Atomistic Probing of Defect-Engineered 2H-MoTe2 Monolayers.

Point defects dictate various physical, chemical, and optoelectronic properties of two-dimensional (2D) materials, and therefore, a rudimentary understanding of the formation and spatial distribution of point defects is a key to advancement in 2D material-based nanotechnology. In this work, we performed the demonstration to directly probe the point defects in 2H-MoTe2 monolayers that are tactically exposed to (i) 200 °C-vacuum-annealing and (ii) 532 nm-laser-illumination; and accordingly, we utilize a deep learning algorithm to classify and quantify the generated point defects. We discovered that tellurium-related defects are mainly generated in both 2H-MoTe2 samples; but interestingly, 200 °C-vacuum-annealing and 532 nm-laser-illumination modulate a strong n-type and strong p-type 2H-MoTe2, respectively. While 200 °C-vacuum-annealing generates tellurium vacancies or tellurium adatoms, 532 nm-laser-illumination prompts oxygen atoms to be adsorbed/chemisorbed at tellurium vacancies, giving rise to the p-type characteristic. This work significantly advances the current understanding of point defect engineering in 2H-MoTe2 monolayers and other 2D materials, which is critical for developing nanoscale devices with desired functionality.

Full automation of point defect detection in transition metal dichalcogenides through a dual mode deep learning algorithm.

Point defects often appear in two-dimensional (2D) materials and are mostly correlated with physical phenomena. The direct visualisation of point defects, followed by statistical inspection, is the most promising way to harness structure-modulated 2D materials. Here, we introduce a deep learning-based platform to identify the point defects in 2H-MoTe2: synergy of unit cell detection and defect classification. These processes demonstrate that segmenting the detected hexagonal cell into two unit cells elaborately cropped the unit cells: further separating a unit cell input into the Te2/Mo column part remarkably increased the defect classification accuracies. The concentrations of identified point defects were 7.16 × 1020 cm2 of Te monovacancies, 4.38 × 1019 cm2 of Te divacancies and 1.46 × 1019 cm2 of Mo monovacancies generated during an exfoliation process for TEM sample-preparation. These revealed defects correspond to the n-type character mainly originating from Te monovacancies, statistically. Our deep learning-oriented platform combined with atomic structural imaging provides the most intuitive and precise way to analyse point defects and, consequently, insight into the defect-property correlation based on deep learning in 2D materials.

Atomically Thin Synapse Networks on Van Der Waals Photo-Memtransistors.

A new type of atomically thin synaptic network on van der Waals (vdW) heterostructures is reported, where each ultrasmall cell (≈2 nm thick) built with trilayer WS2 semiconductor acts as a gate-tunable photoactive synapse, i.e., a photo-memtransistor. A train of UV pulses onto the WS2 memristor generates dopants in atomic-level precision by direct light-lattice interactions, which, along with the gate tunability, leads to the accurate modulation of the channel conductance for potentiation and depression of the synaptic cells. Such synaptic dynamics can be explained by a parallel atomistic resistor network model. In addition, it is shown that such a device scheme can generally be realized in other 2D vdW semiconductors, such as MoS2 , MoSe2 , MoTe2 , and WSe2 . Demonstration of these atomically thin photo-memtransistor arrays, where the synaptic weights can be tuned for the atomistic defect density, provides implications for a new type of artificial neural networks for parallel matrix computations with an ultrahigh integration density.

Gate-tunable quantum pathways of high harmonic generation in graphene.

Under strong laser fields, electrons in solids radiate high-harmonic fields by travelling through quantum pathways in Bloch bands in the sub-laser-cycle timescales. Understanding these pathways in the momentum space through the high-harmonic radiation can enable an all-optical ultrafast probe to observe coherent lightwave-driven processes and measure electronic structures as recently demonstrated for semiconductors. However, such demonstration has been largely limited for semimetals because the absence of the bandgap hinders an experimental characterization of the exact pathways. In this study, by combining electrostatic control of chemical potentials with HHG measurement, we resolve quantum pathways of massless Dirac fermions in graphene under strong laser fields. Electrical modulation of HHG reveals quantum interference between the multi-photon interband excitation channels. As the light-matter interaction deviates beyond the perturbative regime, elliptically polarized laser fields efficiently drive massless Dirac fermions via an intricate coupling between the interband and intraband transitions, which is corroborated by our theoretical calculations. Our findings pave the way for strong-laser-field tomography of Dirac electrons in various quantum semimetals and their ultrafast electronics with a gate control.

Heteroepitaxial van der Waals semiconductor superlattices.

A broad range of transition metal dichalcogenide (TMDC) semiconductors are available as monolayer (ML) crystals, so the precise integration of each kind into van der Waals (vdW) superlattices (SLs) could enable the realization of novel structures with previously unexplored functionalities. Here we report the atomic layer-by-layer epitaxial growth of vdW SLs with programmable stacking periodicities, composed of more than two kinds of dissimilar TMDC MLs, such as MoS2, WS2 and WSe2. Using kinetics-controlled vdW epitaxy in the near-equilibrium limit by metal-organic chemical vapour depositions, we achieved precise ML-by-ML stacking, free of interlayer atomic mixing, which resulted in tunable two-dimensional vdW electronic systems. As an example, by exploiting the series of type II band alignments at coherent two-dimensional vdW heterointerfaces, we demonstrated valley-polarized carrier excitations-one of the most distinctive electronic features in vdW ML semiconductors-which scale with the stack numbers n in our (MoS2/WS2)n SLs on optical excitations.

Identification of Point Defects in Atomically Thin Transition-Metal Dichalcogenide Semiconductors as Active Dopants.

Selective doping in semiconductors is essential not only for monolithic integrated circuity fabrications but also for tailoring their properties including electronic, optical, and catalytic activities. Such active dopants are essentially point defects in the host lattice. In atomically thin two-dimensional (2D) transition-metal dichalcogenides (TMDCs), the roles of such point defects are particularly critical in addition to their large surface-to-volume ratio, because their bond dissociation energy is relatively weaker, compared to elemental semiconductors. In this Mini Review, we review recent advances in the identifications of diverse point defects in 2D TMDC semiconductors, as active dopants, toward the tunable doping processes, along with the doping methods and mechanisms in literature. In particular, we discuss key issues in identifying such dopants both at the atomic scales and the device scales with selective examples. Fundamental understanding of these point defects can hold promise for tunability doping of atomically thin 2D semiconductor platforms.