arXiv subjects

Jiahan Li

Publications and source records attributed to Jiahan Li.

30 records · Page 2Linked to original sources

arXiv2024

Unconventional Unidirectional Magnetoresistance in vdW Heterostructures

Electrical readout of magnetic states is a key to realize novel spintronics devices for efficient computing and data storage. Unidirectional magnetoresistance (UMR) in bilayer systems, consisting of a spin source material and a magnetic layer, refers to a change in the longitudinal resistance upon the reversal of magnetization, which typically originates from the interaction of spin-current and magnetization at the interface. Because of UMR s linear dependence on applied charge current and magnetization, it can be used to electrically read the magnetization state. However, in conventional spin source materials, the spin polarization of an electric field induced spin current is restricted to be in the film plane and hence the ensuing UMR can only respond to the in plane component of the magnetization. On the other hand, magnets with perpendicular magnetic anisotropy (PMA) are highly desired for magnetic memory and spin-logic devices, while the electrical read out of PMA magnets through UMR is critically missing. Here, we report the discovery of an unconventional UMR in bilayer heterostructures of a topological semimetal (WTe2) and a PMA ferromagnetic insulator (Cr2Ge2Te6, CGT), which allows to electrically read the up and down magnetic states of the CGT layer by measuring the longitudinal resistance. Our theoretical calculations based on a tight binding model show that the unconventional UMR originates from the interplay of crystal symmetry breaking in WTe2 and magnetic exchange interaction across the WTe2 and CGT interface. Combining with the ability of WTe2 to obtain magnetic field free switching of the PMA magnets, our discoveries open an exciting pathway to achieve two terminal magnetic memory devices that operate solely on the spin orbit torque and UMR, which is critical for developing next-generation non volatile and low power consumption data storage technologies.

cond-mat.mes-hall↗

arXiv2024

Ultrafast evanescent heat transfer across solid interfaces via hyperbolic phonon polaritons in hexagonal boron nitride

The efficiency of phonon-mediated heat transport is limited by the intrinsic atomistic properties of materials, seemingly providing an upper limit to heat transfer in materials and across their interfaces. The typical speeds of conductive transport, which are inherently limited by the chemical bonds and atomic masses, dictate how quickly heat will move in solids. Given that phonon-polaritons, or coupled phonon-photon modes, can propagate at speeds approaching 1 percent of the speed of light - orders of magnitude faster than transport within a pure diffusive phonon conductor - we demonstrate that volume-confined, hyperbolic phonon-polariton(HPhP) modes supported by many biaxial polar crystals can couple energy across solid-solid interfaces at an order of magnitude higher rates than phonon-phonon conduction alone. Using pump-probe thermoreflectance with a mid-infrared, tunable, probe pulse with sub-picosecond resolution, we demonstrate remote and spectrally selective excitation of the HPhP modes in hexagonal boron nitride in response to radiative heating from a thermally emitting gold source. Our work demonstrates a new avenue for interfacial heat transfer based on broadband radiative coupling from a hot spot in a gold film to hBN HPhPs, independent of the broad spectral mismatch between the pump(visible) and probe(mid-IR) pulses employed. This methodology can be used to bypass the intrinsically limiting phonon-phonon conductive pathway, thus providing an alternative means of heat transfer across interfaces. Further, our time-resolved measurements of the temperature changes of the HPhP modes in hBN show that through polaritonic coupling, a material can transfer heat across and away from an interface at rates orders of magnitude faster than diffusive phonon speeds intrinsic to the material, thus demonstrating a pronounced thermal transport enhancement in hBN via phonon-polariton coupling.

cond-mat.mes-hall↗

arXiv2023

Chemically detaching hBN crystals grown at atmospheric pressure and high temperature for high-performance graphene devices

In this work, we report on the growth of hexagonal boron nitride (hBN) crystals from an iron flux at atmospheric pressure and high temperature and demonstrate that (i) the entire sheet of hBN crystals can be detached from the metal in a single step using hydrochloric acid and that (ii) these hBN crystals allow the fabrication of high carrier mobility graphene devices. By combining spatially-resolved confocal Raman spectroscopy and electrical transport measurements, we confirm the excellent quality of these crystals for high-performance hBN-graphene-based van der Waals heterostructures. The full width at half maximum of the graphene Raman 2D peak is as low as 16 cm$^{-1}$, and the room temperature charge carrier mobilitiy is around 80000 cm$^2$/(Vs) at a carrier density 1$\times$10$^{12}$cm$^{-12}$. This is fully comparable with devices of similar dimensions fabricated using crystalline hBN synthesized by the high pressure and high temperature method. Finally, we show that for exfoliated high-quality hBN flakes with a thickness between 20 nm and 40 nm the line width of the hBN Raman peak, in contrast to the graphene 2D line width, is not useful for benchmarking hBN in high mobility graphene devices.

