arXiv subjects

Weisheng Zhao

Publications and source records attributed to Weisheng Zhao.

At least 91 records · Page 5Linked to original sources

arXiv2022

Impact of Spin-Orbit Coupling on Quantum Transport in Magnetic Tunnel Junction with an anti-ferromagnetic Capping Layer

Using first-principles calculations, we explore the role of an anti-ferromagnetic heavy-metal, L1$_0$-IrMn, as a capping layer in a perpendicular magnetic tunnel junction (\emph{p}-MTJ). A comparative study is conducted by employing conventional non-magnetic heavy-metals (Ta, W or Mo) capping layers along with an anti-ferromagnetic IrMn in a symmetric-MTJ X/FeCo/MgO/FeCo/X, where X=Ta, W, Mo or IrMn. Firstly, the calculations without including spin-orbit coupling (SOC) are presented where the highest TMR is achieved in IrMn-IrMn MTJ compared to that of Ta-Ta, W-W and Mo-Mo MTJs. The origin of this large TMR is attributed to, both, the large spin-polarization due to reduced lattice-mismatch and the non-identical signatures of both the anti-parallel conduction-channels caused by the anti-ferromagnetic ordering of IrMn that reflects the spins at IrMn/FeCo interface. Moreover, when SOC is switched-on, the increase of TMR is observed in all the MTJs with a particularly giant enhancement in IrMn-IrMn MTJ. This SOC-induced increase in TMR is ascribed to the mixed contribution of $Δ_1$ and $Δ_5$ states and the additional increase in the band levels of the out-of-plane orbitals, \emph{p}$_z$, \emph{d}$_{z}^2$ and \emph{d}$_{xz}$ due to the lifting of degeneracy. Furthermore, it was also observed that the lattice-mismatch-induced strain might create an orbital reconstruction within the capping layer, probably, due to the crystallographic deformation and, in turn, in the adjacent FeCo-electrode. This microscopic mechanism also plays an additional decisive role in enhancing the TMR. Finally, our results indicate that IrMn can offer giant TMR in future spin-orbit toque (SOT) based MRAM devices with a straightforward design strategy.

cond-mat.mes-hall↗

arXiv2022

Time Division Multiplexing Ising Computer Using Single Tunable True Random Number Generator Based on Spin Torque Nano-Oscillator

Ising computer is a powerful computation scheme to deal with NP-hard optimization problems that cannot be efficiently addressed by conventional computers. A robust probabilistic bit (P-Bit) which is realized by a hardware entity fluctuating in time between -1 and 1 plays a key role in the success of Ising computer. Spintronics technology, such as stochastic nanomagnet, is recently proposed as a good platform for the hardware emulation of P-Bit. Here, we report, for the first time, a Time Division Multiplexing (TDM) Ising computer using single tunable true random number generator which is comprised of a Spin Torque Nano-Oscillator (STNO). First, the intrinsic frequency fluctuation of the STNO is utilized to design a simple digital true random number generator (TRNG). The true random number generator is further evolved into a tunable random number generator to act as a P-Bit. Second, in order to accomplish combinational optimization with our proposed P-Bit under Ising model, a novel incremental coupling rule is proposed. With such coupling rule between P-Bit array, high fidelity NOT and XOR logic gate is demonstrated. Third, it is proposed that our digital TRNG can be simply reused acting as a P-Bit array by time division multiplexing. The whole Ising computer can be implemented by one single STNO, and integer factorization of as high as 87% accuracy rate is achieved.

cond-mat.mes-hall↗

arXiv2021

Triangle Counting Accelerations: From Algorithm to In-Memory Computing Architecture

