Search

Observations of emergent quantum phases in twisted bilayer graphene prompted a flurry of activities in van-der-Waals (vdW) materials beyond graphene. Most current twisted experiments use a so-called tear-and-stack method using a polymer called PPC. However, despite the clear advantage of the current PPC tear-and-stack method, there are also technical limitations, mainly a limited number of vdW materials that can be studied using this PPC-based method. This technical bottleneck has been preventing further development of the exciting field beyond a few available vdW samples. To overcome this challenge and facilitate future expansion, we developed a new tear-and-stack method using a strongly adhesive polycaprolactone (PCL). With similar angular accuracy, our technology allows fabrication without a capping layer, facilitating surface analysis and ensuring inherently clean interfaces and low operating temperatures. More importantly, it can be applied to many other vdW materials that have remained inaccessible with the PPC-based method. We present our results on twist homostructures made with a wide choice of vdW materials - from two well-studied vdW materials (graphene and MoS$_2$) to the first-ever demonstrations of other vdW materials (NbSe$_2$, NiPS$_3$, and Fe$_3$GeTe$_2$). Therefore, our new technique will help expand $moir\acute{e}$ physics beyond few selected vdW materials and open up more exciting developments., Comment: 26 pages, 4+7 figures, accepted to 2D Materials

The unique discovery of magnetic exciton in van der Waals antiferromagnet NiPS3 arises between two quantum many-body states of a Zhang-Rice singlet excited state and a Zhang-Rice triplet ground state. Simultaneously, the spectral width of photoluminescence originating from this exciton is exceedingly narrow as 0.4 meV. These extraordinary properties, including the extreme coherence of the magnetic exciton in NiPS3, beg many questions. We studied doping effects using Ni1-xCdxPS3 using two experimental techniques and theoretical studies. Our experimental results show that the magnetic exciton is drastically suppressed upon a few % Cd doping. All these happen while the width of the exciton only gradually increases, and the antiferromagnetic ground state is robust. These results highlight the lattice uniformity's hidden importance as a prerequisite for coherent magnetic exciton. Finally, an exciting scenario emerges: the broken charge transfer forbids the otherwise uniform formation of the coherent magnetic exciton in (Ni,Cd)PS3., Comment: 40 pages, 4 main figures, 13 supporting figures, accepted by Nano Letters

Advancements in adapting deep convolution architectures for Spiking Neural Networks (SNNs) have significantly enhanced image classification performance and reduced computational burdens. However, the inability of Multiplication-Free Inference (MFI) to align with attention and transformer mechanisms, which are critical to superior performance on high-resolution vision tasks, imposing limitations on these gains. To address this, our research explores a new pathway, drawing inspiration from the progress made in Multi-Layer Perceptrons (MLPs). We propose an innovative spiking MLP architecture that uses batch normalization to retain MFI compatibility and introducing a spiking patch encoding layer to enhance local feature extraction capabilities. As a result, we establish an efficient multi-stage spiking MLP network that blends effectively global receptive fields with local feature extraction for comprehensive spike-based computation. Without relying on pre-training or sophisticated SNN training techniques, our network secures a top-1 accuracy of 66.39% on the ImageNet-1K dataset, surpassing the directly trained spiking ResNet-34 by 2.67%. Furthermore, we curtail computational costs, model parameters, and simulation steps. An expanded version of our network compares with the performance of the spiking VGG-16 network with a 71.64% top-1 accuracy, all while operating with a model capacity 2.1 times smaller. Our findings highlight the potential of our deep SNN architecture in effectively integrating global and local learning abilities. Interestingly, the trained receptive field in our network mirrors the activity patterns of cortical cells. Source codes are publicly accessible at https://github.com/EMI-Group/mixer-snn., Comment: IEEE TNNLS

The nature of electronic motion and structural information of atoms and molecules is encoded into strong-field induced radiations ranging from terahertz (THz) to extreme ultraviolet wavelength. The dependence of THz yields in bi-chromatic laser fields on ellipticity and interpulse phase delay were experimentally measured, and the trajectory calculations establish the link between the THz emission and the motion of the photoelectron wave packet. The interaction between the photoelectron and parent core transforms from soft collision to recollision as the laser field tuned from elliptical to linear polarization, which can be reflected in THz emission. The soft collision is found to be more effective in reconstructing electron dynamics through THz polarization, which enables to construct the effective core potential of the generating medium with the Coulomb-reshaped THz radiation in an elliptically polarized laser field. Our work allows designing innovative all-optical THz measurements of electronic and structural dynamics.

