Mutually synchronized spin Hall nano-oscillator arrays

Time: Tuesday, October 9th, 10:10
Speaker: Johan AKERMAN, Gothenburg University

Spin Hall nano-oscillators (SHNOs) [1] are an emerging class of nano-scopic microwave signal generators with potential for new disruptive applications ranging from microwave signal generation/detection to neuromorphic computing [2,3]. SHNOs are based on an intrinsic magnetodynamic resonance with frequencies in the GHz range, which depends on material parameters, device layout, and external parameters such as magnetic field and drive current. For sufficiently high current densities, the resonance can be driven into a state of coherent auto-oscillation. Through the magnetoresistance of the device, the auto-oscillation can be used to generate a current- and field-tunable microwave voltage.
The auto-oscillation state is highly non-linear in nature, and neighbouring SHNOs can therefore interact with each other and even mutually synchronize, which further increases the power and coherence of the microwave signal [4]. This is important, as the nano-scale volume of the auto-oscillating spin wave mode is susceptible to thermal noise, leading to detrimental phase noise in the microwave signal. I will present resent results on long chains of SHNOs and the first two-dimensional SHNO arrays. We demonstrate robust mutual synchronization in chains or 21 SHNOs with record quality factors of Q=f/df of 30,000. We also demonstrate robust mutual synchronization in two-dimensional arrays of as many as 8 x 8 = 64 SHNOs. We find that the linewidth of these arrays decreases linearly with the number of SHNOs, which enables us to reach Q factors as high as 170,000, i.e. an order of magnitude higher than literature values. Based on these results I will argue that a viable path towards commercial microwave signal generators based on spintronic devices must be based on mutually synchronized SHNO arrays.
The mutual synchronization phenomenon can also be used for ultra-fast pattern matching with potential for speeding up image recognition by orders of magnitude. With the recent rapidly increasing interest in artificial intelligence and neuromorphic computing, mutually synchronized SHNO chains and arrays hence represent a highly attractive emerging technology platform for low-power, and ultrafast non-conventional computing.
[1] T. Chen, et al, Proc. IEEE 104, 1919 (2016).
[2] J. Grollier, D. Querlioz, and M. Stiles, Proc. IEEE 104, 2024 (2016).
[3] J. Torrejon et al., Nature 547, 428 (2017)
[4] A. A. Awad et al. Nature Physics 13, 292 (2017).
[5] M. Zahedinejad et al. unpublished (2018).

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