Search

The magnetization manipulation by spin-orbit torques (SOTs) in nonmagnetic-metal (NM)/ferromagnet (FM) heterostructures has provided great opportunities for spin devices. Besides the conventional spin Hall effect (SHE) in heavy metals with strong spin-orbit coupling, the orbital currents have been proposed to be another promising approach to generate strong SOTs. Here, we systematically study the SOTs efficiency and its dependence on the FM thickness and different NM/FM interfaces in two prototypical Pt/Py and Ta/Py systems by inserting an ultrathin magnetic layer (0.4 nm thick ML = Co, Fe, Gd, and Ni). The dampinglike (DL) torque efficiency $\xi_{DL}$ is significantly enhanced by inserting ultrathin Co, Fe, and Ni layers and is noticeably suppressed for the Gd insertion. Moreover, the Ni insertion results in a sign change of the field-like (FL) torque in Pt/Py and substantially reduces $\xi_{DL}$ in Ta/Py. These results are likely related to the additional spin currents generated by combining the orbital Hall effect (OHE) in the NM and orbital-to-spin conversion in the ML insertion layer and/or their interfaces, especially for the Ni insertion. Our results demonstrate that inserting ultrathin ML can effectively manipulate the strength and sign of the SOTs, which would be helpful for spintronics applications.

Neuromorphic hardware as a non-Von Neumann architecture has better energy efficiency and parallelism than the conventional computer. Here, with numerical modeling spin-orbit torque (SOT) device using current-induced SOT and Joule heating effects, we acquire its magnetization switching probability as a function of the input current pulses and use it to mimic the spike-timing-dependent plasticity learning behavior like actual brain working. We further demonstrate that the artificial spiking neural network (SNN) built by this SOT device can perform unsupervised handwritten digit recognition with the accuracy of 80% and logic operation learning. Our work provides a new clue to achieving SNN-based neuromorphic hardware using high-energy efficiency and nonvolatile spintronics nanodevices, Comment: 8 pages, 5 figures

The interlayer antiferromagnetic coupling rare-earth/transition-metal bilayer ferrimagnet systems have attracted much attention because they present variously unusual temperature-and field-dependent nontrivial magnetic states and dynamics. These properties and the implementation of their applications in spintronics highly depend on the significant temperature dependence of the magnetic exchange stiffness constant A. Here, we quantitatively determine the temperature dependence of magnetic exchange stiffness A_{Py-Gd} and A_{Gd} in the artificially layered ferrimagnet consisting of a Py/Gd bilayer, using a measurement of anisotropic magnetoresistance (AMR) of the bilayer thin film at different temperatures and magnetic fields. The obtained temperature dependence of A_{Py-Gd} and A_{Gd} exhibit a scaling power law with the magnetization of Gd. The critical field of spin-flop transition and its temperature dependence can also be directly obtained by this method. Additionally, the experimental results are well reproduced by micromagnetic simulations with the obtained parameters A_{Py-Gd} and A_{Gd}, which further confirms the reliability of this easily accessible technique., Comment: 5 pages, 4 figures

We systematically investigated the Ni and Co thickness-dependent perpendicular magnetic anisotropy (PMA) coefficient, magnetic domain structures, and magnetization dynamics of Pt(5 nm)/[Co(t_Co nm)/Ni(t_Ni nm)]5/Pt(1 nm) multilayers by combining the four standard magnetic characterization techniques. The magnetic-related hysteresis loops obtained from the field-dependent magnetization M and anomalous Hall resistivity (AHR) \r{ho}_xy found that the two serial multilayers with t_Co = 0.2 and 0.3 nm have the optimum PMA coefficient K_U well as the highest coercivity H_C at the Ni thickness t_Ni = 0.6 nm. Additionally, the magnetic domain structures obtained by Magneto-optic Kerr effect (MOKE) microscopy also significantly depend on the thickness and K_U of the films. Furthermore, the thickness-dependent linewidth of ferromagnetic resonance is inversely proportional to K_U and H_C, indicating that inhomogeneous magnetic properties dominate the linewidth. However, the intrinsic Gilbert damping constant determined by a linear fitting of frequency-dependent linewidth does not depend on Ni thickness and K_U. Our results could help promote the PMA [Co/Ni] multilayer applications in various spintronic and spin-orbitronic devices., Comment: 17 pages, 4 figures