The orientation and ellipticity of terahertz (THz) polarization generated by two-color strong field not only cast light on underlying mechanisms of laser-matter interaction, but also play an important role for various applications. We develop the Coulomb-corrected classical trajectory Monte Carlo (CTMC) method to well reproduce the joint measurements, that the THz polarization generated by the linearly-polarized 800 nm and circularly-polarized 400 nm fields is independent on two-color phase delay. The trajectory analysis shows that the Coulomb potential twists the THz polarization by deflecting the orientation of asymptotic momentum of electron trajectories. Further, the CTMC calculations predict that, the two-color mid-infrared field can effectively accelerate the electron rapidly away from the parent core to relieve the disturbance of Coulomb potential, and simultaneously create large transverse acceleration of trajectories, leading to the circularly-polarized THz radiation.

We report unidirectional charge transport in a $\mathrm{NbSe_2}$ noncentrosymmetric superconductor, which is exchange-coupled with a $\mathrm{CrPS_4}$ van der Waals layered antiferromagnetic insulator. The $\mathrm{NbSe_2/CrPS_4}$ bilayer device exhibits bias-dependent superconducting critical-current variations of up to $16\%$, with the magnetochiral anisotropy reaching $\sim 10^5\mathrm{\ T^{-1}A^{-1}}$. Furthermore, the $\mathrm{CrPS_4/NbSe_2/CrPS_4}$ spin-valve structure exhibits the superconducting diode effect with critical-current variations of up to $40\%$. We also utilize the magnetic proximity effect to induce switching in the superconducting state of the spin-valve structure. It exhibits an infinite magnetoresistance ratio depending on the field sweep direction and magnetization configuration. Our result demonstrates a novel route for enhancing the nonreciprocal response in the weak external field regime ($<50\mathrm{\ mT}$) by exploiting the magnetic proximity effect., Comment: 25 pages, 3 figures, 2 supplemental figures

Ferromagnetism in two-dimensional materials presents a promising platform for the development of ultrathin spintronic devices with advanced functionalities. Recently discovered ferromagnetic van der Waals crystals such as CrI$_{3}$, readily isolated two-dimensional crystals, are highly tunable through external fields or structural modifications. However, there remains a challenge because of material instability under air exposure. Here, we report the observation of an air stable and layer dependent ferromagnetic (FM) van der Waals crystal, CrPS$_{4}$, using magneto-optic Kerr effect microscopy. In contrast to the antiferromagnetic (AFM) bulk, the FM out-of-plane spin orientation is found in the monolayer crystal. Furthermore, alternating AFM and FM properties observed in even and odd layers suggest robust antiferromagnetic exchange interactions between layers. The observed ferromagnetism in these crystals remains resilient even after the air exposure of about a day, providing possibilities for the practical applications of van der Waals spintronics., Comment: 22 pages, 5 figures

Robust multi-level spin memory with the ability to write information electrically is a long-sought capability in spintronics, with great promise for applications. Here we achieve nonvolatile and highly energy-efficient magnetization switching in a single-material device formed of van-der-Waals topological ferromagnet Fe3GeTe2, whose magnetic information can be readily controlled by a tiny current. Furthermore, the switching current density and power dissipation are about 400 and 4000 times smaller than those of the existing spin-orbit-torque magnetic random access memory based on conventional magnet/heavy-metal systems. Most importantly, we also demonstrate multi-level states, switched by electrical current, which can dramatically enhance the information capacity density and reduce computing costs. Thus, our observations combine both high energy efficiency and large information capacity density in one device, showcasing the potential applications of the emerging field of van-der-Waals magnets in the field of spin memory and spintronics., Comment: Accepted by Advanced Functional Materials; 28 pages, 5 main figures, 4 supporting figures

