Recent progress on excitation and manipulation of spin-waves in spin Hall nano-oscillators

doi:10.1088/1674-1056/abaed5

Abstract

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 and interfacial Rashba effect. Several spin-wave modes have been excited successfully and investigated substantially in SHNOs based on dozens of different ferromagnetic/nonmagnetic (FM/NM) bilayer systems (e.g., FM = Py, [Co/Ni], Fe, CoFeB, Y₃Fe₅O₁₂; NM = Pt, Ta, W). Here, we will review recent progress about spin-wave excitation and experimental parameters dependent dynamics in SHNOs. The nanogap SHNOs with in-plane magnetization exhibit 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 out of plane magnetization, 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 spin-wave modes show thermal-induced mode hopping behavior at high temperature due to the coupling between the modes mediated by thermal magnon mediated scattering. Moreover, thanks to the perpendicular magnetic anisotropy induced effective field, the 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 which improve 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.

Keywords: spin-orbit torque spin Hall nano-oscillator spin-waves synchronization

Received: 03 July 2020
Revised: 07 August 2020
Accepted manuscript online: 13 August 2020

Fund: the National Key Research and Development Program of China (Grant No. 2016YFA0300803), the National Natural Science Foundation of China (Grant Nos. 11774150, 12074178, and 12004171), the Applied Basic Research Programs of Science and Technology Commission Foundation of Jiangsu Province, China (Grant No. BK20170627), and the Open Research Fund of Jiangsu Provincial Key Laboratory for Nanotechnology.

Corresponding Authors: ^†Corresponding author. E-mail: linachen@nju.edu.cn ^‡Corresponding author. E-mail: rhliu@nju.edu.cn

Cite this article:

Liyuan Li(李丽媛), Lina Chen(陈丽娜), Ronghua Liu(刘荣华), and Youwei Du(都有为) Recent progress on excitation and manipulation of spin-waves in spin Hall nano-oscillators 2020 Chin. Phys. B 29 117102

Fig. 1.

Several types of magnetization dynamical modes observed in spin-torque nano-oscillators. (a)–(b) 3D spatial intensity distribution of the quasilinear propagating spin-wave mode (a) and nonlinear localized bullet mode (b), respectively. (c)–(f) Snapshots of the spatial magnetization distribution of the dynamical droplet mode or bubble mode without nontrivial topological property (c), Bloch-type (d), or Neel-type bubble skyrmion mode (e) with a topological number N = 1, and gyrotropic vortex mode (f) with a topological number N = 1/2, respectively. The color and vector represent the amplitude of the out-of-plane magnetization component M_z (left color label) and the direction of M in (c)–(e), respectively. The right color label represents the in-plane magnetization component M_y in (f). (a) and (b) are adopted from Refs. [21,25] with permission.

Fig. 2.

(a) Schematic of the SHNO device structure and the experimental setup. (b) Schematic of the cross-sectional view of charge and spin currents distribution of SHNO device. (c) Pseudocolor map of the power spectral density (PSD) of the experimentally obtained microwave signal of nanogap SHNO based on a Py(5)/Pt(3) bilayer for varying current at H = 200 Oe and θ = 60°. (d) Pseudocolor map of the experimentally obtained PSD for the varying field at I = 17 mA and θ = 60°. Insets in (c) and (d): Normalized spatial maps of ${m}_{x}^{2}$ corresponding to the two dominant auto-oscillation modes at f₁ = 2.86 GHz, f₂ = 3.97 GHz and 2f₁ = 5.72 GHz, respectively, which were obtained by micromagnetic simulations. Panels are adapted from Refs. [36,38].

Fig. 3.

(a) The schematic of the SHNO structure and the experimental setup. (b) PSD signals obtained at I = 6.7 mA and the labeled values of the magnetic field for the gate voltage V_g = 0. (c) PSD signals detected at I = 6.2 mA and the labeled values of V_g varying from −5 V to 5 V at H = 340 Oe. Inset: frequency shift vs. gate voltage. (d) Pseudocolor map of three-generation microwave spectra at V_g = 0, ± 5 V with different currents. Reproduced with permission from from Ref. [37].

Fig. 4.

(a) Anomalous Hall effect measured in a film with in-plane (triangles) and out-of-plane (circles) field at 295 K. (b) Dependence of the device resistance on the direction of in-plane field H = 1 kOe, due to the anisotropic magnetoresistance of the magnetic film. (c) Pseudocolor map of the PSD for varying current at H = 1.1 kOe. The dashed line marks the FMR of the magnetic film. Insets hint the spatial characteristics of the propagating mode (left) and bullet mode (right), respectively. (d) PSD signals obtained varying field H with a step of 100 Oe at I = 13 mA. (e)–(g) Snapshot of dynamical magnetization obtained from micromagnetic simulations of propagating spin-wave, bullet mode, and magnetic bubble skyrmion, respectively. The out-of-plane and in-plane magnetization components are represented by color and vector, respectively. Reproduced with permission from Refs. [20, 21].

Fig. 5.

Temperature effect on mode hopping. Pseudocolor maps of the dependence of the generated microwave spectra on the current at several selected fields measured at T = 295 K (a)–(c) and T = 6 K (d)–(f). Reproduced with permission from Ref. [49].

Fig. 6.

Microwave spectra and micromagnetic simulation of the VNC-SHNO. (a) The device structure and the experimental setup of VNC-SHNO. (b)–(c) Pseudocolor plots of the current-dependent spectra of SHNO experimentally obtained at fields H = 960 Oe (b), and H = 1090 Oe (c) with angle ϕ = 82° relative to the film plane and T = 295 K. (d) Representative calculated auto-oscillation spectrum at H = 1000 Oe, ϕ = 85°, and I = 14 mA. (e) Normalized spatial maps of ${m}_{x}^{2}$ of the fundamental droplet mode. The boundary of the active simulation region and the nanocontact are marked by the large solid circle and dotted circle, respectively. Reproduced with permission from Ref. [51].

Fig. 7.

(a) BLS spectra of SHNO under an external RF signal. The dashed line represents that the auto-oscillation frequency exactly follows f_MW/2. (b) A scanning electron microscope image of an SHNO array with nine 120-nm-wide nanoconstrictions each separated by 300 nm. (c)–(d) BLS spatial intensity map (c) and BLS frequency map (d) obtained at I = 3.21 mA. Reproduced with permission from Refs. [52, 53].

Fig. 8.

Spin-waves propagation in a microscale waveguide. (a) AFM image (top panel) and schematic of the device structure and the experimental setup (bottom panel). (b) A normalized color-coded map of the measured BLS intensity. (c) The simulation snapshot of the out-of-plane component m_z of the dynamic magnetization. Reproduced with permission from Ref. [58].

Fig. 9.

(a) A schematic of the biological neural network. (b) Top: the schematic experiment setup of an STNO. Bottom: The nonlinear response (relaxation process) of the output voltage V(t) of SHNO with the stimulation voltage V_in. (c)–(f) Training and prediction results for two nonlinear dynamic systems. (c)–(d) The second-order nonlinear system described by Eq. (1): theoretical output (black line) vs. prediction result (red line) in the training phase (c) and the test phase (d). (e) and (f) NARMA10 described by Eq. (2), same as (c) and (d). Reproduced with permission from Refs. [55, 67].

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