (e-beam) is focused on the sample in the STEM, secondary electrons (SEs) are generated. If the sample is conductive and electrically connected to an amplifier, the SE current can be measured as a function of the e-beam position. This scenario is similar to the better-known scanning electron microscopy-based technique, electron beam-induced current imaging, except that the signal in the STEM is generated by the emission of SEs, hence the name secondary electron e-beam-induced current (SEEBIC), and in this case, the current flows in the opposite direction. Here, we provide a brief review of recent work in this area, examine the various contrast generation mechanisms associated with SEEBIC, and illustrate its use for the characterization of graphene nanoribbon devices.

Low electron dose observation is indispensable for observing various samples using a transmission electron microscope; consequently, image processing has been used to improve transmission electron microscopy (TEM) images. To apply such image processing to in situ observations, we here apply a convolutional neural network to TEM imaging. Using a dataset that includes short-exposure images and long-exposure images, we develop a pipeline for processed short-exposure images, based on end-to-end training. The quality of images acquired with a total dose of approximately $5$ $e^{-}$ per pixel becomes comparable to that of images acquired with a total dose of approximately $1{,}000$ $e^{-}$ per pixel. Because the conversion time is approximately 8 ms, in situ observation at 125 fps is possible. This imaging technique enables in situ observation of electron-beam-sensitive specimens.

Here, we report a case study implementation of reinforcement learning (RL) to automate operations in the scanning transmission electron microscopy workflow. To do so, we design a virtual, prototypical RL environment to test and develop a network to autonomously align the electron beam position without prior knowledge. Using this simulator, we evaluate the impact of environment design and algorithm hyperparameters on alignment accuracy and learning convergence, showing robust convergence across a wide hyperparameter space. Additionally, we deploy a successful model on the microscope to validate the approach and demonstrate the value of designing appropriate virtual environments. Consistent with simulated results, the on-microscope RL model achieves convergence to the goal alignment after minimal training. Overall, the results highlight that by taking advantage of RL, microscope operations can be automated without the need for extensive algorithm design, taking another step toward augmenting electron microscopy with machine learning methods.

This paper describes a reconstruction method for atom probe tomography based on a bottom-up approach accounting for (i) the final tip morphology (which is frequently induced by inhomogeneous evaporation probabilities across the tip surface due to laser absorption, heat diffusion effects, and inhomogeneous material properties), (ii) the limited (and changing) field of view, and (iii) the detector efficiency. The reconstruction starts from the final tip morphology and reverses the evaporation sequence through the pseudo-deposition of defined small reconstruction volumes, which are then stacked together to create the full three-dimensional (3D) tip. The subdivision in small reconstruction volumes allows the scheme to account for the changing tip shape and field of view as evaporation proceeds. Atoms within the same small reconstruction volume are reconstructed at once by placing atoms back onto their possible lattice sites through a trajectory-matching process involving simulated and experimental hit maps. As the ejected ion trajectories are simulated using detailed electrostatic modeling inside the chamber, no simplifications have been imposed on the shape of the trajectories, projection laws, or tip surface. We demonstrate the superior performance of our approach over the conventional reconstruction method (Bas) for an asymmetrical tip shape.

It is known that 2D materials can exhibit a nonflat topography, which gives rise to an inherent strain. Since local curvature and strain influence mechanical, optical, and electrical properties, but are often difficult to distinguish from each other, a robust measurement technique is needed. In this study, a novel method is introduced, which is capable of obtaining quantitative strain and topography information of 2D materials with nanometer resolution. Relying on scanning nanobeam electron diffraction (NBED), it is possible to disentangle strain from the local sample slope. Using the positions of the diffraction spots of a specimen at two different tilts to reconstruct the locations and orientations of the reciprocal lattice rods, the local strain and slope can be simultaneously retrieved. We demonstrate the differences to strain measurements from a single NBED map in theory, simulation, and experiment. MoS2 monolayers with different shapes are used as simulation test structures. The slope and height information are recovered, as well as tensile and angular strain which have an absolute difference of less than 0.2% and 0.2° from the theoretical values. An experimental proof of concept using a freely suspended WSe2 monolayer together with a discussion of the accuracy of the method is provided.

