Ultramicroscopy最新文献

An innovative software with a user-friendly interface for calculation of differential phase contrast (DPC) scanning transmission electron microscopy images (integrated iDPC- and differentiated dDPC-STEM) is presented. The underlying algorithm is described and the program functionalities are demonstrated on the examples of Li₅OsO₆, α-Ga₂O₃, and LiCoO₂. The software supports interpretation of DPC-STEM images, which is crucial for qualitative and quantitative analysis of crystal structures and defects.

This study investigates the impact of the surface electric field on the quantification accuracy of boron (B) implanted silicon (Si) using atom probe tomography (APT). The Si Charge-State Ratio (CSR(Si) = Si²⁺/Si⁺) was used as an indirect measure of the average apex electric field during analysis. For a range of electric fields, the accuracy of the total implanted dose and the depth profile shape determined by APT was evaluated against the National Institute of Standards and Technology Standard Reference Material 2137. The radial (non-)uniformity of the detected B was also examined. At a higher surface electric field (i.e., a greater CSR(Si)), the determined B dose converges on the certified dose. Additionally, the depth profile shape tends towards that derived by secondary ion mass spectrometry. This improvement coincides with a more uniform radial B distribution, evidenced by desorption maps. In contrast, for lower surface electric fields (i.e., a lower CSR(Si)), the B dose is significantly underestimated, and the depth profile is artificially stretched. The desorption maps also indicate a highly inhomogeneous B emission localized around the center of the detector, which is believed to be an artifact of B surface migration on the tip of the sample. For the purposes of routine investigations of semiconductor devices using APT, these results illustrate the potential origin of quantification artifacts and their severity at different operating conditions, thus providing pathways towards best practices for accurate and repeatable measurements.

Genetic algorithm (GA) and particle swarm optimisation (PSO) techniques have been integrated with the differential algebra (DA) method in charged particle optics to optimise an Einzel lens. The DA method is a robust and efficient tool for the calculation of aberration coefficients of electrostatic lenses, which makes use of nonstandard analysis for ray tracing a particle as it is subjected to the field generated by a lens. In this study, initial populations of lenses with random geometrical configurations are generated. These initial populations are then subjected to GA and PSO algorithms to alter the geometry of each lens for a set number of iterations. The lens performance is evaluated by calculating the spot size using the aberrations coefficients up to third-order generated by the DA method. Moreover, a focusing column comprising two lenses and a Wien filter was optimised using GA method.

The convergent-beam low energy electron diffraction technique has been proposed as a novel method to gather local structural and electronic information from crystalline surfaces during low-energy electron microscopy. However, the approach suffers from high complexity of the resulting diffraction patterns. We show that Convolutional Autoencoders trained on CBLEED patterns achieve a highly structured latent space. The latent space is then used to estimate structural parameters with sub-angstrom accuracy. The low complexity of the neural networks enables real time application of the approach during experiments with low latency.

Nowadays, 3D Electron Diffraction (3DED) is widely used for the structure determination of sub-micron-sized particles. In this work, we investigate the influence of the acceleration voltage on the quality of 3DED datasets acquired on BaTiO₃ nanoparticles. Datasets were acquired using a wide range of beam energies, from common, high acceleration voltages (300 kV and 200 kV) to medium (120 kV and 80 kV) and low acceleration voltages (60 kV and 30 kV). It was observed that, in the integration process, R_int increases as the beam energy is reduced, which is mainly due to the increased dynamical scattering. Nevertheless, the structure was solved successfully in all cases. The structure refinement was comparable for all beam energies with small deficiencies such as negative atomic displacements for the heaviest atom in the structure, barium. Including extinction correction in the refinement noticeably improved the model for low acceleration voltages, probably due to higher beam absorption in these cases. Dynamical refinement, however, shows superior results for higher acceleration voltages, since the dynamical refinement calculations currently ignore inelastic scattering effects.

The diffraction patterns of crystalline materials with local order contain sharp Bragg reflections as well as highly structured diffuse scattering. In this study, we quantitatively show how the diffuse scattering in three-dimensional electron diffraction (3D ED) data is influenced by various parameters, including the data acquisition mode, the detector type and the use of an energy filter. We found that diffuse scattering data used for quantitative analysis are preferably acquired in selected area electron diffraction (SAED) mode using a CCD and an energy filter. In this study, we also show that the diffuse scattering in 3D ED data can be obtained with a quality comparable to that from single-crystal X-ray diffraction. As electron diffraction requires much smaller crystal sizes than X-ray diffraction, this opens up the possibility to investigate the local structure of many technologically relevant materials for which no crystals large enough for single-crystal X-ray diffraction are available.

