Abstract Material properties strongly depend on the nature and concentration of defects. Characterizing these features may require nano- to atomic-scale resolution to establish structure–property relationships. 4D-STEM, a technique where diffraction patterns are acquired at a grid of points on the sample, provides a versatile method for highlighting defects. Computational analysis of the diffraction patterns with virtual detectors produces images that can map material properties. Here, using multislice simulations, we explore different virtual detectors that can be applied to the diffraction patterns that go beyond the binary response functions that are possible using ordinary STEM detectors. Using graphene and lead titanate as model systems, we investigate the application of virtual detectors to study local order and in particular defects. We find that using a small convergence angle with a rotationally varying detector most efficiently highlights defect signals. With experimental graphene data, we demonstrate the effectiveness of these detectors in characterizing atomic features, including vacancies, as suggested in simulations. Phase and amplitude modification of the electron beam provides another process handle to change image contrast in a 4D-STEM experiment. We demonstrate how tailored electron beams can enhance signals from short-range order and how a vortex beam can be used to characterize local symmetry.
more »
« less
Variational Convolutional Autoencoders for Anomaly Detection in Scanning Transmission Electron Microscopy
Identifying point defects and other structural anomalies using scanning transmission electron microscopy (STEM) is important to understand a material's properties caused by the disruption of the regular pattern of crystal lattice. Due to improvements in instrumentation stability and electron optics, atomic‐resolution images with a field of view of several hundred nanometers can now be routinely acquired at 1–10 Hz frame rates and such data, which often contain thousands of atomic columns, need to be analyzed. To date, image analysis is performed largely manually, but recent developments in computer vision (CV) and machine learning (ML) now enable automated analysis of atomic structures and associated defects. Here, the authors report on how a Convolutional Variational Autoencoder (CVAE) can be utilized to detect structural anomalies in atomic‐resolution STEM images. Specifically, the training set is limited to perfect crystal images , and the performance of a CVAE in differentiating between single‐crystal bulk data or point defects is demonstrated. It is found that the CVAE can reproduce the perfect crystal data but not the defect input data. The disagreesments between the CVAE‐predicted data for defects allows for a clear and automatic distinction and differentiation of several point defect types.
more » « less- NSF-PAR ID:
- 10391775
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Small
- Volume:
- 19
- Issue:
- 16
- ISSN:
- 1613-6810
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Abstract Electron backscattering diffraction provides the analysis of crystalline phases at large scales (microns) while precession electron diffraction may be used to get 4D‐STEM data to elucidate structure at nanometric resolution. Both are limited by the probe size and also exhibit some difficulties for the generation of large datasets, given the inherent complexity of image acquisition. The latter appoints the application of advanced machine learning techniques, such as deep learning adapted for several tasks, including pattern matching, image segmentation, etc. This research aims to show how Gabor filters provide an appropriate feature extraction technique for electron microscopy images that could prevent the need of large volumes of data to train deep learning models. The work presented herein combines an algorithm based on Gabor filters for feature extraction and an unsupervised learning method to perform particle segmentation of polyhedral metallic nanoparticles and crystal orientation mapping at atomic scale. Experimental results have shown that Gabor filters are convenient for electron microscopy images analysis, that even a nonsupervised learning algorithm can provide remarkable results in crystal segmentation of individual nanoparticles. This approach enables its application to dynamic analysis of particle transformation recorded with aberration‐corrected microscopy, offering new possibilities of analysis at nanometric scale.
-
Length Scale and Dimensionality of Defects in Epitaxial SnTe Topological Crystalline Insulator Filmsmore » « less
Topological crystalline insulators (TCIs) are new materials with metallic surface states protected by crystal symmetry. The properties of molecular beam epitaxy grown SnTe TCI on SrTiO3(001) are investigated using scanning tunneling microscopy (STM), noncontact atomic force microscopy, low‐energy and reflection high‐energy electron diffraction, X‐ray diffraction, Auger electron spectroscopy, and density functional theory. Initially, SnTe (111) and (001) surfaces are observed; however, the (001) surface dominates with increasing film thickness. The films grow island‐by‐island with the [011] direction of SnTe (001) islands rotated up to 7.5° from SrTiO3[010]. Microscopy reveals that this growth mechanism induces defects on different length scales and dimensions that affect the electronic properties, including point defects (0D); step edges (1D); grain boundaries between islands rotated up to several degrees; edge‐dislocation arrays (2D out‐of‐plane) that serve as periodic nucleation sites for pit growth (2D in‐plane); and screw dislocations (3D). These features cause variations in the surface electronic structure that appear in STM images as standing wave patterns and a nonuniform background superimposed on atomic features. The results indicate that both the growth process and the scanning probe tip can be used to induce symmetry breaking defects that may disrupt the topological states in a controlled way.
-
more » « less
Abstract Defect mitigation of electronic devices is conventionally achieved using thermal annealing. To mobilize the defects, very high temperatures are necessary. Since thermal diffusion is random in nature, the process may take a prolonged period of time. In contrast, we demonstrate a room temperature annealing technique that takes only a few seconds. The fundamental mechanism is defect mobilization by atomic scale mechanical force originating from very high current density but low duty cycle electrical pulses. The high-energy electrons lose their momentum upon collision with the defects, yet the low duty cycle suppresses any heat accumulation to keep the temperature ambient. For a 7 × 105A cm−2pulsed current, we report an approximately 26% reduction in specific on-resistance, a 50% increase of the rectification ratio with a lower ideality factor, and reverse leakage current for as-fabricated vertical geometry GaN p–n diodes. We characterize the microscopic defect density of the devices before and after the room temperature processing to explain the improvement in the electrical characteristics. Raman analysis reveals an improvement in the crystallinity of the GaN layer and an approximately 40% relaxation of any post-fabrication residual strain compared to the as-received sample. Cross-sectional transmission electron microscopy (TEM) images and geometric phase analysis results of high-resolution TEM images further confirm the effectiveness of the proposed room temperature annealing technique to mitigate defects in the device. No detrimental effect, such as diffusion and/or segregation of elements, is observed as a result of applying a high-density pulsed current, as confirmed by energy dispersive x-ray spectroscopy mapping.
-
more » « less
Abstract Modern electronic systems rely on components with nanometer-scale feature sizes in which failure can be initiated by atomic-scale electronic defects. These defects can precipitate dramatic structural changes at much larger length scales, entirely obscuring the origin of such an event. The transmission electron microscope (TEM) is among the few imaging systems for which atomic-resolution imaging is easily accessible, making it a workhorse tool for performing failure analysis on nanoscale systems. When equipped with spectroscopic attachments TEM excels at determining a sample’s structure and composition, but the physical manifestation of defects can often be extremely subtle compared to their effect on electronic structure. Scanning TEM electron beam-induced current (STEM EBIC) imaging generates contrast directly related to electronic structure as a complement the physical information provided by standard TEM techniques. Recent STEM EBIC advances have enabled access to a variety of new types of electronic and thermal contrast at high resolution, including conductivity mapping. Here we discuss the STEM EBIC conductivity contrast mechanism and demonstrate its ability to map electronic transport in both failed and pristine devices.
