Despite numerous studies on two-dimensional van der Waals heterostructures, a full understanding of the charge transport and photoinduced current mechanisms in these structures, in particular, associated with charge depletion/inversion layers at the interface remains elusive. Here, we investigate transport properties of a prototype multilayer MoS/WSe heterojunction via a tunable charge inversion/depletion layer. A charge inversion layer was constructed at the surface of WSe due to its relatively low doping concentration compared to that of MoS, which can be tuned by the back-gate bias. The depletion region was limited within a few nanometers in the MoS side, while charges are fully depleted on the whole WSe side, which are determined by Raman spectroscopy and transport measurements. Charge transport through the heterojunction was influenced by the presence of the inversion layer and involves two regimes of tunneling and recombination. Furthermore, photocurrent measurements clearly revealed recombination and space-charge-limited behaviors, similar to those of the heterostructures built from organic semiconductors. This contributes to research of various other types of heterostructures and can be further applied for electronic and optoelectronic devices.
Vertically stacked two-dimensional van der Waals (vdW) heterostructures, used to obtain homogeneity and band steepness at interfaces, exhibit promising performance for band-to-band tunneling (BTBT) devices. Esaki tunnel diodes based on vdW heterostructures, however, yield poor current density and peak-to-valley ratio, inferior to those of three-dimensional materials. Here, we report the negative differential resistance (NDR) behavior in a WSe2/SnSe2 heterostructure system at room temperature and demonstrate that heterointerface control is one of the keys to achieving high device performance by constructing WSe2/SnSe2 heterostructures in inert gas environments. While devices fabricated in ambient conditions show poor device performance due to the observed oxidation layer at the interface, devices fabricated in inert gas exhibit extremely high peak current density up to 1460 mA/mm2, 3–4 orders of magnitude higher than reported vdW heterostructure-based tunnel diodes, with a peak-to-valley ratio of more than 4 at room temperature. Besides, Pd/WSe2 contact in our device possesses a much higher Schottky barrier than previously reported Cr/WSe2 contact in the WSe2/SnSe2 device, which suppresses the thermionic emission current to less than the BTBT current level, enabling the observation of NDR at room temperature. Diode behavior can be further modulated by controlling the electrostatic doping and the tunneling barrier as well.
With the development of depth sensors and skeleton tracking algorithms, many skeletonbased pathological gait classification methods have recently been proposed. However, these methods classify only simple gait patterns, and there is no approach to classify complicated gait patterns. In this paper, we classify 1 normal and 5 pathological gaits (antalgic, stiff-legged, lurching, steppage, and Trendelenburg gaits) by using a gated recurrent unit (GRU)-based classifier and 3D skeleton data. We collected skeleton datasets for 1 normal and 5 pathological gaits by using a multiperspective Kinect system. We developed the GRU classifier to classify the pathological gaits and compared its performance with that of other machine learning-based classifiers. Furthermore, we considered various joint groups to identify important and irrelevant joints for pathological gait classification and to improve the performance of the GRU classifier. When all skeleton data are used, the GRU classifier achieves a classification accuracy of 90.13%. A long short-term memory (LSTM)-based classifier achieves the next highest accuracy of 87.25%. The classification accuracy of the GRU classifier depends on the joint groups considered, and the classification accuracy increases to 93.67% when only leg joints are considered. This study indicates that various pathological gaits can be classified by using skeleton data and the GRU classifier. The proposed method can be used to support medical and clinical decisions. Furthermore, the results for various joint groups can be used to develop other methods in cases where only specific joint data are available because of environmental limitations.
The enhanced growth of Cu oxides underneath graphene grown on a Cu substrate has been of great interest to many groups. In this work, the strain and doping status of graphene, based on the gradual growth of Cu oxides from underneath, were systematically studied using time evolution Raman spectroscopy. The compressive strain to graphene, due to the thermal expansion coefficient difference between graphene and the Cu substrate, was almost released by the nonuniform Cu2O growth; however, slight tensile strain was exerted. This induced p-doping in the graphene with a carrier density up to 1.7 × 1013 cm–2 when it was exposed to air for up to 30 days. With longer exposure to ambient conditions (>1 year), we observed that graphene/Cu2O hybrid structures significantly slow down the oxidation compared to that using a bare Cu substrate. The thickness of the CuO layer on the bare Cu substrate was increased to approximately 270 nm. These findings were confirmed through white light interference measurements and scanning electron microscopy.
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