Neuromorphic computing has garnered significant attention because it can overcome the limitations of the current von-Neumann computing system. Analog synaptic devices are essential for realizing hardware-based artificial neuromorphic devices; however, only a few systematic studies in terms of both synaptic materials and device structures have been conducted so far, and thus, further research is required in this direction. In this study, we demonstrate the synaptic characteristics of a ferroelectric material-based thin-film transistor (FeTFT) that uses partial switching of ferroelectric polarization to implement analog conductance modulation. For a ferroelectric material, an aluminumdoped hafnium oxide (Al-doped HfO 2 ) thin film was prepared by atomic layer deposition. As an analog synaptic device, our FeTFT successfully emulated short-term plasticity and long-term plasticity characteristics, such as paired-pulse facilitation and spike timing-dependent plasticity. In addition, we obtained potentiation/depression weight updates with high linearity, an on/off ratio, and low cycle-to-cycle variation by adjusting the amplitude and number of input pulses. In the simulation trained with optimized potentiation/depression conditions, we achieved a pattern recognition accuracy of approximately 90% for the Modified National Institute of Standard and Technology (MNIST) handwritten data set. Our results indicated that ferroelectric transistors can be used as an alternative artificial synapse.

Continuous advancement in nonvolatile and morphotropic beyond-Moore electronic devices requires integration of ferroelectric and semiconductor materials. The emergence of hafnium oxide (HfO 2 )–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 HfO 2 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.

as core materials for future spintronics given their potential for high Curie temperature (T C ) and efficient field-tunable magnetic properties. [1][2][3] Despite half-century-long research efforts, the following three primary issues remain unsolved: i) the uncertain origin of ferromagnetism, called phantom ferromagnetism, owing to a lack of structural analysis of nanodefects; [4,5] ii) solubility limit to only a few percent without forming aggregation; [6] and iii) activation of short-range antiferromagnetic transitions in the high-dopingconcentration regime, [7] which limits the improvement of the magnetic moment and T C .The recent emergence of magnetic order in 2D van der Waals layered materials, which is enabled by strong magnetic anisotropy, [8] has stimulated interest in 2D-DMSs owing to their exotic spindependent physical properties, including long spin-relaxation time, light-controlled magnetism, [9] and spin-valley locking, inherent to their atomically thin nature. [10][11][12] In particular, transition metal dichalcogenide (TMD) semiconductors with magnetic dopants synthesized via chemical vapor deposition (CVD) offer room-temperature T C and gate-tunable magnetism. [13][14][15] Although vanadium dopants in WSe 2 and WS 2 semiconductors have been successfully distributed randomly without aggregation to a relatively high doping concentration of approximately 10%, their saturation magnetization is still limited to approximately 10 −5 emu cm −2 , thus making further analysis and applications difficult. [14,15] While magnetism has been proposed for inducing various defects such as vacancies, [16] anti-sites, [17] and grain boundaries [18] in III-V, oxides, and nitride DMSs, the underlying mechanism of magnetism is little known mainly due to the lack of structural analysis. On the contrary, because of facile monolayer growth, a variety of defects, including transition metal and chalcogen vacancies in 2D-TMDs, can be precisely analyzed using state-of-the-art scanning transmission electron microscopy (STEM) with atomic elemental mapping. [19] This affords the possibility of elucidating the origins of magnetism from defects and further enhancing magnetic order by tailoring intrinsic defects and impurities in 2D-TMD semiconductors. Here, we present a comprehensive atomic analysis of Se-vacancy defects 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 WSe 2 monolayer is herein demonstrated by tailoring complex configurations of Se vacancies (Se Vac ) 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-tre...

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.

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 Na 2 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.