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You Zhou

You Zhou contributes to research discovery and scholarly infrastructure.

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cond-mat.mes-hallquant-phphysics.opticscond-mat.mtrl-sciArtificial Intelligencecond-mat.str-elMachine LearningRoboticsComputation and LanguageComputer Visionnucl-exnucl-theess.ASeess.IVphysics.app-phphysics.soc-phPopulations and EvolutionSound

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Published work

25 published item(s)

preprint2026arXiv

PersonaGesture: Single-Reference Co-Speech Gesture Personalization for Unseen Speakers

We propose PersonaGesture, a diffusion-based pipeline for single-reference co-speech gesture personalization of unseen speakers. Given target speech and one motion clip from a new speaker, the model must synthesize gestures that follow the new utterance while retaining speaker-specific pose choices, without per-speaker optimization. This setting is useful for avatars and virtual agents, but it is hard because the reference mixes stable speaker habits with utterance-specific trajectories. PersonaGesture consists of two key components, Adaptive Style Infusion (ASI) and Implicit Distribution Rectification (IDR), to separate temporal identity evidence from residual statistic correction. A Style Perceiver first encodes the variable-length reference into compact speaker-memory tokens. ASI injects these tokens into denoising through zero-initialized residual cross-attention, enabling style evidence to affect motion formation without replacing the pretrained speech-to-motion prior. Building on this, IDR applies a length-aware diagonal affine map in latent space to correct residual channel-wise moments estimated from the same reference. Across BEAT2 and ZeroEGGS, we evaluate quantitative metrics, reference-identity controls, same-audio diagnostics, qualitative comparisons, and human preference. Experiments show that separating denoising-time speaker memory from conservative post-generation moment correction improves unseen-speaker personalization over collapsed style codes, full-reference attention, and one-clip finetuning. Project: https://xiangyue-zhang.github.io/PersonaGesture.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Spike Imaging Velocimetry: Dense Motion Estimation of Fluids Using Spike Cameras

Particle Image Velocimetry (PIV) is a widely adopted non-invasive imaging technique that tracks the motion of tracer particles across image sequences to capture the velocity distribution of fluid flows. It is commonly employed to analyze complex flow structures and validate numerical simulations. This study explores the untapped potential of spike cameras--ultra-high-speed, high-dynamic-range vision sensors--in high-speed fluid velocimetry. We propose a deep learning framework, Spike Imaging Velocimetry (SIV), tailored for high-resolution fluid motion estimation. To enhance the network's performance, we design three novel modules specifically adapted to the characteristics of fluid dynamics and spike streams: the Detail-Preserving Hierarchical Transform (DPHT), the Graph Encoder (GE), and the Multi-scale Velocity Refinement (MSVR). Furthermore, we introduce a spike-based PIV dataset, Particle Scenes with Spike and Displacement (PSSD), which contains labeled samples from three representative fluid-dynamics scenarios: steady turbulence, high-speed flow, and high-dynamic-range conditions. Our proposed method outperforms existing baselines across all these scenarios, demonstrating its effectiveness.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Characterizing correlation within multipartite quantum systems via local randomized measurements

Given a quantum system on many qubits split into a few different parties, how many total correlations are there between these parties? Such a quantity, aimed to measure the deviation of the global quantum state from an uncorrelated state with the same local statistics, plays an important role in understanding multipartite correlations within complex networks of quantum states. Yet, the experimental access of this quantity remains challenging as it tends to be non-linear, and hence often requires tomography which becomes quickly intractable as dimensions of relevant quantum systems scale. Here, we introduce a much more experimentally accessible quantifier of total correlations, which can be estimated using only single-qubit measurements. It requires far fewer measurements than state tomography and obviates the need to coherently interfere multiple copies of a given state. Thus, we provide a tool for proving multipartite correlations that can be applied to near-term quantum devices.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Contextual Networks and Unsupervised Ranking of Sentences