Crystalline materials used in technological applications are often complex assemblies composed of multiple phases and differently oriented grains. Robust identification of the phases and orientation relationships from these samples is crucial, but the information extracted from the diffraction condition probed by an electron beam is often incomplete. We have developed an automated crystal orientation mapping (ACOM) procedure which uses a converged electron probe to collect diffraction patterns from multiple locations across a complex sample. We provide an algorithm to determine the orientation of each diffraction pattern based on a fast sparse correlation method. We demonstrate the speed and accuracy of our method by indexing diffraction patterns generated using both kinematical and dynamical simulations. We have also measured orientation maps from an experimental dataset consisting of a complex polycrystalline twisted helical AuAgPd nanowire. From these maps we identify twin planes between adjacent grains, which may be responsible for the twisted helical structure. All of our methods are made freely available as open source code, including tutorials which can be easily adapted to perform ACOM measurements on diffraction pattern datasets.

Atom probe tomography (APT) is often introduced as providing “atomic-scale” mapping of the composition of materials and as such is often exploited to analyze atomic neighborhoods within a material. Yet quantifying the actual spatial performance of the technique in a general case remains challenging, as it depends on the material system being investigated as well as on the specimen's geometry. Here, by using comparisons with field-ion microscopy experiments, field-ion imaging and field evaporation simulations, we provide the basis for a critical reflection on the spatial performance of APT in the analysis of pure metals, low alloyed systems and concentrated solid solutions (i.e., akin to high-entropy alloys). The spatial resolution imposes strong limitations on the possible interpretation of measured atomic neighborhoods, and directional neighborhood analyses restricted to the depth are expected to be more robust. We hope this work gets the community to reflect on its practices, in the same way, it got us to reflect on our work.

Warping is a limiting factor when preparing transmission electron microscopy (TEM) samples using focused ion beam (FIB)/scanning electron microscope (SEM) systems. The conventional FIB sputtering process leaves at least one side of the lamella too thin to provide structural support to offset inherent stresses. As a result, warping can occur impacting imagining and reducing the potential size of lamellae. For example, capturing more than a few back-end metal layers in a 3 μm wide cross-section lamella can be difficult. Frequently, TEM analysts must perform multiple stage adjustments to analyze such a sample. In this paper, two methods are presented that enable FIB/SEM operators to prepare TEM samples where the thinned region of interest is surrounded by thick structures. As a result, these methods reduce warping and enable the fabrication of TEM lamellae not possible by conventional means. For example, these methods have been used to produce a 10 μm wide by 8 μm tall cross-section TEM sample that captured front-end transistors and 14 back-end metal layers. Furthermore, warping was so limited that only one alignment was needed by the TEM analyst to complete the imaging of the sample. The methods, called the horizontal bracing and window methods, make use of the deposition of low-Z amorphous films that are electron transparent in the TEM.

We present a fast and precise deep-learning architecture, which we term O-Net, for obtaining super-resolved images from conventional phase-modulated optical microscopical techniques, such as phase-contrast microscopy and differential interference contrast microscopy. O-Net represents a novel deep convolutional neural network that can be trained on both simulated and experimental data, the latter of which is being demonstrated in the present context. The present study demonstrates the ability of the proposed method to achieve super-resolved images even under poor signal-to-noise ratios and does not require prior information on the point spread function or optical character of the system. Moreover, unlike previous state-of-the-art deep neural networks (such as U-Nets), the O-Net architecture seemingly demonstrates an immunity to network hallucination, a commonly cited issue caused by network overfitting when U-Nets are employed. Models derived from the proposed O-Net architecture are validated through empirical comparison with a similar sample imaged via scanning electron microscopy (SEM) and are found to generate ultra-resolved images which came close to that of the actual SEM micrograph.