cond-mat.mes-hall↗

arXiv2023

Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning

Unsupervised domain adaptation person re-identification (Re-ID) aims to identify pedestrian images within an unlabeled target domain with an auxiliary labeled source-domain dataset. Many existing works attempt to recover reliable identity information by considering multiple homogeneous networks. And take these generated labels to train the model in the target domain. However, these homogeneous networks identify people in approximate subspaces and equally exchange their knowledge with others or their mean net to improve their ability, inevitably limiting the scope of available knowledge and putting them into the same mistake. This paper proposes a Dual-level Asymmetric Mutual Learning method (DAML) to learn discriminative representations from a broader knowledge scope with diverse embedding spaces. Specifically, two heterogeneous networks mutually learn knowledge from asymmetric subspaces through the pseudo label generation in a hard distillation manner. The knowledge transfer between two networks is based on an asymmetric mutual learning manner. The teacher network learns to identify both the target and source domain while adapting to the target domain distribution based on the knowledge of the student. Meanwhile, the student network is trained on the target dataset and employs the ground-truth label through the knowledge of the teacher. Extensive experiments in Market-1501, CUHK-SYSU, and MSMT17 public datasets verified the superiority of DAML over state-of-the-arts.

cs.CV↗

arXiv2022

Equivariant Point Cloud Analysis via Learning Orientations for Message Passing

Equivariance has been a long-standing concern in various fields ranging from computer vision to physical modeling. Most previous methods struggle with generality, simplicity, and expressiveness -- some are designed ad hoc for specific data types, some are too complex to be accessible, and some sacrifice flexible transformations. In this work, we propose a novel and simple framework to achieve equivariance for point cloud analysis based on the message passing (graph neural network) scheme. We find the equivariant property could be obtained by introducing an orientation for each point to decouple the relative position for each point from the global pose of the entire point cloud. Therefore, we extend current message passing networks with a module that learns orientations for each point. Before aggregating information from the neighbors of a point, the networks transforms the neighbors' coordinates based on the point's learned orientations. We provide formal proofs to show the equivariance of the proposed framework. Empirically, we demonstrate that our proposed method is competitive on both point cloud analysis and physical modeling tasks. Code is available at https://github.com/luost26/Equivariant-OrientedMP .

cs.CV↗

arXiv2021

Revealing Nanoscale Confinement Effects on Hyperbolic Phonon Polaritons with an Electron Beam

Hyperbolic phonon polaritons (HPhPs) in hexagonal boron nitride (hBN) enable the direct manipulation of mid-infrared light at nanometer scales, many orders of magnitude below the free-space light wavelength. High resolution monochromated electron energy-loss spectroscopy (EELS) facilitates measurement of excitations with energies extending into the mid-infrared while maintaining nanoscale spatial resolution, making it ideal for detecting HPhPs. The electron beam is a precise source and probe of HPhPs, that allows us to perform novel experiments to observe nanoscale confinement in HPhP structures and directly extract hBN polariton dispersions for both modes in the bulk of the flake and modes along the edge. Our measurements reveal technologically important non-trivial phenomena, such as localized polaritons induced by environmental heterogeneity, enhanced and suppressed excitation due to two-dimensional interference, and strong modification of high-momenta excitations of edge-confined polaritons by nanoscale heterogeneity on edge boundaries. Our work opens exciting prospects for the design of real-world optical mid-infrared devices based on hyperbolic polaritons.

physics.optics↗

arXiv2020

Field-free deterministic switching of a perpendicularly polarized magnet using unconventional spin-orbit torques in WTe2

Spin-orbit torque (SOT) driven deterministic control of the magnetization state of a magnet with perpendicular magnetic anisotropy (PMA) is key to next generation spintronic applications including non-volatile, ultrafast, and energy efficient data storage devices. But, field-free deterministic switching of perpendicular magnetization remains a challenge because it requires an out-of-plane anti-damping torque, which is not allowed in conventional spin source materials such as heavy metals (HM) and topological insulators due to the system's symmetry. The exploitation of low-crystal symmetries in emergent quantum materials offers a unique approach to achieve SOTs with unconventional forms. Here, we report the first experimental realization of field-free deterministic magnetic switching of a perpendicularly polarized van der Waals (vdW) magnet employing an out-of-plane anti-damping SOT generated in layered WTe2 which is a low-crystal symmetry quantum material. The numerical simulations confirm that out-of-plane antidamping torque in WTe2 is responsible for the observed magnetization switching in the perpendicular direction.

cond-mat.mes-hall↗

arXiv2020

High quality factor polariton resonators using van der Waals materials

We present high quality factor optical nanoresonators operating in the mid-IR to far-IR based on phonon polaritons in van der Waals materials. The nanoresonators are disks patterned from isotopically pure hexagonal boron nitride (isotopes 10B and 11B) and α-molybdenum trioxide. We experimentally achieved quality factors of nearly 400, the highest ever observed in nano-resonators at these wavelengths. The excited modes are deeply subwavelength, and the resonators are 10 to 30 times smaller than the exciting wavelength. These results are very promising for the realization of nano-photonics devices such as optical bio-sensors and miniature optical components such as polarizers and filters.

physics.optics↗

arXiv2019

Three-dimensional Near-field Analysis Through Peak Force Scattering-type Near-field Optical Microscopy