We construct a contextual network to represent a document with syntactic and semantic relations between word-sentence pairs, based on which we devise an unsupervised algorithm called CNATAR (Contextual Network And Text Analysis Rank) to score sentences, and rank them through a bi-objective 0-1 knapsack maximization problem over topic analysis and sentence scores. We show that CNATAR outperforms the combined ranking of the three human judges provided on the SummBank dataset under both ROUGE and BLEU metrics, which in term significantly outperforms each individual judge's ranking. Moreover, CNATAR produces so far the highest ROUGE scores over DUC-02, and outperforms previous supervised algorithms on the CNN/DailyMail and NYT datasets. We also compare the performance of CNATAR and the latest supervised neural-network summarization models and compute oracle results.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Entanglement of random hypergraph states

Random quantum states and operations are of fundamental and practical interests. In this work, we investigate the entanglement properties of random hypergraph states, which generalize the notion of graph states by applying generalized controlled-phase gates on an initial reference product state. In particular, we study the two ensembles generated by random Controlled-Z(CZ) and Controlled-Controlled-Z(CCZ) gates, respectively. By applying tensor network representation and combinational counting, we analytically show that the average subsystem purity and entanglement entropy for the two ensembles feature the same volume law, but greatly differ in typicality, namely the purity fluctuation is small and universal for the CCZ ensemble while it is large for the CZ ensemble. We discuss the implications of these results for the onset of entanglement complexity and quantum chaos.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Learning to Sequence and Blend Robot Skills via Differentiable Optimization

In contrast to humans and animals who naturally execute seamless motions, learning and smoothly executing sequences of actions remains a challenge in robotics. This paper introduces a novel skill-agnostic framework that learns to sequence and blend skills based on differentiable optimization. Our approach encodes sequences of previously-defined skills as quadratic programs (QP), whose parameters determine the relative importance of skills along the task. Seamless skill sequences are then learned from demonstrations by exploiting differentiable optimization layers and a tailored loss formulated from the QP optimality conditions. Via the use of differentiable optimization, our work offers novel perspectives on multitask control. We validate our approach in a pick-and-place scenario with planar robots, a pouring experiment with a real humanoid robot, and a bimanual sweeping task with a human model.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Meta-optic Accelerators for Object Classifiers

Rapid advances in deep learning have led to paradigm shifts in a number of fields, from medical image analysis to autonomous systems. These advances, however, have resulted in digital neural networks with large computational requirements, resulting in high energy consumption and limitations in real-time decision making when computation resources are limited. Here, we demonstrate a meta-optic based neural network accelerator that can off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both spatial multiplexing and additional information channels, such as polarization, in object classification. End-to-end design is used to co-optimize the optical and digital systems resulting in a robust classifier that achieves 95% accurate classification of handwriting digits and 94% accuracy in classifying both the digit and its polarization state. This approach could enable compact, high-speed, and low-power image and information processing systems for a wide range of applications in machine-vision and artificial intelligence.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Nanoscale Thermal Imaging of VO$_2$ via Poole-Frenkel Conduction

We present a method for nanoscale thermal imaging of insulating thin films using atomic force microscopy (AFM), and we demonstrate its utility on VO$_2$. We sweep the applied voltage $V$ to a conducting AFM tip in contact mode and measure the local current $I$ through the film. By fitting the $IV$ curves to a Poole-Frenkel conduction model at low $V$, we calculate the local temperature with spatial resolution better than 50 nm using only fundamental constants and known film properties. Our thermometry technique enables local temperature measurement of \textit{any} insulating film dominated by the Poole-Frenkel conduction mechanism, and can be extended to insulators that display other conduction mechanisms.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Crossed-Time Delay Neural Network for Speaker Recognition

Time Delay Neural Network (TDNN) is a well-performing structure for DNN-based speaker recognition systems. In this paper we introduce a novel structure Crossed-Time Delay Neural Network (CTDNN) to enhance the performance of current TDNN. Inspired by the multi-filters setting of convolution layer from convolution neural network, we set multiple time delay units each with different context size at the bottom layer and construct a multilayer parallel network. The proposed CTDNN gives significant improvements over original TDNN on both speaker verification and identification tasks. It outperforms in VoxCeleb1 dataset in verification experiment with a 2.6% absolute Equal Error Rate improvement. In few shots condition CTDNN reaches 90.4% identification accuracy, which doubles the identification accuracy of original TDNN. We also compare the proposed CTDNN with another new variant of TDNN, FTDNN, which shows that our model has a 36% absolute identification accuracy improvement under few shots condition and can better handle training of a larger batch in a shorter training time, which better utilize the calculation resources. The code of the new model is released at https://github.com/chenllliang/CTDNN