Controlling magnetic states by a small current is essential for the next-generation of energy-efficient spintronic devices. However, it invariably requires considerable energy to change a magnetic ground state of intrinsically quantum nature governed by fundamental Hamiltonian, once stabilized below a phase transition temperature. We report that surprisingly an in-plane current can tune the magnetic state of nm-thin van der Waals ferromagnet Fe3GeTe2 from a hard magnetic state to a soft magnetic state. It is the direct demonstration of the current-induced substantial reduction of the coercive field. This surprising finding is possible because the in-plane current produces a highly unusual type of gigantic spin-orbit torque for Fe3GeTe2. And we further demonstrate a working model of a new nonvolatile magnetic memory based on the principle of our discovery in Fe3GeTe2, controlled by a tiny current. Our findings open up a new window of exciting opportunities for magnetic van der Waals materials with potentially huge impacts on the future development of spintronic and magnetic memory., Comment: 61 pages, 4 main figures, 14 supporting figures

Anisotropy in electronic structures may ignite intriguing anisotropic optical responses, as has been well demonstrated in various systems including superconductors, semiconductors, and even topological Weyl semimetals. Meanwhile, it is well established in metal optics that the metal reflectance declines from one to zero when the photon frequency is above the plasma frequency {\omega}p , behaving as a plasma mirror. However, the exploration of anisotropic plasma mirrors and corresponding applications remains elusive, especially at room temperature. Here, we discover a pronounced anisotropic plasma reflectance edge in the type-II Weyl semimetal WP2, with an anisotropy ratio of {\omega}p up to 1.5. Such anisotropic plasma mirror behavior and its robustness against temperature promise optical device applications over a wide temperature range. For example, the high sensitivity of polarization-resolved plasma reflectance edge renders WP2 an inherent polarization detector. We further achieve a room-temperature WP2-based optical switch, effectively controlled by simply tuning the light polarization. These findings extend the frontiers of metal optics as a discipline and promise the design of multifunctional devices combining both topological and optical features.

One-dimensional (1D) confinement has been revealed to effectively tune the properties of materials in homogeneous states. The 1D physics can be further enriched by electronic inhomogeneity, which unfortunately remains largely unknown. Here we demonstrate the ultra-high sensitivity to magnetic fluctuations and the tunability of phase stability in the electronic transport properties of self-assembled electronically phase-separated manganite nanowires with extreme aspect ratio. The onset of magnetic nano-droplet state, a precursor to the ferromagnetic metallic state, is unambiguously revealed, which is attributed to the small lateral size of the nanowires that is comparable to the droplet size. Moreover, the quasi-1D anisotropy stabilizes thin insulating domains to form intrinsic tunneling junctions in the low temperature range, which is robust even under magnetic field up to 14 T, and thus essentially modifies the classic 1D percolation picture to stabilize a novel quantum percolation state. A new phase diagram is therefore established for the manganite system under quasi-1D confinement for the first time. Our findings offer new insight to understand and manipulate the colorful properties of the electronically phase-separated systems via dimensionality engineering.

Weyl semimetal emerges as a new topologically nontrivial phase of matter, hosting low-energy excitations of massless Weyl fermions. Here, we present a comprehensive study of the type-II Weyl semimetal WP2. Transport studies show a butterfly-like magnetoresistance at low temperature, reflecting the anisotropy of the electron Fermi surfaces. The four-lobed feature gradually evolves into a two-lobed one upon increasing temperature, mainly due to the reduced relative contribution of electron Fermi surfaces compared to hole Fermi surfaces for the magnetoresistance. Moreover, angle-dependent Berry phase is further discovered from the quantum oscillations, which is ascribed to the effective manipulation of the extremal Fermi orbits by the magnetic field to feel the nearby topological singularities in the momentum space. The revealed topological characters and anisotropic Fermi surfaces of WP2 substantially enrich the physical properties of Weyl semimetals and hold great promises in topological electronic and Fermitronic device applications., Comment: 22 pages, 7 figures, accepted by the journal "Chinese Physics Letters"

