arXiv subjects

Weisheng Zhao

Publications and source records attributed to Weisheng Zhao.

At least 73 records · Page 4Linked to original sources

arXiv2023

Spin-flop magnetoresistance in a collinear antiferromagnetic tunnel junction

Collinear antiferromagnetic (AFM) materials have unique promise of no stray fields, display ultrafast dynamics, and being robust against perturbation filed which motivates the extensive research of antiferromagnetic spintronics. However, the manipulation and detection of antiferromagnetic order remain formidable challenges. Here, we report the electrical detection of colinear antiferromagnetism in all-epitaxial RuO2/MgO/RuO2 three-terminal tunnel junctions (TJ) using spin-flop tunnel anisotropy magnetoresistance (TAMR). We measured a TAMR ratio of around 60% at room temperature, which arises between the parallel and perpendicular configurations of the adjacent collinear AFM state. Furthermore, we carried out angular dependent measurements using this AFM-TJ and showed that the magnitude of anisotropic longitudinal magnetoresistance in the AFM-TJ can be controlled by the direction of magnetic field. We also theoretically found that the colinear antiferromagnetic MTJ may produce a substantially large TAMR ratio as a result of the time-reversal, strong spin orbit coupling (SOC) characteristic of antiferromagnetic RuO2. Our work not only propels antiferromagnetic materials to the forefront of spintronic device innovation but also unveils a novel paradigm for electrically governed antiferromagnetic spintronics, auguring transformative advancements in high-speed, low-energy information devices.

physics.app-ph↗

arXiv2023

DDC-PIM: Efficient Algorithm/Architecture Co-design for Doubling Data Capacity of SRAM-based Processing-In-Memory

Processing-in-memory (PIM), as a novel computing paradigm, provides significant performance benefits from the aspect of effective data movement reduction. SRAM-based PIM has been demonstrated as one of the most promising candidates due to its endurance and compatibility. However, the integration density of SRAM-based PIM is much lower than other non-volatile memory-based ones, due to its inherent 6T structure for storing a single bit. Within comparable area constraints, SRAM-based PIM exhibits notably lower capacity. Thus, aiming to unleash its capacity potential, we propose DDC-PIM, an efficient algorithm/architecture co-design methodology that effectively doubles the equivalent data capacity. At the algorithmic level, we propose a filter-wise complementary correlation (FCC) algorithm to obtain a bitwise complementary pair. At the architecture level, we exploit the intrinsic cross-coupled structure of 6T SRAM to store the bitwise complementary pair in their complementary states ($Q/\overline{Q}$), thereby maximizing the data capacity of each SRAM cell. The dual-broadcast input structure and reconfigurable unit support both depthwise and pointwise convolution, adhering to the requirements of various neural networks. Evaluation results show that DDC-PIM yields about $2.84\times$ speedup on MobileNetV2 and $2.69\times$ on EfficientNet-B0 with negligible accuracy loss compared with PIM baseline implementation. Compared with state-of-the-art SRAM-based PIM macros, DDC-PIM achieves up to $8.41\times$ and $2.75\times$ improvement in weight density and area efficiency, respectively.

cs.AR↗

arXiv2023

SOT-MRAM-Enabled Probabilistic Binary Neural Networks for Noise-Tolerant and Fast Training

We report the use of spin-orbit torque (SOT) magnetoresistive random-access memory (MRAM) to implement a probabilistic binary neural network (PBNN) for resource-saving applications. The in-plane magnetized SOT (i-SOT) MRAM not only enables field-free magnetization switching with high endurance (> 10^11), but also hosts multiple stable probabilistic states with a low device-to-device variation (< 6.35%). Accordingly, the proposed PBNN outperforms other neural networks by achieving an 18* increase in training speed, while maintaining an accuracy above 97% under the write and read noise perturbations. Furthermore, by applying the binarization process with an additional SOT-MRAM dummy module, we demonstrate an on-chip MNIST inference performance close to the ideal baseline using our SOT-PBNN hardware.