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Learning to Shift Attention for Motion Generation

One challenge of motion generation using robot learning from demonstration techniques is that human demonstrations follow a distribution with multiple modes for one task query. Previous approaches fail to capture all modes or tend to average modes of the demonstrations and thus generate invalid trajectories. The other difficulty is the small number of demonstrations that cannot cover the entire working space. To overcome this problem, a motion generation model with extrapolation ability is needed. Previous works restrict task queries as local frames and learn representations in local frames. We propose a model to solve both problems. For multiple modes, we suggest to learn local latent representations of motion trajectories with a density estimation method based on real-valued non-volume preserving (RealNVP) transformations that provides a set of powerful, stably invertible, and learnable transformations. To improve the extrapolation ability, we propose to shift the attention of the robot from one local frame to another during the task execution. In experiments, we consider the docking problem used also in previous works where a trajectory has to be generated to connect two dockers without collision. We increase complexity of the task and show that the proposed method outperforms other approaches. In addition, we evaluate the approach in real robot experiments.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

VDPC: Variational Density Peak Clustering Algorithm

The widely applied density peak clustering (DPC) algorithm makes an intuitive cluster formation assumption that cluster centers are often surrounded by data points with lower local density and far away from other data points with higher local density. However, this assumption suffers from one limitation that it is often problematic when identifying clusters with lower density because they might be easily merged into other clusters with higher density. As a result, DPC may not be able to identify clusters with variational density. To address this issue, we propose a variational density peak clustering (VDPC) algorithm, which is designed to systematically and autonomously perform the clustering task on datasets with various types of density distributions. Specifically, we first propose a novel method to identify the representatives among all data points and construct initial clusters based on the identified representatives for further analysis of the clusters' property. Furthermore, we divide all data points into different levels according to their local density and propose a unified clustering framework by combining the advantages of both DPC and DBSCAN. Thus, all the identified initial clusters spreading across different density levels are systematically processed to form the final clusters. To evaluate the effectiveness of the proposed VDPC algorithm, we conduct extensive experiments using 20 datasets including eight synthetic, six real-world and six image datasets. The experimental results show that VDPC outperforms two classical algorithms (i.e., DPC and DBSCAN) and four state-of-the-art extended DPC algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Broken mirror symmetry in excitonic response of reconstructed domains in twisted MoSe$_2$/MoSe$_2$ bilayers

Structural engineering of van der Waals heterostructures via stacking and twisting has recently been used to create moiré superlattices, enabling the realization of new optical and electronic properties in solid-state systems. In particular, moiré lattices in twisted bilayers of transition metal dichalcogenides (TMDs) have been shown to lead to exciton trapping, host Mott insulating and superconducting states, and act as unique Hubbard systems whose correlated electronic states can be detected and manipulated optically. Structurally, these twisted heterostructures also feature atomic reconstruction and domain formation. Unfortunately, due to the nanoscale sizes (~10 nm) of typical moiré domains, the effects of atomic reconstruction on the electronic and excitonic properties of these heterostructures could not be investigated systematically and have often been ignored. Here, we use near-0$^o$ twist angle MoSe$_2$/MoSe$_2$ bilayers with large rhombohedral AB/BA domains to directly probe excitonic properties of individual domains with far-field optics. We show that this system features broken mirror/inversion symmetry, with the AB and BA domains supporting interlayer excitons with out-of-plane (z) electric dipole moments in opposite directions. The dipole orientation of ground-state $Γ$-K interlayer excitons (X$_{I,1}$) can be flipped with electric fields, while higher-energy K-K interlayer excitons (X$_{I,2}$) undergo field-asymmetric hybridization with intralayer K-K excitons (X$_0$). Our study reveals the profound impacts of crystal symmetry on TMD excitons and points to new avenues for realizing topologically nontrivial systems, exotic metasurfaces, collective excitonic phases, and quantum emitter arrays via domain-pattern engineering.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Constructing Multipartite Bell inequalities from stabilizers