Controlling the stacking of van der Waals (vdW) materials is found to produce exciting new findings, since hetero- or homo- structures have added the diverse possibility of assembly and manipulated functionalities. However, so far, the homostructure with a twisted angle based on the magnetic vdW materials remains unexplored. Here, we achieved a twisted magnetic vdW Fe3GeTe2/Fe3GeTe2 junction with broken crystalline symmetry. A clean and metallic vdW junction is evidenced by the temperature-dependent resistance and the linear I-V curve. Unlike the pristine FGT, a plateau-like magnetoresistance (PMR) is observed in the magnetotransport of our homojunction due to the antiparallel magnetic configurations of the two FGT layers. The PMR ratio is found to be ~0.05% and gets monotonically enhanced as temperature decreases like a metallic giant magnetoresistance (GMR). Such a tiny PMR ratio is at least three orders of magnitude smaller than the tunneling magnetoresistance (TMR) ratio, justifying our clean metallic junction without a spacer. Our findings demonstrate the feasibility of the controllable homostructure and shed light on future spintronics using magnetic vdW materials., Comment: 10 pages, 4 figures, accepted for publication in Journal of Applied physics

Electric voltage applied in electrowetting can induce speading, sliding and even jumping of an individual droplet by changing the intrinsic balance of three-phase interfacial tensions, which has been widely used for droplet manipulating in microfluidics and lab-on-a-chip devices in over decades. In the present paper, we present an analytical prediction of jumping velocity for droplets electrowetting on textured hydrophobic surfaces with different wetting states. In particular, we consider a liquid droplet wetting on a textured hydrophobic substrate with a voltage applied between the droplet and the substrate. Once the voltage is turned off, the energy stored in the droplet during the electrowetting releases and could even result in the detachment of the droplet. The effects of the initial and electro-wetting states, i.e. Cassie-Baxter state and Wenzel state, on the jumping velocity of droplets are systematically discussed. Based on energy conservation, the energy conversion between the surface energy, internal viscous dissipation and the kinetic energy of droplets in different wetting states are analysed. The close-form formulas to predict the jumping velocity for different droplet wetting states are systematically derived. Finally, the unified form for predicting the electrowetting-induced jumping velocity of droplets on both flat and textured substrates with different wetting states is obtained, which can describe the jumping motion with various wetting conditions. This work provide theoretical insights on accurate control of the electrowetting-induced jumping motion of droplets on textured hydrophobic surfaces.

The dynamic properties of fluid, including density, surface tension, diffusivity and viscosity, are temperature-dependent and can significantly influence the flow dynamics of mesoscopic non-isothermal systems. To capture the correct temperature-dependence of a fluid, a many-body dissipative particle dynamics model with energy conservation (mDPDe) is developed by combining the temperature-dependent coefficient of the conservative force and weighting terms of the dissipative and random forces. The momentum and thermal diffusivity, viscosity, and surface tension of liquid water at various temperatures ranging from 273 K to 373 K are used as examples for verifying the proposed model. Simulations of a periodic Poiseuille flow driven by body forces and heat sources are carried out to validate the diffusivity of the present model. Also, a steady case of heat conduction for reproducing the Fourier law is used to validate the thermal boundary conditions. By using this mDPDe simulations, the thermocapillary motion of liquid water nanodroplets on hydrophobic substrates with thermal gradients is investigated. The migration of the droplet is observed on flat substrates with gradient temperature. The velocity of the migration becomes larger for higher temperature difference, which is in agreement with the present theoretical analysis and DVDWT simulations. The results illustrate that the modified model can be used to study Marangoni effect on a nanodroplet and other heat and mass transfer problems with free interface.