Triangles are the basic substructure of networks and triangle counting (TC) has been a fundamental graph computing problem in numerous fields such as social network analysis. Nevertheless, like other graph computing problems, due to the high memory-computation ratio and random memory access pattern, TC involves a large amount of data transfers thus suffers from the bandwidth bottleneck in the traditional Von-Neumann architecture. To overcome this challenge, in this paper, we propose to accelerate TC with the emerging processing-in-memory (PIM) architecture through an algorithm-architecture co-optimization manner. To enable the efficient in-memory implementations, we come up to reformulate TC with bitwise logic operations (such as AND), and develop customized graph compression and mapping techniques for efficient data flow management. With the emerging computational Spin-Transfer Torque Magnetic RAM (STT-MRAM) array, which is one of the most promising PIM enabling techniques, the device-to-architecture co-simulation results demonstrate that the proposed TC in-memory accelerator outperforms the state-of-the-art GPU and FPGA accelerations by 12.2x and 31.8x, respectively, and achieves a 34x energy efficiency improvement over the FPGA accelerator.

cs.AR↗

arXiv2021

Room temperature energy-efficient spin-orbit torque switching in wafer-scale all-vdW heterostructure

The emergent two-dimensional (2D) ferromagnetic materials with unique magnetic properties have endowed great potential for next-generation spintronic devices with extraordinary merits of high flexibility, easy controllability, and high heretointegrability, which is expected to promote the development of Moore's Law continuously. However, it is extremely challenging to realize magnetic switching with ultra-low power consumption at room temperature. Here, we demonstrate the room-temperature spin-orbit torque (SOT) driven magnetization switching in a well-epitaxial all-van der Waals (vdW) heterostructure. The topological insulator Bi2Te3 not only helps to elevate the Curie temperature of Fe3GeTe2 (FGT) through interfacial exchange coupling but also works as a spin current source allowing to switch FGT at a low current density of 2.2 * 106 A cm2. A large SOT efficiency of 0.7 is measured at room temperature, and the thickness of FGT is further adjusted to reduce the influence of the thermal contribution on the second-harmonic signal. Furthermore, the temperature and thickness-dependent SOT efficiency prove that the large SOT in our system mainly originates from the nontrivial origin of topological materials. Our experiment has enabled an all-vdW SOT structure and lays a solid foundation for the implementation of room-temperature all-vdW spintronic devices in the future.

physics.app-ph↗

arXiv2021

FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients Update

Federated learning aims to protect users' privacy while performing data analysis from different participants. However, it is challenging to guarantee the training efficiency on heterogeneous systems due to the various computational capabilities and communication bottlenecks. In this work, we propose FedSkel to enable computation-efficient and communication-efficient federated learning on edge devices by only updating the model's essential parts, named skeleton networks. FedSkel is evaluated on real edge devices with imbalanced datasets. Experimental results show that it could achieve up to 5.52$\times$ speedups for CONV layers' back-propagation, 1.82$\times$ speedups for the whole training process, and reduce 64.8% communication cost, with negligible accuracy loss.

cs.LG↗

arXiv2021

Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations

Deep learning has an increasing impact to assist research, allowing, for example, the discovery of novel materials. Until now, however, these artificial intelligence techniques have fallen short of discovering the full differential equation of an experimental physical system. Here we show that a dynamical neural network, trained on a minimal amount of data, can predict the behavior of spintronic devices with high accuracy and an extremely efficient simulation time, compared to the micromagnetic simulations that are usually employed to model them. For this purpose, we re-frame the formalism of Neural Ordinary Differential Equations (ODEs) to the constraints of spintronics: few measured outputs, multiple inputs and internal parameters. We demonstrate with Spin-Neural ODEs an acceleration factor over 200 compared to micromagnetic simulations for a complex problem -- the simulation of a reservoir computer made of magnetic skyrmions (20 minutes compared to three days). In a second realization, we show that we can predict the noisy response of experimental spintronic nano-oscillators to varying inputs after training Spin-Neural ODEs on five milliseconds of their measured response to different excitations. Spin-Neural ODE is a disruptive tool for developing spintronic applications in complement to micromagnetic simulations, which are time-consuming and cannot fit experiments when noise or imperfections are present. Spin-Neural ODE can also be generalized to other electronic devices involving dynamics.

cs.LG↗

arXiv2021

Implementing Multiple Modes of the Perpendicular Magnetization Switching within a Single Spin Orbit Torque Device

