Agility of spin Hall nano-oscillators measured using time-resolved micro-BLS

Author / Creator

MMM 2020 (2020)

Conferences

MMM 2020 Q4: Spin Currents III (2020)

Available as

Online

Summary

Spin Hall nano-oscillators (SHNOs) have the unique ability to convert a direct current input to microwave signals by means of spin Hall effect and spin orbit torque [1]. The generated microwave sig...

Spin Hall nano-oscillators (SHNOs) have the unique ability to convert a direct current input to microwave signals by means of spin Hall effect and spin orbit torque [1]. The generated microwave signals depend on several factors inherent to having a flow of direct current such as the current density, Oersted fields, Joule heating and naturally the magnetic state in a given device geometry. In addition, other factors such as frequency locking or synchronisation onto an external microwave source can contribute to the microwave output of a given SHNO [2-5]. The interplay of all these factors affects the agility of the magnetisation auto-oscillations of an SHNO. We report the study of SHNOs while subject to current pulses as well as RF pulses, measured using time resolved micro-focused Brillouin light scattering (micro-BLS). The SHNOs under test consist of a double-disk constriction of NiFe(5 nm)/Pt(7 nm). First, we discuss how few-nanosecond pulses can still efficiently induce magnetisation auto-oscillations, thereby demonstrating that both the onset and outset of the auto-oscillations occur within a sub-nanosecond timescale. Then, we proceed to showing how auto-oscillations can be affected by external microwave pulses. Various degrees of enhancement (injection-locking) or suppression of the auto-oscillation signal can be achieved by choice of the frequency and the amplitude of the external microwave signal. The knowledge of the agility or the response to either intended or unwanted parasitic external excitations is paramount for SHNOs to be able to operate on very short time-scale with well defined responses in, for example, the context neuromorphic computing [6].References: [1] Demidov, V. E. et al., Nature Materials, 11(12), 1028-1031 (2012). [2] Demidov, V. E. et al., Nature Communications, 5(1), 3179 (2014). [3] Sato, N. et al. Physical Review Letters, 123(5), 057204 (2019). [4] Hache, T. et al., Applied Physics Letters, 116(19), 192405 (2020). [5] Hache, T. et al., Applied Physics Letters, 114(10), 102403 (2019). [6] Zahedinejad, M. et al., Nature Nanotechnology, 15(1), 47-52 (2020).

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