physics.app-ph↗

arXiv2023

Giant orbit-to-charge conversion induced via the inverse orbital Hall effect

We investigate the orbit-to-charge conversion in YIG/Pt/nonmagnetic material (NM) trilayer heterostructures. With the additional Ru layer on the top of YIG/Pt stacks, the charge current signal increases nearly an order of magnitude in both longitudinal spin Seebeck effect (SSE) and spin pumping (SP) measurements. Through thickness dependence studies of the Ru metal layer and theoretical model, we quantitatively clarify different contributions of the increased SSE signal that mainly comes from the inverse orbital Hall effect (IOHE) of Ru, and partially comes from the orbital sink effect in the Ru layer. A similar enhancement of SSE(SP) signals is also observed when Ru is replaced by other materials (Ta, W, and Cu), implying the universality of the IOHE in transition metals. Our findings not only suggest a more efficient generation of the charge current via the orbital angular moment channel but also provides crucial insights into the interplay among charge, spin, and orbit.

cond-mat.mtrl-sci↗

arXiv2023

Orbitronics: Light-induced Orbit Currents in Terahertz Emission Experiments

Orbitronics is based on the use of orbit currents as information carriers. Up to now, orbit currents were created from the conversion of charge or spin currents, and inversely, they could be converted back to charge or spin currents. Here we demonstrate that orbit currents can also be generated by femtosecond light pulses on Ni. In multilayers associating Ni with oxides and nonmagnetic metals such as Cu, we detect the orbit currents by their conversion into charge currents and the resulting terahertz emission. We show that the orbit currents extraordinarily predominate the light-induced spin currents in Ni-based systems, whereas only spin currents can be detected with CoFeB-based systems. In addition, the analysis of the time delays of the terahertz pulses leads to relevant information on the velocity and propagation of orbit carriers. Our finding of light-induced orbit currents and our observation of their conversion into charge currents opens new avenues in orbitronics, including the development of orbitronic terahertz devices.

cond-mat.mes-hall↗

arXiv2023

Ultralow power and shifting-discretized magnetic racetrack memory device driven by chirality switching and spin current

Magnetic racetrack memory has significantly evolved and developed since its first experimental verification and is considered as one of the most promising candidates for future high-density on-chip solid state memory. However, the lack of a fast and precise magnetic domain wall (DW) shifting mechanism and the required extremely high DW motion (DWM) driving current both make the racetrack difficult to commercialize. Here, we propose a method for coherent DWM that is free from above issues, which is driven by chirality switching (CS) and an ultralow spin-orbit-torque (SOT) current. The CS, as the driving force of DWM, is achieved by the sign change of DM interaction which is further induced by a ferroelectric switching voltage. The SOT is used to break the symmetry when the magnetic moment is rotated to the Bloch direction. We numerically investigate the underlying principle and the effect of key parameters on the DWM through micromagnetic simulations. Under the CS mechanism, a fast (102 m/s), ultralow energy (5 attojoule), and precisely discretized DWM can be achieved. Considering that skyrmions with topological protection and smaller size are also promising for future racetrack, we similarly evaluate the feasibility of applying such a CS mechanism to a skyrmion. However, we find that the CS only causes it to "breathe" instead of moving. Our results demonstrate that the CS strategy is suitable for future DW racetrack memory with ultralow power consumption and discretized DWM.

cond-mat.mes-hall↗

arXiv2023

Hardware-Aware Graph Neural Network Automated Design for Edge Computing Platforms

