Our new work of spin Hall nano-oscillator entitled “Dynamical mode coexistence and chaos in the nano-gap spin Hall nano-oscillator” is recently published in Phy. Rev. B [Lina Chen, Kaiyuan Zhou, S. Urazhdin, Wencong Jiang, Y. W. Du, and R. H. Liu*, Phys. Rev. B 100, 104436 (2019)]. Congratulations to Dr. Chen. In this paper, we utilize microwave spectroscopy to study the auto-oscillation modes in the nanogap spin Hall nano-oscillators based on Permalloy/Pt bilayers. We show that two distinct spin-wave modes appear in such oscillators, regardless of the magnetic film thicknesses or the size of the electrode gap. We identify the primary mode as a nonlinear self-localized bullet soliton localized at the center of the gap between the electrodes, which is excited over a broad range of currents, field magnitudes, and orientations. The secondary high-frequency mode appears at higher currents and coexists with the primary bullet mode. Micromagnetic modeling shows that this mode is stabilized by the dipolar field of the bullet, and its spatial profile exhibits two maxima offset from the center of the gap in two opposite directions collinear with the field. Simulations also suggest chaotic dynamics that emerges at large currents due to the incoherent coupling between the two modes. Our results demonstrate the possibility to induce and control complex nonlinear dynamical phenomena in spin Hall oscillators, which can be utilized in the emergent neuromorphic and reservoir computing applications. This work is collaborated with Prof. Sergei Urazhdin, Department of Physics, Emory Universty, USA.

Ms. Liyuan Li has been awarded the “Best Poster Prize” at the 2019 Chinese Physical Society Fall Meeting held by Zhengzhou University, Zhengzhou, Henan Province. Congratulations to her!

Our new work entitled “Magnetic droplet mode in a vertical nanocontact-based spin Hall nano-oscillator at oblique fields” is published in Physical Review Applied [Lina Chen, S. Urazhdin, Kaiyuan Zhou, Y. W. Du, and R. H. Liu*, Phys. Rev. Applied 13, 024034 (2020)]. Congratulations to Dr. Chen. In this paper, we experimentally demonstrate an alternative type of spin Hall nano-oscillator based on a vertical nanocontact fabricated on a $\mathrm{Pt}$/($\mathrm{Co}$/$\mathrm{Ni}$) multilayer. We analyze the spectral characteristics of the nano-oscillator as a function of current, magnetic field, and temperature. At sufficiently large currents, the oscillator exhibits dynamics at a frequency far below the ferromagnetic resonance, which at large fields exhibits a redshift with increasing current. At smaller fields and low temperatures, the frequency becomes nearly independent of current, with a well-defined threshold current. These distinct spectral characteristics of the demonstrated nano-oscillator can be explained by the formation of the magnetic droplet—a dissipative magnetic soliton stabilized by the local injection of spin current produced by the spin Hall effect in $\mathrm{Pt}$. The minimum linewidth exhibits a linear temperature dependence, suggesting single-mode dynamics, and enabling coherent magnetization auto-oscillation at room temperature. The demonstrated nano-oscillator geometry provides alternative opportunities for the development of active nanomagnetic devices and for optimization of their spectral characteristics for applications in microwave technology and spin-wave logic. This work is collaborated with Prof. Sergei Urazhdin, Department of Physics, Emory Universty, USA.

Our work about spin-type neuromorphic computing entitled “Physical reservoir computing using magnetic skyrmion memristor and spin torque nano-oscillator” is published in Applied Physics Letters [Wencong Jiang*, Lina Chen*, Kaiyuan Zhou, Liyuan Li, Qingwei Fu, Youwei Du, and R. H. Liu*, Appl. Phys. Lett. 115, 192403 (2019)]. Congratuations to Mr. Wencong Jiang and Dr. Lina Chen. In this work, we  numerically modeled two physical RC systems based on one single magnetic skyrmion memristor (MSM) and 24 spin-torque nano-oscillators (STNOs) to process image classification task and nonlinear dynamic system prediction, respectively. Based on the nonlinear responses of the MSM and STNO with current pulse stimulation, our results demonstrate that the MSM-based RC system exhibits excellent performance on image classification, while the STNO-based RC system does well in solving the complex unknown nonlinear dynamic problems, e.g., a second-order nonlinear dynamic system and NARMA10. Our result and analysis of the current-dependent nonlinear dynamic properties of the MSM and STNO provide the strategy to optimize the experimental parameters in building the better spintronic-based brainlike devices for machine learning based computing.

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