Bell inequality with self-testing property has played an important role in quantum information field with both fundamental and practical applications. However, it is generally challenging to find Bell inequalities with self-testing property for multipartite states and actually there are not many known candidates. In this work, we propose a systematical framework to construct Bell inequalities from stabilizers which are maximally violated by general stabilizer states, with two observables for each local party. We show that the constructed Bell inequalities can self-test any stabilizer state which is essentially device-independent, if and only if these stabilizers can uniquely determine the state in a device-dependent manner. This bridges the gap between device-independent and device-dependent verification methods. Our framework can provide plenty of Bell inequalities for self-testing stabilizer states. Among them, we give two families of Bell inequalities with different advantages: (1) a family of Bell inequalities with a constant ratio of quantum and classical bounds using 2N correlations, (2) Single pair inequalities improving on all previous robustness self-testing bounds using N+1 correlations, which are both efficient and suitable for realizations in multipartite systems. Our framework can not only inspire more fruitful multipartite Bell inequalities from conventional verification methods, but also pave the way for their practical applications.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Generic algorithm for multi-particle cumulants of azimuthal correlations in high energy nucleus collisions

Multi-particle cumulants of azimuthal angle correlations have been compelling tools to probe the properties of the Quark-Gluon Plasma (QGP) created in the ultra-relativistic heavy-ion collisions and the search for the QGP in small collision systems at RHIC and the LHC. However, only very few of them are available and have been studied in theoretical calculations and experimental measurements. In this paper, we present a generic recursive algorithm for multi-particle cumulants, which enables the calculation of arbitrary order multi-particle cumulants. Among them, the new 10-, 12-, 14-, and 16-particle cumulants of a single harmonic, named $c_{n}\{10\}$, $c_{n}\{12\}$, $c_{n}\{14\}$, and $c_{n}\{16\}$, and the corresponding $v_n$ coefficients, will be discussed for the first time. These new multi-particle cumulants can be readily used along with updates to the generic framework of multi-particle correlations to a very high order. Finally, we propose a particular series of mixed harmonic multi-particle cumulants, which measures the general correlations between any moments of different flow coefficients. The predictions of these new observables are shown based on an initial state model MC-Glauber, a toy Monte Carlo model, and the HIJING transport model for future comparisons between experimental data and theoretical model calculations. The study of these new multi-particle cumulants in heavy-ion collisions will significantly improve the understanding of the joint probability density function which involves both different harmonics of flow and also the symmetry planes. This will pave the way for more stringent constraints on the initial state and help to extract more precisely how the created hot and dense matter evolves. Meanwhile, the efforts applied to small systems could be very helpful in the understanding of the origin of the observed collectivity at RHIC and the LHC.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Learning Compliance Adaptation in Contact-Rich Manipulation

Compliant robot behavior is crucial for the realization of contact-rich manipulation tasks. In such tasks, it is important to ensure a high stiffness and force tracking accuracy during normal task execution as well as rapid adaptation and complaint behavior to react to abnormal situations and changes. In this paper, we propose a novel approach for learning predictive models of force profiles required for contact-rich tasks. Such models allow detecting unexpected situations and facilitates better adaptive control. The approach combines an anomaly detection based on Bidirectional Gated Recurrent Units (Bi-GRU) and an adaptive force/impedance controller. We evaluated the approach in simulated and real world experiments on a humanoid robot.The results show that the approach allow simultaneous high tracking accuracy of desired motions and force profile as well as the adaptation to force perturbations due to physical human interaction.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