Graph neural networks (GNNs) have emerged as a popular strategy for handling non-Euclidean data due to their state-of-the-art performance. However, most of the current GNN model designs mainly focus on task accuracy, lacking in considering hardware resources limitation and real-time requirements of edge application scenarios. Comprehensive profiling of typical GNN models indicates that their execution characteristics are significantly affected across different computing platforms, which demands hardware awareness for efficient GNN designs. In this work, HGNAS is proposed as the first Hardware-aware Graph Neural Architecture Search framework targeting resource constraint edge devices. By decoupling the GNN paradigm, HGNAS constructs a fine-grained design space and leverages an efficient multi-stage search strategy to explore optimal architectures within a few GPU hours. Moreover, HGNAS achieves hardware awareness during the GNN architecture design by leveraging a hardware performance predictor, which could balance the GNN model accuracy and efficiency corresponding to the characteristics of targeted devices. Experimental results show that HGNAS can achieve about $10.6\times$ speedup and $88.2\%$ peak memory reduction with a negligible accuracy loss compared to DGCNN on various edge devices, including Nvidia RTX3080, Jetson TX2, Intel i7-8700K and Raspberry Pi 3B+.

cs.LG↗

arXiv2023

Inverse Orbital Hall Effect Discovered from Light-Induced Terahertz Emission

Recent progress in orbitronics reveals the possibility of using orbit current as an information carrier. The interconversion between orbit currents and charge currents is crucial for orbit information processing. Although orbit currents can be created from charge currents via the orbital Hall effect, the conversion from orbit currents into charge currents has been observed only in very few systems due to the lack of a reliable orbit current source and the disturbance of the omnipresent inverse spin Hall effect. In this study, we show that ultrafast pulses of orbit current can be generated in Ni layers by femtosecond laser pulses. We demonstrate that, by injecting such orbit current pulses into nonmagnetic metals, a transient charge current is induced and emits terahertz electromagnetic pulses. The nonmagnetic metal layer acts as a converter of the orbit current into the charge current. The discovery of the generation and conversion of light-induced orbit current opens a new route for developing future orbitronic devices.

cond-mat.mes-hall↗

arXiv2023

Single-shot laser-induced switching of an exchange biased antiferromagnet

Ultrafast manipulation of magnetic order has challenged our understanding the fundamental and dynamic properties of magnetic materials. So far single shot magnetic switching has been limited to ferrimagnetic alloys and multilayers. In ferromagnetic (FM)/antiferromagnetic (AFM) bilayers, exchange bias (He) arises from the interfacial exchange coupling between the two layers and reflects the microscopic orientation of the antiferromagnet. Here we demonstrate the possibility of single shot switching of the antiferromagnet (change of the sign and amplitude of He) with a single femtosecond laser pulse in IrMn/CoGd bilayers. We demonstrate the switching for a wide range of fluences for different layer thicknesses and compositions. Atomistic simulations predict ultrafast switching and recovery of the AFM magnetization on a timescale of 2 ps. These results provide the fastest and the most energy-efficient method to set the exchange bias and pave the way to potential applications for ultrafast spintronic devices.

cond-mat.mtrl-sci↗

arXiv2022

The effect of temperature and excitation energy on Raman scattering in bulk HfS$_2$

Raman scattering (RS) in bulk hafnium disulfide (HfS$_2$) is investigated as a function of temperature (5 K $-$ 350 K) with polarization resolution and excitation of several laser energies. An unexpected temperature dependence of the energies of the main Raman-active (A$_{\textrm{1g}}$ and E$_{\textrm{g}}$) modes with the temperature-induced blueshift in the low-temperature limit is observed. The low-temperature quenching of a mode $ω_1$ (134 cm$^{-1}$) and the emergence of a new mode at approx. 184 cm$^{-1}$, labeled Z, is reported. The optical anisotropy of the RS in HfS$_2$ is also reported, which is highly susceptible to the excitation energy. The apparent quenching of the A$_{\textrm{1g}}$ mode at $T$=5 K and of the E$_{\textrm{g}}$ mode at $T$=300 K in the RS spectrum excited with 3.06~eV excitation is also observed. We discuss the results in the context of possible resonant character of light-phonon interactions. Analyzed is also a possible effect of the iodine molecules intercalated in the van der Waals gaps between neighboring HfS$_2$ layers, which inevitably result from the growth procedure.