Spin orbit torque (SOT) has been considered as one of the promising technologies for the next-generation magnetic random access memory (MRAM). So far, SOT has been widely utilized for inducing various modes of magnetization switching. However, it is challenging to integrate multiple modes of magnetization switching together. In this work we propose a method for implementing both unipolar and bipolar switching of the perpendicular magnetization within a single SOT device. The mode of switching could be easily altered by tuning the amplitude of the applied current. We show that the field-like torque plays an important role in the switching process. The field-like torque induces the precession of the magnetization in the case of unipolar switching, whereas it helps to generate an effective z-component torque in the case of bipolar switching. In addition, the influence of key parameters on the mode of switching is discussed. Our proposal could be used to design novel reconfigurable logic circuits in the near future.

physics.app-ph↗

arXiv2021

S2Engine: A Novel Systolic Architecture for Sparse Convolutional Neural Networks

Convolutional neural networks (CNNs) have achieved great success in performing cognitive tasks. However, execution of CNNs requires a large amount of computing resources and generates heavy memory traffic, which imposes a severe challenge on computing system design. Through optimizing parallel executions and data reuse in convolution, systolic architecture demonstrates great advantages in accelerating CNN computations. However, regular internal data transmission path in traditional systolic architecture prevents the systolic architecture from completely leveraging the benefits introduced by neural network sparsity. Deployment of fine-grained sparsity on the existing systolic architectures is greatly hindered by the incurred computational overheads. In this work, we propose S2Engine $-$ a novel systolic architecture that can fully exploit the sparsity in CNNs with maximized data reuse. S2Engine transmits compressed data internally and allows each processing element to dynamically select an aligned data from the compressed dataflow in convolution. Compared to the naive systolic array, S2Engine achieves about $3.2\times$ and about $3.0\times$ improvements on speed and energy efficiency, respectively.

cs.AR↗

arXiv2021

Optimizing Memory Efficiency of Graph Neural Networks on Edge Computing Platforms

Graph neural networks (GNN) have achieved state-of-the-art performance on various industrial tasks. However, the poor efficiency of GNN inference and frequent Out-Of-Memory (OOM) problem limit the successful application of GNN on edge computing platforms. To tackle these problems, a feature decomposition approach is proposed for memory efficiency optimization of GNN inference. The proposed approach could achieve outstanding optimization on various GNN models, covering a wide range of datasets, which speeds up the inference by up to 3x. Furthermore, the proposed feature decomposition could significantly reduce the peak memory usage (up to 5x in memory efficiency improvement) and mitigate OOM problems during GNN inference.

cs.LG↗

arXiv2021

Field-free spin-orbit torque-induced switching of perpendicular magnetization in a ferrimagnetic layer with vertical composition gradient

Current-induced spin-orbit torques (SOTs) are of interest for fast and energy-efficient manipulation of magnetic order in spintronic devices. To be deterministic, however, switching of perpendicularly magnetized materials by SOT requires a mechanism for in-plane symmetry breaking. Existing methods to do so involve the application of an in-plane bias magnetic field, or incorporation of in-plane structural asymmetry in the device, both of which can be difficult to implement in practical applications. Here, we reported bias-field-free SOT switching in a single perpendicular CoTb layer with an engineered vertical composition gradient. The vertical structural inversion asymmetry induces strong intrinsic SOTs and a gradient-driven Dzyaloshinskii-Moriya interaction (g-DMI), which breaks the in-plane symmetry during the switching process. Micromagnetic simulations are in agreement with experimental results, and elucidate the role of g-DMI in the deterministic switching. This bias-field-free switching scheme for perpendicular ferrimagnets with g-DMI provides a strategy for efficient and compact SOT device design.