We experimentally study the dynamical modes excited by current-induced spin-orbit torque and its electrostatic gating effect in a 3-terminal planar nano-gap spin Hall nano-oscillator (SHNO) with a moderate interfacial perpendicular magnetic anisotropy (IPMA). Both quasilinear propagating spin-wave and localized "bullet" modes are achieved and controlled by varying the applied in-plane magnetic field and driving current. The minimum linewidth shows a linear dependence on the actual temperature of the active area, confirming single-mode dynamics based on the nonlinear theory of spin-torque nano-oscillation with a single mode. The observed electrostatic gating tuning oscillation frequency arises from voltage-controlled magnetic anisotropy and threshold current of SHNO via modification of the nonlinear damping and/or the interfacial spin-orbit coupling of the magnetic multilayer. In contrast to previously observed two-mode coexistence degrading the spectral purity in Py/Pt-based SHNOs with a negligible IPMA, a single coherent spin-wave mode with a low driven current can be achieved by selecting the ferromagnet layer with a suitable IPMA because the nonlinear mode coupling can be diminished by bringing in the PMA field to compensate the easy-plane shape anisotropy. Moreover, the simulations demonstrate that the experimentally observed current and gate-voltage modulation of auto-oscillation modes are also closely associated with the nonlinear damping and mode coupling, which are determined by the ellipticity of magnetization precession. The demonstrated nonlinear mode coupling mechanism and electrical control approach of spin-wave modes could provide the clue to facilitate the implementation of the mutual synchronization map for neuromorphic computing applications in SHNO array networks., Comment: 11 pages, 10 figures

We study inserting Co layer thickness-dependent spin transport and spin-orbit torques (SOTs) in the Pt/Co/Py trilayers by spin-torque ferromagnetic resonance. The interfacial perpendicular magnetic anisotropy energy density ($K_s = 2.7~erg/cm^2$), which is dominated by interfacial spin-orbit coupling (ISOC) in the Pt/Co interface, total effective spin-mixing conductance ($G_{eff,tot} = 0.42 {\times} 10^{15}~{\Omega}^{-1} m^{-2}$) and two-magnon scattering (${\beta}_{TMS} = 0.46~nm^2$) are first characterized, and the damping-like torque (${\xi}_{DL}$ = 0.103) and field-like torque (${\xi}_{FL}$ = -0.017) efficiencies are also calculated quantitatively by varying the thickness of the inserting Co layer. The significant enhancement of ${\xi}_{DL}$ and ${\xi}_{FL}$ in Pt/Co/Py than Pt/Py bilayer system originates from the interfacial Rashba-Edelstein effect due to the strong ISOC between Co-3d and Pt-5d orbitals at the Pt/Co interface. Additionally, we find a considerable out-of-plane spin polarization SOT, which is ascribed to the spin anomalous Hall effect and possible spin precession effect due to IPMA-induced perpendicular magnetization at the Pt/Co interface. Our results demonstrate that the ISOC of the Pt/Co interface plays a vital role in spin transport and SOTs-generation. Our finds offer an alternative approach to improve the conventional SOTs efficiencies and generate unconventional SOTs with out-of-plane spin polarization to develop low power Pt-based spintronic via tailoring the Pt/FM interface., Comment: 17 pages, 4 figures

The chiral magnets with topological spin textures provide a rare platform to explore topology and magnetism for potential application implementation. Here, we study the magnetic dynamics of several spin configurations on the monoaxial chiral magnetic crystal $MnNb_3S_6$ via broadband ferromagnetic resonance (FMR) technique at cryogenic temperature. In the high-field forced ferromagnetic state (FFM) regime, the obtained frequency f vs. resonance field Hres dispersion curve follows the well-known Kittel formula for a single FFM, while in the low-field chiral magnetic soliton lattice (CSL) regime, the dependence of Hres on magnetic field angle can be well-described by our modified Kittel formula including the mixture of a helical spin segment and the FFM phase. Furthermore, compared to the sophisticated Lorentz micrograph technique, the observed magnetic dynamics corresponding to different spin configurations allow us to obtain temperature- and field-dependent proportion of helical spin texture and helical spin period ratio L(H)/L(0) via our modified Kittel formula. Our results demonstrated that field- and temperature-dependent nontrivial magnetic structures and corresponding distinct spin dynamics in chiral magnets can be an alternative and efficient approach to uncovering and controlling nontrivial topological magnetic dynamics., Comment: 29 pages (including Supporting Information), 7 figures, accepted by SCIENCE CHINA Physics, Mechanics & Astronomy

This paper aims to address the problem of anomaly discovery in semantic segmentation. Our key observation is that semantic classification plays a critical role in existing approaches, while the incorrectly classified pixels are easily regarded as anomalies. Such a phenomenon frequently appears and is rarely discussed, which significantly reduces the performance of anomaly discovery. To this end, we propose a novel Distillation Comparison Network (DiCNet). It comprises of a teacher branch which is a semantic segmentation network that removed the semantic classification head, and a student branch that is distilled from the teacher branch through a distribution distillation. We show that the distillation guarantees the semantic features of the two branches hold consistency in the known classes, while reflect inconsistency in the unknown class. Therefore, we leverage the semantic feature discrepancy between the two branches to discover the anomalies. DiCNet abandons the semantic classification head in the inference process, and hence significantly alleviates the issue caused by incorrect semantic classification. Extensive experimental results on StreetHazards dataset and BDD-Anomaly dataset are conducted to verify the superior performance of DiCNet. In particular, DiCNet obtains a 6.3% improvement in AUPR and a 5.2% improvement in FPR95 on StreetHazards dataset, achieves a 4.2% improvement in AUPR and a 6.8% improvement in FPR95 on BDD-Anomaly dataset. Codes are available at https://github.com/zhouhuan-hust/DiCNet., Comment: 9 pages, 7 figures