cond-mat.mes-hall↗

arXiv2022

Emergent Anti-ferromagnetism in a Y -Shaped Kekulé Graphene

Antiferromagnetic (AF) transitions of birefringent Dirac fermions created by a Y-shaped Kekulé distortion in graphene are investigated by the mean-field theory and the determinant quantum Monte Carlo simulations. We show that the quantum critical point can be continuously tuned by the bond-modulation strength, and the universality of the quantum criticality remains in the Gross-Neveu-Heisenberg class. The critical interaction scales with the geometric average of the two velocities of the birefringent Dirac cones and decreases monotonically between the uniform and the completely depleted limits. Since the AF critical interaction can be tuned to very small values, antiferromagnetism may emerge automatically, realizing the long-sought magnetism in graphene. These results enrich our understanding of the semimetal-AF transitions in Dirac-fermion systems and open a new route to achieving magnetism in graphene.

cond-mat.str-el↗

arXiv2022

Real-time probing of magnetic domain wall dynamic

We present a study of a very seldom used way of measuring magnetic domain wall velocity, which makes it possible to have a real-time probing of the domain movement in the area illuminated by a laser spot. We have compared this method to the most usual one: although the velocities are similar, the different method do not give the same results if the laser spot is too small. It can be explained by a dendritic shape of the domain wall. By changing the size spot, we propose a basic model which describes quite well the transit time in the laser spot as a function of its size and makes it possible to extract the velocity and the depth of the dendrites.

physics.app-ph↗

arXiv2022

The Existence of Graph whose Vertex Set Can be Partitioned into a Fixed Number of Domination Strong Critical Vertex-sets

Let $γ(G)$ denote the domination number of a graph $G$. A vertex $v\in V(G)$ is called a \emph{critical vertex} of $G$ if $γ(G-v)=γ(G)-1$. A graph is called \emph{vertex-critical} if every vertex of it is critical. In this paper, we correspondingly introduce two such definitions: (i) a set $S\subseteq V(G)$ is called a \emph{strong critical vertex-set} of $G$ if $γ(G-S)=γ(G)-|S|$; (ii) a graph $G$ is called \emph{strong $l$-vertex-sets-critical} if $V(G)$ can be partitioned into $l$ strong critical vertex-sets of $G$. Whereafter, we give some properties of strong $l$-vertex-sets-critical graphs by extending the previous results of vertex-critical graphs. As the core work, we study on the existence of this class of graphs and obtain that there exists a strong $l$-vertex-sets-critical connected graph if and only if $l\notin\{2,3,5\}$.

math.CO↗

arXiv2022

Towards Systems Education for Artificial Intelligence: A Course Practice in Intelligent Computing Architectures

With the rapid development of artificial intelligence (AI) community, education in AI is receiving more and more attentions. There have been many AI related courses in the respects of algorithms and applications, while not many courses in system level are seriously taken into considerations. In order to bridge the gap between AI and computing systems, we are trying to explore how to conduct AI education from the perspective of computing systems. In this paper, a course practice in intelligent computing architectures are provided to demonstrate the system education in AI era. The motivation for this course practice is first introduced as well as the learning orientations. The main goal of this course aims to teach students for designing AI accelerators on FPGA platforms. The elaborated course contents include lecture notes and related technical materials. Especially several practical labs and projects are detailed illustrated. Finally, some teaching experiences and effects are discussed as well as some potential improvements in the future.

cs.AI↗

arXiv2022

Eventor: An Efficient Event-Based Monocular Multi-View Stereo Accelerator on FPGA Platform