One fluid might not rule them all

In this proceeding, we present our recent investigations on hydrodynamic collectivity in high-multiplicity proton--proton collisions at $\sqrt{s}=$ 13 TeV using the VISHNU hybrid model with different initial condition models, called HIJING, super-MC and TRENTo. We find that with carefully tuned parameters, hydrodynamic simulations can give reasonable descriptions of the measured two-particle correlations. However, multi-particle single and mixed harmonics cumulants can not be described by hydrodynamics with these three initial conditions, even for the signs in a few cases. Further studies show that the non-linear response plays an important role in the hydrodynamic expansion of the p--p systems. Such an effect can change $c_2\{4\}$ from a negative value in the initial state to a positive value in the final state. The failure of the hydrodynamic description of multi-particle cumulant triggers the questions on whether the hydrodynamics can rule all collision systems, including p--p collisions at the LHC.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Quantum gate verification and its application in property testing

To guarantee the normal functioning of quantum devices in different scenarios, appropriate benchmarking tool kits are quite significant. Inspired by the recent progress on quantum state verification, here we establish a general framework of verifying a target unitary gate. In both the non-adversarial and adversarial scenarios, we provide efficient methods to evaluate the performance of verification strategies for any qudit unitary gate. Furthermore, we figure out the optimal strategy and its realization with local operations. Specifically, for the commonly-used quantum gates like single qubit and qudit gates, multi-qubit Clifford gates, and multi-qubit Controlled-Z(X) gates, we provide efficient verification protocols. Besides, we discuss the application of gate verification for the detection of entanglement-preserving property of quantum channels and further quantify the robustness measure of them. We believe that the gate verification is a promising way to benchmark a large-scale quantum circuit as well as to test its property.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Taking the pulse of COVID-19: A spatiotemporal perspective

The sudden outbreak of the Coronavirus disease (COVID-19) swept across the world in early 2020, triggering the lockdowns of several billion people across many countries, including China, Spain, India, the U.K., Italy, France, Germany, and most states of the U.S. The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S., and New York City became an epicenter of the pandemic by the end of March. In response to this national and global emergency, the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implemented strategies to rapidly respond to this crisis, for supporting research, saving lives, and protecting the health of global citizens. This perspective paper presents our collective view on the global health emergency and our effort in collecting, analyzing, and sharing relevant data on global policy and government responses, geospatial indicators of the outbreak and evolving forecasts; in developing research capabilities and mitigation measures with global scientists, promoting collaborative research on outbreak dynamics, and reflecting on the dynamic responses from human societies.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Controlling excitons in an atomically thin membrane with a mirror

We demonstrate a new approach for dynamically manipulating the optical response of an atomically thin semiconductor, a monolayer of MoSe2, by suspending it over a metallic mirror. First, we show that suspended van der Waals heterostructures incorporating a MoSe2 monolayer host spatially homogeneous, lifetime-broadened excitons. Then, we interface this nearly ideal excitonic system with a metallic mirror and demonstrate control over the exciton-photon coupling. Specifically, by electromechanically changing the distance between the heterostructure and the mirror, thereby changing the local photonic density of states in a controllable and reversible fashion, we show that both the absorption and emission properties of the excitons can be dynamically modulated. This electromechanical control over exciton dynamics in a mechanically flexible, atomically thin semiconductor opens up new avenues in cavity quantum optomechanics, nonlinear quantum optics, and topological photonics.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Electrically tunable valley dynamics in twisted WSe$_2$/WSe$_2$ bilayers

The twist degree of freedom provides a powerful new tool for engineering the electrical and optical properties of van der Waals heterostructures. Here, we show that the twist angle can be used to control the spin-valley properties of transition metal dichalcogenide bilayers by changing the momentum alignment of the valleys in the two layers. Specifically, we observe that the interlayer excitons in twisted WSe$_2$/WSe$_2$ bilayers exhibit a high (>60%) degree of circular polarization (DOCP) and long valley lifetimes (>40 ns) at zero electric and magnetic fields. The valley lifetime can be tuned by more than three orders of magnitude via electrostatic doping, enabling switching of the DOCP from ~80% in the n-doped regime to <5% in the p-doped regime. These results open up new avenues for tunable chiral light-matter interactions, enabling novel device schemes that exploit the valley degree of freedom.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Entanglement detection under coherent noise: Greenberger-Horne-Zeilinger-like states