cond-mat.mes-hall↗

arXiv2020

All-optical helicity-independent switching state diagram in GdFeCo alloys

Ultra-fast magnetization switching induced by a single femtosecond laser pulse, under no applied magnetic field has attracted a lot of attention during the last 10 years because of its high potential for low energy and ultra-fast memory applications. Single-pulse helicity-independent switching has mostly been demonstrated for Gd based materials. It is now important to optimize the pulse duration and the energy needed to switch a GdFeCo magnet depending on the alloy thickness, concentration. Here we experimentally report state diagrams showing the magnetic state obtained after one single pulse depending on the laser pulse duration and fluence for various GdFeCo thin films with different compositions and thicknesses. We demonstrate that these state diagrams share similar characteristics: the fluence window for switching narrows for longer pulse duration and for the considered pulse duration range the critical fluence for single pulse switching increases linearly as a function of the pulse duration while the critical fluence required for creating a multidomain state remains almost constant. Calculations based on the atomistic spin model qualitatively reproduce the experimental state diagrams and their evolution. By studying the effect of the composition and the thickness on the state diagram, we demonstrated that the best energy efficiency and the longest pulse duration for switching are obtained for concentration around the magnetic compensation.

physics.optics↗

arXiv2020

Observation of Magnetic Droplets in Magnetic Tunnel Junctions

Magnetic droplets, a class of highly non-linear magnetodynamical solitons, can be nucleated and stabilized in nanocontact spin-torque nano-oscillators where they greatly increase the microwave output power. Here, we experimentally demonstrate magnetic droplets in magnetic tunnel junctions (MTJs). The droplet nucleation is accompanied by a power increase of over 300 times compared to its ferromagnetic resonance modes. The nucleation and stabilization of droplets are ascribed to the double-CoFeB free layer structure in the all-perpendicular MTJ which provides a low Zhang-Li torque and a high pinning field. Our results enable better electrical sensitivity in the fundamental studies of droplets and show that the droplets can be utilized in MTJ-based applications.

physics.app-ph↗

arXiv2020

Magnetic Skyrmions for Unconventional Computing

Improvements in computing performance have significantly slowed down over the past few years owing to the intrinsic limitations of computing hardware. However, the demand for data computing has increased exponentially. To solve this problem, tremendous attention has been focused on the continuous scaling of Moore's Law as well as the advanced non-von Neumann computing architecture. A rich variety of unconventional computing paradigms has been raised with the rapid development of nanoscale devices. Magnetic skyrmions, spin swirling quasiparticles, have been endowed with great expectations for unconventional computing due to their potential as the smallest information carriers by exploiting their physics and dynamics. In this paper, we provide an overview of the recent progress of skyrmion-based unconventional computing from a joint device-application perspective. This paper aims to build up a panoramic picture, analyze the remaining challenges, and most importantly to shed light on the outlook of skyrmion based unconventional computing for interdisciplinary researchers.

cond-mat.mes-hall↗

arXiv2020

Spin structure relation to phase contrast imaging of isolated magnetic Bloch and Neel skyrmions

Magnetic skyrmions are promising candidates for future storage devices with a large data density. A great variety of materials have been found that host skyrmions up to the room-temperature regime. Lorentz microscopy, usually performed in a transmission electron microscope (TEM), is one of the most important tools for characterizing skyrmion samples in real space. Using numerical calculations, this work relates the phase contrast in a TEM to the actual magnetization profile of an isolated Neel or Bloch skyrmion, the two most common skyrmion types. Within the framework of the used skyrmion model, the results are independent of skyrmion size and wall width and scale with sample thickness for purely magnetic specimens. Simple rules are provided to extract the actual skyrmion configuration of pure Bloch or Neel skyrmions without the need of simulations. Furthermore, first differential phase contrast (DPC) measurements on Neel skyrmions that meet experimental expectations are presented and showcase the described principles. The work is relevant for material sciences where it enables the engineering of skyrmion profiles via convenient characterization.

cond-mat.mes-hall↗

arXiv2020

Picosecond Switching of Optomagnetic Tunnel Junctions

Perpendicular magnetic tunnel junctions are one of the building blocks for spintronic memories, which allow fast nonvolatile data access, offering substantial potentials to revolutionize the mainstream computing architecture. However, conventional switching mechanisms of such devices are fundamentally hindered by spin polarized currents4, either spin transfer torque or spin orbit torque with spin precession time limitation and excessive power dissipation. These physical constraints significantly stimulate the advancement of modern spintronics. Here, we report an optomagnetic tunnel junction using a spintronic-photonic combination. This composite device incorporates an all-optically switchable Co/Gd bilayer coupled to a CoFeB/MgO-based perpendicular magnetic tunnel junction by the Ruderman-Kittel-Kasuya-Yosida interaction. A picosecond all-optical operation of the optomagnetic tunnel junction is explicitly confirmed by time-resolved measurements. Moreover, the device shows a considerable tunnel magnetoresistance and thermal stability. This proof-of-concept device represents an essential step towards ultrafast spintronic memories with THz data access, as well as ultralow power consumption.