Scattering-type scanning near-field optical microscopy (s-SNOM) is instrumental in exploring polaritonic behaviors of two-dimensional (2D) materials at the nanoscale. A sharp s-SNOM tip couples momenta into 2D materials through phase matching to excite phonon polaritons, which manifest as nanoscale interference fringes in raster images. However, s-SNOM lacks the ability to detect the progression of near-field property along the perpendicular axis to the surface. Here, we perform near-field analysis of a micro-disk and a reflective edge made of isotopically pure hexagonal boron nitride (h-11BN), by using three-dimensional near-field response cubes obtained by peak force scattering-type near-field optical microscopy (PF-SNOM). Momentum quantization of polaritons from the confinement of the circular structure is revealed in situ. Moreover, tip-sample distance is found to be capable of fine-tuning the momentum of polaritons and modifying the superposition of quantized polaritonic modes. The PF-SNOM-based three-dimensional near-field analysis provides detailed characterization capability with a high spatial resolution to fully map three-dimensional near-fields of nano-photonics and polaritonic structures.

physics.optics↗

arXiv2019

Polariton Nanophotonics using Phase Change Materials

Polaritons formed by the coupling of light and material excitations such as plasmons, phonons, or excitons enable light-matter interactions at the nanoscale beyond what is currently possible with conventional optics. Recently, significant interest has been attracted by polaritons in van der Waals materials, which could lead to applications in sensing, integrated photonic circuits and detectors. However, novel techniques are required to control the propagation of polaritons at the nanoscale and to implement the first practical devices. Here we report the experimental realization of polariton refractive and meta-optics in the mid-infrared by exploiting the properties of low-loss phonon polaritons in isotopically pure hexagonal boron nitride (hBN), which allow it to interact with the surrounding dielectric environment comprising the low-loss phase change material, Ge$_3$Sb$_2$Te$_6$ (GST). We demonstrate waveguides which confine polaritons in a 1D geometry, and refractive optical elements such as lenses and prisms for phonon polaritons in hBN, which we characterize using scanning near field optical microscopy. Furthermore, we demonstrate metalenses, which allow for polariton wavefront engineering and sub-wavelength focusing. Our method, due to its sub-diffraction and planar nature, will enable the realization of programmable miniaturized integrated optoelectronic devices, and will lay the foundation for on-demand biosensors.

physics.app-ph↗

arXiv2015

Bayesian group Lasso for nonparametric varying-coefficient models with application to functional genome-wide association studies

Although genome-wide association studies (GWAS) have proven powerful for comprehending the genetic architecture of complex traits, they are challenged by a high dimension of single-nucleotide polymorphisms (SNPs) as predictors, the presence of complex environmental factors, and longitudinal or functional natures of many complex traits or diseases. To address these challenges, we propose a high-dimensional varying-coefficient model for incorporating functional aspects of phenotypic traits into GWAS to formulate a so-called functional GWAS or fGWAS. The Bayesian group lasso and the associated MCMC algorithms are developed to identify significant SNPs and estimate how they affect longitudinal traits through time-varying genetic actions. The model is generalized to analyze the genetic control of complex traits using subject-specific sparse longitudinal data. The statistical properties of the new model are investigated through simulation studies. We use the new model to analyze a real GWAS data set from the Framingham Heart Study, leading to the identification of several significant SNPs associated with age-specific changes of body mass index. The fGWAS model, equipped with the Bayesian group lasso, will provide a useful tool for genetic and developmental analysis of complex traits or diseases.

stat.AP↗

arXiv2015

A fast algorithm for detecting gene-gene interactions in genome-wide association studies

With the recent advent of high-throughput genotyping techniques, genetic data for genome-wide association studies (GWAS) have become increasingly available, which entails the development of efficient and effective statistical approaches. Although many such approaches have been developed and used to identify single-nucleotide polymorphisms (SNPs) that are associated with complex traits or diseases, few are able to detect gene-gene interactions among different SNPs. Genetic interactions, also known as epistasis, have been recognized to play a pivotal role in contributing to the genetic variation of phenotypic traits. However, because of an extremely large number of SNP-SNP combinations in GWAS, the model dimensionality can quickly become so overwhelming that no prevailing variable selection methods are capable of handling this problem. In this paper, we present a statistical framework for characterizing main genetic effects and epistatic interactions in a GWAS study. Specifically, we first propose a two-stage sure independence screening (TS-SIS) procedure and generate a pool of candidate SNPs and interactions, which serve as predictors to explain and predict the phenotypes of a complex trait. We also propose a rates adjusted thresholding estimation (RATE) approach to determine the size of the reduced model selected by an independence screening. Regularization regression methods, such as LASSO or SCAD, are then applied to further identify important genetic effects. Simulation studies show that the TS-SIS procedure is computationally efficient and has an outstanding finite sample performance in selecting potential SNPs as well as gene-gene interactions. We apply the proposed framework to analyze an ultrahigh-dimensional GWAS data set from the Framingham Heart Study, and select 23 active SNPs and 24 active epistatic interactions for the body mass index variation. It shows the capability of our procedure to resolve the complexity of genetic control.

stat.AP↗