Entanglement is an essential resource in many quantum information tasks and entanglement witness is a widely used tool for its detection. In experiments the prepared state generally deviates from the target state due to some noise. Normally the white noise model is applied to quantifying such derivation and in the same time reveals the robustness of the witness. However, there may exist other kind of noise, in which the coherent noise can dramatically &#34;rotate&#34; the prepared state. In this way, the coherent noise is likely to lead to a failure of the detection, even though the underlying state is actually entangled. In this work, we propose an efficient entanglement detection protocol for $N$-partite Greenberger-Horne-Zeilinger (GHZ)-like states. The protocol can eliminate the effect of the coherent noise and in the same time feedback the corresponding noise parameters, which are beneficial to further improvements on the experiment system. In particular, we consider two experiment-relevant coherent noise models, one is from the unconscious phase accumulation on $N$ qubits, the other is from the rotation on the control qubit. The protocol effectively realizes a family of entanglement witnesses by postprocessing the measurement results from $N+2$ local measurement settings, which only adds one more setting than the original witness specialized for the GHZ state. Moreover, by considering the trade-off between the detection efficiency and the white-noise robustness, we further reduce the number of local measurements to 3 without altering the performance on the coherent noise. Our protocol can enhance the entanglement detection under coherent noises and act as a benchmark for the state-of-the-art quantum devices.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Moiré Excitons Correlated with Superlattice Structure in Twisted WSe$_2$/WSe$_2$ Homobilayers

Moiré superlattices in twisted van der Waals materials constitute a promising platform for engineering electronic and optical properties. However, a major obstacle to fully understanding these systems and harnessing their potential is the limited ability to correlate the local moiré structure with optical properties. By using a recently developed scanning electron microscopy technique to image twisted WSe$_2$/WSe$_2$ bilayers, we directly correlate increasing moiré periodicity with the emergence of two distinct exciton species. These can be tuned individually through electrostatic gating, and feature different valley coherence properties. Our observations can be understood as resulting from an array of two intralayer exciton species residing in alternating locations in the superlattice, and illuminate the influence of the moiré potential on lateral exciton motion. They open up new avenues for controlling exciton arrays in twisted TMDs, with applications in quantum optoelectronics and explorations of novel many body systems.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Temperature-independent thermal radiation

Thermal emission is the process by which all objects at non-zero temperatures emit light, and is well-described by the classic Planck, Kirchhoff, and Stefan-Boltzmann laws. For most solids, the thermally emitted power increases monotonically with temperature in a one-to-one relationship that enables applications such as infrared imaging and non-contact thermometry. Here, we demonstrate ultrathin thermal emitters that violate this one-to-one relationship via the use of samarium nickel oxide (SmNiO3), a strongly correlated quantum material that undergoes a fully reversible, temperature-driven solid-state phase transition. The smooth and hysteresis-free nature of this unique insulator-to-metal (IMT) phase transition allows us to engineer the temperature dependence of emissivity to precisely cancel out the intrinsic blackbody profile described by the Stefan-Boltzmann law, for both heating and cooling. Our design results in temperature-independent thermally emitted power within the long-wave atmospheric transparency window (wavelengths of 8 - 14 um), across a broad temperature range of ~30 °C, centered around ~120 °C. The ability to decouple temperature and thermal emission opens a new gateway for controlling the visibility of objects to infrared cameras and, more broadly, new opportunities for quantum materials in controlling heat transfer.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

Electrical control of interlayer exciton dynamics in atomically thin heterostructures

Excitons in semiconductors, bound pairs of excited electrons and holes, can form the basis for new classes of quantum optoelectronic devices. A van der Waals heterostructure built from atomically thin semiconducting transition metal dichalcogenides (TMDs) enables the formation of excitons from electrons and holes in distinct layers, producing interlayer excitons with large binding energy and a long lifetime. Employing heterostructures of monolayer TMDs, we realize optical and electrical generation of long-lived neutral and charged interlayer excitons. We demonstrate the transport of neutral interlayer excitons across the whole sample that can be controlled by excitation power and gate electrodes. We also realize the drift motion of charged interlayer excitons using Ohmic-contacted devices. The electrical generation and control of excitons provides a new route for realizing quantum manipulation of bosonic composite particles with complete electrical tunability.

0 citations0 reviewsNo code linkedOpen access