Beside its main purpose as a high-end tool in material analysis reaching the atomic scale for structure, chemical and electronic properties, aberration-corrected scanning transmission electron microscopy (STEM) is increasingly used as a tool to manipulate materials down to that very same scale. In order to obtain exact and reproducible results, it is essential to consider the interaction processes and interaction ranges between the electron beam and the involved materials. Here, we show in situ that electron beam-induced etching in a low-pressure oxygen atmosphere can extend up to a distance of several nm away from the Ångström-size electron beam, usually used for probing the sample. This relatively long-range interaction is related to beam tails and inelastic scattering involved in the etching process. To suppress the influence of surface diffusion, we measure the etching effect indirectly on isolated nm-sized holes in a 2 nm thin amorphous carbon foil that is commonly used as sample support in STEM. During our experiments, the electron beam is placed inside the nanoholes so that most electrons cannot directly participate in the etching process. We characterize the etching process from measuring etching rates at multiple nanoholes with different distances between the hole edge and the electron beam.

Laser micromachining can serve as a coarse machining step during sample preparation for high-resolution characterization methods leading to swift sample preparation. However, selecting the right laser parameters is crucial to minimize the heat-affected zone, which can potentially compromise the microstructure of the specimen. This study focuses on evaluating the size of heat-affected zone in laser annular milling, aiming to ascertain a minimal scan diameter that safeguards the inner region of micropillars against thermal damage. A computational model based on the finite element method was utilized to simulate the laser heating process. To validate the simulation results, a picosecond pulsed laser is then used to machine the micropillars of Al and Si. The laser-machined samples were subjected to surface and microstructural analysis using Scanning Electron Microscope (SEM) and Electron Backscatter Diffraction (EBSD) scans. The length of heat affected zone obtained from simulations was approximately 6 $\mu$m for silicon and 12 $\mu$m for aluminum. The diameter of micropillars formed with laser machining was 10 $\mu$m for silicon 26 $\mu$m for aluminum. The core of the pillars was preserved with less than one degree of microstructural misorientations making it suitable for further processing for preparing specimens for techniques like APT and TEM. For silicon micropillars, the preserved central region has a diameter of 6 $\mu$m and for aluminum its around 20–24 $\mu$m. Additionally, the study determines the minimum scan diameter that can be achieved using the given laser machining setup across a range of common materials.

Structural and chemical characterization of nanomaterials provides important information for understanding their functional properties. Nanomaterials with characteristic structure sizes in the nanometer range can be characterized by scanning transmission electron microscopy (STEM). In conventional STEM, two-dimensional (2D) projection images of the samples are acquired, information about the third dimension is lost. This drawback can be overcome by STEM tomography, where the three-dimensional (3D) structure is reconstructed from a series of projection images acquired using various projection directions. However, 3D measurements are expensive with respect to acquisition and evaluation time. Furthermore, the method is hardly applicable to beam-sensitive materials, i.e. samples that degrade under the electron beam. For this reason, it is desirable to know whether sufficient information on structural and chemical information can be extracted from 2D-projection measurements. In the present work, a comparison between 3D-reconstruction and 2D-projection characterization of structure and mixing in nanoparticle hetero-aggregates is provided. To this end, convolutional neural networks are trained in 2D and 3D to extract particle positions and material types from the simulated or experimental measurement. Results are used to evaluate structure, particle size distributions, hetero-aggregate compositions and mixing of particles quantitatively and to find an answer to the question, whether an expensive 3D characterization is required for this material system for future characterizations.

Cryogenic Scanning/Transmission Electron Microscopy has been established as a leading method to image sensitive biological samples and is now becoming a powerful tool to understand materials' behavior at low temperatures. However, achieving precise local temperature calibration at low temperatures remains a challenge, which is especially crucial for studying phase transitions and emergent physical properties in quantum materials. In this study, we employ electron energy loss spectroscopy (EELS) to measure local cryogenic specimen temperatures. We use the temperature-dependent characteristics of aluminum's bulk plasmon peak in EEL spectra, which shifts due to changes in electron density caused by thermal expansion and contraction. We successfully demonstrate the versatility of this method by calibrating different liquid nitrogen cooling holders in various microscopes, regardless of whether a monochromated or non-monochromated electron beam is used. Temperature discrepancies between the actual temperature and the setpoint temperatures are identified across a range from room temperature to 100 K. This work demonstrates the importance of temperature calibrations at intermediate temperatures and presents a straightforward, robust method for calibrating local temperatures of cryogenically-cooled specimens in electron microscopes.