That one can stack van der Waals materials with atomically sharp interfaces has provided a new material platform of constructing heterostructures. The technical challenge of mechanical stacking is picking up the exfoliated atomically thin materials after mechanical exfoliation without chemical and mechanical degradation. Chemically inert hexagonal boron nitride (hBN) has been widely used for encapsulating and picking up vdW materials. However, due to the relatively weak adhesion of hBN, assembling vdW heterostructures based on hBN has been limited. We report a new dry transfer technique. We used two vdW semiconductors (ZnPS3 and CrPS4) to pick up and encapsulate layers for vdW heterostructures, which otherwise are known to be hard to fabricate. By combining with optimized polycaprolactone (PCL) providing strong adhesion, we demonstrated various vertical heterostructure devices, including quasi-2D superconducting NbSe2 Josephson junctions with atomically clean interface. The versatility of the PCL-based vdW stacking method provides a new route for assembling complex 2D vdW materials without interfacial degradation.

Deep learning-based approaches for automatic document layout analysis and content extraction have the potential to unlock rich information trapped in historical documents on a large scale. One major hurdle is the lack of large datasets for training robust models. In particular, little training data exist for Asian languages. To this end, we present HJDataset, a Large Dataset of Historical Japanese Documents with Complex Layouts. It contains over 250,000 layout element annotations of seven types. In addition to bounding boxes and masks of the content regions, it also includes the hierarchical structures and reading orders for layout elements. The dataset is constructed using a combination of human and machine efforts. A semi-rule based method is developed to extract the layout elements, and the results are checked by human inspectors. The resulting large-scale dataset is used to provide baseline performance analyses for text region detection using state-of-the-art deep learning models. And we demonstrate the usefulness of the dataset on real-world document digitization tasks. The dataset is available at https://dell-research-harvard.github.io/HJDataset/., Comment: 8 pages, 8 figures, accepted at CVPR2020 Workshop on Text and Documents in the Deep Learning Era

The semiclassical electron trajectory, the so-called quantum orbits, is employed to explain the terahertz wave generation (TWG) in dual-color strong field, and the feasibility of the theory is validated by the measurement. We find that TWG stems from quantum path interference of partial electron wavepacket released at the neighbouring cycles of the dual-color electric field, manifesting the temporal Young's double-slit interference of single electron. The trajectories released from neighbouring cycles, creating TWG, also account for intercycle interference fringes in the photoelectron momentum distribution, whereas no signature of TWG is found when taking trajectories only from a single cycle.

We propose an approach that connects recurrent networks with different orders of hidden interaction with regular grammars of different levels of complexity. We argue that the correspondence between recurrent networks and formal computational models gives understanding to the analysis of the complicated behaviors of recurrent networks. We introduce an entropy value that categorizes all regular grammars into three classes with different levels of complexity, and show that several existing recurrent networks match grammars from either all or partial classes. As such, the differences between regular grammars reveal the different properties of these models. We also provide a unification of all investigated recurrent networks. Our evaluation shows that the unified recurrent network has improved performance in learning grammars, and demonstrates comparable performance on a real-world dataset with more complicated models.

Data samples collected for training machine learning models are typically assumed to be independent and identically distributed (iid). Recent research has demonstrated that this assumption can be problematic as it simplifies the manifold of structured data. This has motivated different research areas such as data poisoning, model improvement, and explanation of machine learning models. In this work, we study the influence of a sample on determining the intrinsic topological features of its underlying manifold. We propose the Shapley Homology framework, which provides a quantitative metric for the influence of a sample of the homology of a simplicial complex. By interpreting the influence as a probability measure, we further define an entropy which reflects the complexity of the data manifold. Our empirical studies show that when using the 0-dimensional homology, on neighboring graphs, samples with higher influence scores have more impact on the accuracy of neural networks for determining the graph connectivity and on several regular grammars whose higher entropy values imply more difficulty in being learned.