Event cameras are bio-inspired vision sensors that asynchronously represent pixel-level brightness changes as event streams. Event-based monocular multi-view stereo (EMVS) is a technique that exploits the event streams to estimate semi-dense 3D structure with known trajectory. It is a critical task for event-based monocular SLAM. However, the required intensive computation workloads make it challenging for real-time deployment on embedded platforms. In this paper, Eventor is proposed as a fast and efficient EMVS accelerator by realizing the most critical and time-consuming stages including event back-projection and volumetric ray-counting on FPGA. Highly paralleled and fully pipelined processing elements are specially designed via FPGA and integrated with the embedded ARM as a heterogeneous system to improve the throughput and reduce the memory footprint. Meanwhile, the EMVS algorithm is reformulated to a more hardware-friendly manner by rescheduling, approximate computing and hybrid data quantization. Evaluation results on DAVIS dataset show that Eventor achieves up to $24\times$ improvement in energy efficiency compared with Intel i5 CPU platform.

cs.AR↗

arXiv2022

Field-free spin orbit torque switching of synthetic antiferromagnet through interlayer Dzyaloshinskii-Moriya interaction

Perpendicular synthetic antiferromagnets (SAFs) are of interest for the next generation ultrafast, high density spintronic memory and logic devices. However, to energy efficiently operate their magnetic order by current-induced spin orbit torques (SOTs), an unfavored high external field is conventionally required to break the symmetry. Here, we theoretically and experimentally demonstrate the field-free SOT switching of a perpendicular SAF through the introduction of interlayer Dzyaloshinskii-Moriya interaction (DMI). By macro-spin simulation, we show that the speed of field-free switching increases with the in-plane mirror asymmetry of injected spins. We experimentally observe the existence of interlayer DMI in our SAF sample by an azimuthal angular dependent anomalous Hall measurement. Field-free switching is accomplished in such a sample and the strength of the effective switching field demonstrates its origin from interlayer DMI. Our results provide a new strategy for SAF based high performance SOT devices.

physics.app-ph↗

arXiv2022

Spin Manipulation by Giant Valley-Zeeman Spin-Orbit Field in Atom-Thick WSe2

The phenomenon originating from spin-orbit coupling (SOC) provides energy-efficient strategies for spin manipulation and device applications. The broken inversion symmetry interface and resulting electric field induce a Rashba-type spin-orbit field (SOF), which has been demonstrated to generate spin-orbit torque for data storage applications. In this study, we found that spin flipping can be achieved by the valley-Zeeman SOF in monolayer WSe2 at room temperature, which manifests as a negative magnetoresistance in the vertical spin valve. Quantum transmission calculations based on an effective model near the K valley of WSe2 confirm the precessional spin transport of carriers under the giant SOF, which is estimated to be 650 T. In particular, the valley-Zeeman SOF-induced spin dynamics was demonstrated to be tunable with the layer number and stacking phase of WSe2 as well as the gate voltage, which provides a novel strategy for spin manipulation and can benefit the development of ultralow-power spintronic devices.

cond-mat.mes-hall↗

arXiv2022

NAND-SPIN-Based Processing-in-MRAM Architecture for Convolutional Neural Network Acceleration

The performance and efficiency of running large-scale datasets on traditional computing systems exhibit critical bottlenecks due to the existing "power wall" and "memory wall" problems. To resolve those problems, processing-in-memory (PIM) architectures are developed to bring computation logic in or near memory to alleviate the bandwidth limitations during data transmission. NAND-like spintronics memory (NAND-SPIN) is one kind of promising magnetoresistive random-access memory (MRAM) with low write energy and high integration density, and it can be employed to perform efficient in-memory computation operations. In this work, we propose a NAND-SPIN-based PIM architecture for efficient convolutional neural network (CNN) acceleration. A straightforward data mapping scheme is exploited to improve the parallelism while reducing data movements. Benefiting from the excellent characteristics of NAND-SPIN and in-memory processing architecture, experimental results show that the proposed approach can achieve $\sim$2.6$\times$ speedup and $\sim$1.4$\times$ improvement in energy efficiency over state-of-the-art PIM solutions.

cs.AR↗