Hybrid Deep Learning Crystallographic Mapping of Polymorphic Phases in Polycrystalline Hf0.5 Zr0.5 O2 Thin Films.

By controlling the configuration of polymorphic phases in high-k Hf0.5 Zr0.5 O2 thin films, new functionalities such as persistent ferroelectricity at an extremely small scale can be exploited. To bolster the technological progress and fundamental understanding of phase stabilization (or transition) and switching behavior in the research area, efficient and reliable mapping of the crystal symmetry encompassing the whole scale of thin films is an urgent requisite. Atomic-scale observation with electron microscopy can provide decisive information for discriminating structures with similar symmetries. However, it often demands multiple/multiscale analysis for cross-validation with other techniques, such as X-ray diffraction, due to the limited range of observation. Herein, an efficient and automated methodology for large-scale mapping of the crystal symmetries in polycrystalline Hf0.5 Zr0.5 O2 thin films is developed using scanning probe-based diffraction and a hybrid deep convolutional neural network at a 2 nm2 resolution. The results for the doped hafnia films are fully proven to be compatible with atomic structures revealed by microscopy imaging, not requiring intensive human input for interpretation.

Escalating Ferromagnetic Order via Se-Vacancies Near Vanadium in WSe2 Monolayers.

Magnetic order has been proposed to arise from a variety of defects, including vacancies, antisites, and grain boundaries, which are relevant in numerous electronics and spintronics applications. Nevertheless, its magnetism remains controversial due to the lack of structural analysis. The escalation of ferromagnetism in vanadium-doped WSe2 monolayer is herein demonstrated by tailoring complex configurations of Se vacancies (SeVac ) via post heat-treatment. Structural analysis of atomic defects is systematically performed using transmission electron microscopy (TEM), enabled by the monolayer nature. Temperature-dependent magnetoresistance hysteresis ensures enhanced magnetic order after high-temperature heat-treatment, consistent with magnetic domain analysis from magnetic force microscopy (MFM). The vanadium-Se vacancy pairing is a key to promoting ferromagnetism via spin-flip by electron transfer, predicted from density-functional-theory (DFT) calculations. The approach toward nanodefect engineering paves a way to overcome weak magnetic order in diluted magnetic semiconductors (DMSs) for renovating semiconductor spintronics.

Highly enhanced ferroelectricity in HfO2-based ferroelectric thin film by light ion bombardment.

Continuous advancement in nonvolatile and morphotropic beyond-Moore electronic devices requires integration of ferroelectric and semiconductor materials. The emergence of hafnium oxide (HfO2)-based ferroelectrics that are compatible with atomic-layer deposition has opened interesting and promising avenues of research. However, the origins of ferroelectricity and pathways to controlling it in HfO2 are still mysterious. We demonstrate that local helium (He) implantation can activate ferroelectricity in these materials. The possible competing mechanisms, including He ion-induced molar volume changes, vacancy redistribution, vacancy generation, and activation of vacancy mobility, are analyzed. These findings both reveal the origins of ferroelectricity in this system and open pathways for nanoengineered binary ferroelectrics.

Deep Learning-Assisted Quantification of Atomic Dopants and Defects in 2D Materials.

Atomic dopants and defects play a crucial role in creating new functionalities in 2D transition metal dichalcogenides (2D TMDs). Therefore, atomic-scale identification and their quantification warrant precise engineering that widens their application to many fields, ranging from development of optoelectronic devices to magnetic semiconductors. Scanning transmission electron microscopy with a sub-Å probe has provided a facile way to observe local dopants and defects in 2D TMDs. However, manual data analytics of experimental images is a time-consuming task, and often requires subjective decisions to interpret observed signals. Therefore, an approach is required to automate the detection and classification of dopants and defects. In this study, based on a deep learning algorithm, fully convolutional neural network that shows a superior ability of image segmentation, an efficient and automated method for reliable quantification of dopants and defects in TMDs is proposed with single-atom precision. The approach demonstrates that atomic dopants and defects are precisely mapped with a detection limit of ≈1 × 1012 cm-2 , and with a measurement accuracy of ≈98% for most atomic sites. Furthermore, this methodology is applicable to large volume of image data to extract atomic site-specific information, thus providing insights into the formation mechanisms of various defects under stimuli.

Regulating Te Vacancies through Dopant Balancing via Excess Ag Enables Rebounding Power Factor and High Thermoelectric Performance in p-Type PbTe.

Thermoelectric properties are frequently manipulated by introducing point defects into a matrix. However, these properties often change in unfavorable directions owing to the spontaneous formation of vacancies at high temperatures. Although it is crucial to maintain high thermoelectric performance over a broad temperature range, the suppression of vacancies is challenging since their formation is thermodynamically preferred. In this study, using PbTe as a model system, it is demonstrated that a high thermoelectric dimensionless figure of merit, zT ≈ 2.1 at 723 K, can be achieved by suppressing the vacancy formation via dopant balancing. Hole-killer Te vacancies are suppressed by Ag doping because of the increased electron chemical potential. As a result, the re-dissolution of Na2 Te above 623 K can significantly increase the hole concentration and suppress the drop in the power factor. Furthermore, point defect scattering in material systems significantly reduces lattice thermal conductivity. The synergy between defect and carrier engineering offers a pathway for achieving a high thermoelectric performance by alleviating the power factor drop and can be utilized to enhance thermoelectric properties of thermoelectric materials.