cond-mat.mes-hall↗

arXiv2020

Theoretical Conditions for Field-free Magnetization Switching Induced by Spin-orbit Torque and Dzyaloshinskii-Moriya Interaction

Recently, it was demonstrated that field-free switching could be achieved by combining spin-orbit torque (SOT) and Dzyaloshinskii-Moriya interaction (DMI). However, this mechanism only occurs under certain conditions which have not been well discussed. In this letter, it is found that the ratio of domain wall width to diameter of nanodots could be utilized as a criteria for judging this mechanism. Influences of different magnetic parameters are studied, including exchange constant, DMI magnitude and field-like toque, etc. Besides, we reveal the importance of the shrinkage of magnetic domain wall surface energy for metastable states. Our work provides guidelines to the experiments on the DMI-induced field-free magnetization switching, and also offers a new way for the design of SOT-based memory or logic circuits.

physics.app-ph↗

arXiv2020

Generation and manipulation of chiral terahertz waves emitted from the three-dimensional topological insulator Bi2Te3

Arbitrary manipulation of broadband terahertz waves with flexible polarization shaping at the source has great potential in expanding real applications such as imaging, information encryption, and all-optically coherent control of terahertz nonlinear phenomena. Topological insulators featuring unique spin-momentum locked surface state have already exhibited very promising prospects in terahertz emission, detection and modulation, which may lay a foundation for future on-chip topological insulator-based terahertz systems. However, polarization shaped terahertz emission with prescribed manners of arbitrarily manipulated temporal evolution of the amplitude and electric-field vector direction based on topological insulators have not yet been explored. Here we systematically investigated the terahertz radiation from topological insulator Bi2Te3 nanofilms driven by femtosecond laser pulses, and successfully realized the generation of efficient chiral terahertz waves with controllable chirality, ellipticity, and principle axis. The convenient engineering of the chiral terahertz waves was interpreted by photogalvanic effect induced photocurrent, while the linearly polarized terahertz waves originated from linear photogalvanic effect induced shift currents. We believe our works not only help further understanding femtosecond coherent control of ultrafast spin currents in light-matter interaction but also provide an effective way to generate spin-polarized terahertz waves and accelerate the proliferation of twisting the terahertz waves at the source.

physics.optics↗

arXiv2020

TCIM: Triangle Counting Acceleration With Processing-In-MRAM Architecture

Triangle counting (TC) is a fundamental problem in graph analysis and has found numerous applications, which motivates many TC acceleration solutions in the traditional computing platforms like GPU and FPGA. However, these approaches suffer from the bandwidth bottleneck because TC calculation involves a large amount of data transfers. In this paper, we propose to overcome this challenge by designing a TC accelerator utilizing the emerging processing-in-MRAM (PIM) architecture. The true innovation behind our approach is a novel method to perform TC with bitwise logic operations (such as \texttt{AND}), instead of the traditional approaches such as matrix computations. This enables the efficient in-memory implementations of TC computation, which we demonstrate in this paper with computational Spin-Transfer Torque Magnetic RAM (STT-MRAM) arrays. Furthermore, we develop customized graph slicing and mapping techniques to speed up the computation and reduce the energy consumption. We use a device-to-architecture co-simulation framework to validate our proposed TC accelerator. The results show that our data mapping strategy could reduce $99.99\%$ of the computation and $72\%$ of the memory \texttt{WRITE} operations. Compared with the existing GPU or FPGA accelerators, our in-memory accelerator achieves speedups of $9\times$ and $23.4\times$, respectively, and a $20.6\times$ energy efficiency improvement over the FPGA accelerator.

cs.AR↗