Spin-orbit torques due to interfacial Rashba and spin Hall effects have been widely considered as a potentially more efficient approach than the conventional spin-transfer torque to control the magnetization of ferromagnets. We report a comprehensive study of spin-orbit torque efficiency in Ta(O)/Ni81Fe19 bilayers by tuning low-oxidation of \b{eta}-phase tantalum, and find that the spin Hall angle {\theta}DL increases from ~ -0.18 of the pure Ta/Py to the maximum value ~ -0.30 of Ta(O)/Py with 7.8% oxidation. Furthermore, we distinguish the efficiency of the spin-orbit torque generated by the bulk spin Hall effect and by interfacial Rashba effect, respectively, via a series of Py/Cu(0-2 nm)/Ta(O) control experiments. The latter has more than twofold enhancement, and even more significant than that of the former at the optimum oxidation level. Our results indicate that 65% enhancement of the efficiency should be related to the modulation of the interfacial Rashba-like spin-orbit torque due to oxygen-induced orbital hybridization cross the interface. Our results suggest that the modulation of interfacial coupling via oxygen-induced orbital hybridization can be an alternative method to boost the change-spin conversion rate., Comment: 15 pages, 4 figures

Spin waves (SWs), the collective precessional motion of spins in a magnetic system, have been proposed as a promising alternative system with low-power consumption for encoding information. Spin Hall nano-oscillator (SHNO), a new-type spintronic nano-device, can electrically excite and control spin waves in both nanoscale magnetic metals and insulators with low damping by the spin current due to spin Hall effect. Here, we will review recent progress about spin-wave excitation and experimental parameters dependent spectrum in SHNOs. The nanogap SHNOs based on in-plane magnetization Py/Pt exhibits a nonlinear self-localized bullet soliton localized at the center of the gap between the electrodes and a secondary high-frequency mode which coexists with the primary bullet mode at higher currents. While in the nanogap SHNOs with strong perpendicular magnetic anisotropy (PMA), besides both nonlinear bullet soliton and propagating spin-wave mode are achieved and controlled by varying the external magnetic field and current, the magnetic bubble skyrmion mode also can be excited at a low in-plane magnetic field. These SW modes show thermal-induced mode hopping behavior at high temperature due to the coupling between modes mediated by thermal-magnon-mediated scattering. Moreover, thanks to PMA-induced effective field, a single coherent mode also can be achieved without applying an external magnetic field. The strong nonlinear effect of spin-waves makes SHNOs easy to achieve synchronization with external microwave signals or mutual synchronization between multiple oscillators with improving the coherence and power of oscillation modes significantly. Spin-waves in SHNOs with an external free magnetic layer have a wide range of applications from as a nanoscale signal source of low-power consumption magnonic devices to spin-based neuromorphic computing systems in the field of artificial intelligence., Comment: 33 pages, 9 figures, Review article

Current VHR(Very High Resolution) satellite images enable the detailed monitoring of the earth and can capture the ongoing works of railway construction. In this paper, we present an integrated framework applied to monitoring the railway construction in China, using QuickBird, GF-2 and Google Earth VHR satellite images. We also construct a novel DCNNs-based (Deep Convolutional Neural Networks) semantic segmentation network to label the temporary works such as borrow & spoil area, camp, beam yard and ESAs(Environmental Sensitive Areas) such as resident houses throughout the whole railway construction project using VHR satellite images. In addition, we employ HED edge detection sub-network to refine the boundary details and attention cross entropy loss function to fit the sample class disequilibrium problem. Our semantic segmentation network is trained on 572 VHR true color images, and tested on the 15 QuickBird true color images along Ruichang-Jiujiang railway during 2015-2017. The experiment results show that compared with the existing state-of-the-art approach, our approach has obvious improvements with an overall accuracy of more than 80%., Comment: 15 pages

Spintronic nanodevices have ultrafast nonlinear dynamic and recurrence behaviors on a nanosecond scale that promises to enable spintronic reservoir computing (RC) system. Here two physical RC systems based on a single magnetic skyrmion memristor (MSM) and 24 spin-torque nano-oscillators (STNOs) were proposed and modeled to process image classification task and nonlinear dynamic system prediction, respectively. Based on our micromagnetic simulation results on the nonlinear responses of MSM and STNO with current pulses stimulation, the handwritten digits recognition task domesticates that an RC system using one single MSM has the outstanding performance on image classification. In addition, the complex unknown nonlinear dynamic problems can also be well solved by a physical RC system consisted of 24 STNOs confirmed in a second-order nonlinear dynamic system and NARMA10 tasks. The capability of both high accuracy and fast information processing promises to enable one type of brain-like chip based on spintronics for various artificial intelligence tasks., Comment: 7 pages, 6 figures