Control of the stochastic response of magnetization dynamics in spin-torque oscillator through radio-frequency magnetic fields

Tsunegi, Sumito; Taniguchi, Tomohiro; Suzuki, Daiki; Yakushiji, Kay; Fukushima, Akio; Yuasa, Shinji; Kubota, Hitoshi

doi:10.1038/s41598-021-95636-w

Cited by 6 publications

(7 citation statements)

References 34 publications

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“…Material parameters. The material parameters were taken from typical experiments and simulations [45][46][47] as M = 1300 emu/cm 3 , γ = 1.764 × 10 7 rad/(Oe s), α = 0.01 , L = 5 nm, R = 187.5 nm, R 0 = 10 nm, P = 0.7 , ξ = 2 , and p = (sin 60 • , 0, cos 60 • ) . A current I of 1 mA corresponds to a current density J of 0.9 MA/cm 2 .…”

Section: Methodsmentioning

confidence: 99%

“…The value of the current used in, for example, the experiment in Ref. 47 is 8 mA at maximum. The value of ζ in Ref.…”

Section: Methodsmentioning

confidence: 99%

“…The output signal from a vortex STO is proportional to the y component of the vortex-core position through the magnetoresistance effect. In experiments 18,47,49 , the output signal is decomposed into the amplitude s and the phase through the Hilbert transformation. The amplitude is used as a dynamical response in Refs.…”

Section: Methods Of Evaluating Short-term Memory Capacitymentioning

confidence: 99%

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Input-driven chaotic dynamics in vortex spin-torque oscillator

Imai

Nakajima

Tsunegi

et al. 2022

Sci Rep

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A new research topic in spintronics relating to the operation principles of brain-inspired computing is input-driven magnetization dynamics in nanomagnet. In this paper, the magnetization dynamics in a vortex spin-torque oscillator driven by a series of random magnetic field are studied through a numerical simulation of the Thiele equation. It is found that input-driven synchronization occurs in the weak perturbation limit, as found recently. As well, chaotic behavior is newly found to occur in the vortex core dynamics for a wide range of parameters, where synchronized behavior is disrupted by an intermittency. Ordered and chaotic dynamical phases are examined by evaluating the Lyapunov exponent. The relation between the dynamical phase and the computational capability of physical reservoir computing is also studied.

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Section: Methodsmentioning

confidence: 99%

“…The value of the current used in, for example, the experiment in Ref. 47 is 8 mA at maximum. The value of ζ in Ref.…”

Section: Methodsmentioning

confidence: 99%

Section: Methods Of Evaluating Short-term Memory Capacitymentioning

confidence: 99%

See 1 more Smart Citation

Input-driven chaotic dynamics in vortex spin-torque oscillator

Imai

Nakajima

Tsunegi

et al. 2022

Sci Rep

Self Cite

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“…The oscillator's output was the amplitude of the output voltage estimated by a Hilbert transformation. [36,37,64] Note that thermal activation in a magnetic material adds noise to the amplitude. Therefore, we measured the output signal from the oscillator 60 times for the same input signal.…”

Section: System Outputmentioning

confidence: 99%

Information Processing Capacity of Spintronic Oscillator

Tsunegi

Kubota

Kamimaki

et al. 2023

Advanced Intelligent Systems

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Physical reservoir computing is a framework that enables energy‐efficient information processing by using physical systems. Nonlinear dynamics in physical systems provide a computational capability that is unique to reservoirs. It is, however, difficult to find an appropriate task for a reservoir because of the complexity of nonlinear information processing. The information processing capacity has recently been used to clarify systematically the tasks that are solved by reservoirs; it quantifies the memory capacity of reservoirs in accordance with the order of nonlinearity. Herein, an experimental evaluation of the information processing capacity of a spintronic oscillator consisting of nanostructured ferromagnets is reported. The spintronic reservoir state is electrically manipulated by adding a delayed‐feedback circuit. The total capacity reaches a maximum of 5.6 at the edge of the echo state property. A trade‐off between the linear and nonlinear components of the capacity is also found. The result can be used to better understand the nonlinear information processing in reservoirs and to find good matches between reservoirs and tasks. As an example, a function‐approximation task is performed and it is found that it can be efficiently solved when the reservoir state is appropriately tuned so that its information processing capacity matches that of the task.

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“…∆𝑅 𝑅 ∥ 𝑅 is the AMR resistance and 𝑅 is the minimum resistance of the device. The spectral characteristics of SHOs are normally measured using a spectrum analyzer while the time dependent oscillation amplitudes are detected using an oscilloscope 16,40 . The microwave voltage signal across the device is,…”

Section: Sho Device Modelmentioning

confidence: 99%

Classification tasks using input driven nonlinear magnetization dynamics in spin Hall oscillator

Mohan

Mathew

Nishimura

et al. 2022

Preprint

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The inherent nonlinear magnetization dynamics in spintronic devices makes them suitable candidates for neuromorphic hardware. Among spintronic devices, spin torque oscillators such as spin-toque oscillators and spin Hall oscillators have shown the capability to perform recognition tasks. In this paper, we demonstrate that the magnetization dynamics of a single spin Hall oscillator can be nonlinearly transformed by harnessing input pulse streams and can be utilized for classification tasks. The spin Hall oscillator utilizes the microwave spectral characteristics of its magnetization dynamics for processing a binary data input. The spectral change due to the nonlinear magnetization dynamics assists in real time feature extraction and classification of 4-bit binary digits. The performance is tested for the classification of the standard MNIST handwritten digit data set and achieved an accuracy of 83.1%. Our results suggest that modulating time driven input data can generate diverse magnetization dynamics in the spin Hall oscillator, which is suitable for temporal or sequential information processing.

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Control of the stochastic response of magnetization dynamics in spin-torque oscillator through radio-frequency magnetic fields

Cited by 6 publications

References 34 publications

Input-driven chaotic dynamics in vortex spin-torque oscillator

Input-driven chaotic dynamics in vortex spin-torque oscillator

Information Processing Capacity of Spintronic Oscillator

Classification tasks using input driven nonlinear magnetization dynamics in spin Hall oscillator

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