Compact Macrospin-Based Model of Three-Terminal Spin-Hall Nano Oscillators

Albertsson, Dagur Ingi; Zahedinejad, Mohammad; Åkerman, Johan; Rodriguez, Saul; Rusu, Ana

doi:10.1109/tmag.2019.2925781

Cited by 7 publications

(3 citation statements)

References 42 publications

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“…Another type of spintronic nano-oscillator is MTJ based SHNO, as shown in figure 15(f), where self-oscillation is primarily sustained by SOT current flowing through the SOT channel. This particular spintronic oscillator leverages the advantageous features of SOT, such as high energy efficiency, along with the amplified output power generated by the TMR effect in MTJs [138][139][140].…”

Section: Spin-torque Nano-oscillators (Stnos)mentioning

confidence: 99%

Tunneling magnetoresistance materials and devices for neuromorphic computing

et al. 2023

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Artificial intelligence has become indispensable in modern life, but its energy consumption has become a significant concern due to its huge storage and computational demands. Artificial intelligence algorithms are mainly based on deep learning algorithms, relying on the backpropagation of convolutional neural networks or binary neural networks. While these algorithms aim to simulate the learning process of the human brain, their low bio-fidelity and the separation of storage and computing units lead to significant energy consumption. The human brain is a remarkable computing machine with extraordinary capabilities for recognizing and processing complex information while consuming very low power. Tunneling magnetoresistance (TMR)-based devices, namely magnetic tunnel junctions (MTJs), have great advantages in simulating the behavior of biological synapses and neurons. This is not only because MTJs can simulate biological behavior such as Spike-Timing Dependent Plasticity(STDP) and Leaky Integrate-Fire(LIF), but also because MTJs have intrinsic stochastic and oscillatory properties. These characteristics improve MTJs' bio-fidelity and reduce their power consumption. MTJs also possess advantages such as ultrafast dynamics and non-volatile properties, making them widely utilized in the field of neuromorphic computing in recent years. We conducted a comprehensive review of the development history and underlying principles of TMR, including a detailed introduction to the material and magnetic properties of MTJs and their temperature dependence. We also explored various writing methods of MTJs and their potential applications. Furthermore, we provided a thorough analysis of the characteristics and potential applications of different types of MTJs for neuromorphic computing. TMR-based devices have demonstrated promising potential for broad application in neuromorphic computing, particularly in the development of spiking neural networks. Their ability to perform on-chip learning with ultra-low power consumption makes them an exciting prospect for future advances in the era of the Internet of Things.

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Section: Spin-torque Nano-oscillators (Stnos)mentioning

confidence: 99%

Tunneling magnetoresistance materials and devices for neuromorphic computing

et al. 2023

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“…= τ eq +τ rf (10) where the DC and rf components of B s are separated based on the voltages applied to the heavy metal in Fig. 2(a)…”

Section: Injection Locking Modelmentioning

confidence: 99%

“…We introduce an ISF-based macromodel of the SHNO in Verilog-A that emulates the oscillator's electrical behavior, nonlinear phase dynamics, and thermal phase noise characteristic [33][34][35][36][37]. Previous approaches to model the spin torque oscillator at the circuit level have involved numerically simulating the LLGS equation using an equivalent circuit representation [38][39][40], which is computationally expensive, or solving analytical equations that accurately model the electrical behavior, including output power and linewidth, but do not include nonlinear injection locking [10,[41][42][43][44]. On the other hand, our analytical ISF-based approach comprehensively models the nonlinear behavior of the oscillator without requiring heavy computation.…”

Section: A Verilog-a Oscillator Macromodelmentioning

confidence: 99%

Ising Machine Based on Electrically Coupled Spin Hall Nano-Oscillators

McGoldrick¹,

Sun²,

Liu³

2021

Preprint

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The Ising machine is an unconventional computing architecture that can be used to solve NP-hard combinatorial optimization problems more efficiently than traditional von Neumann architectures. Fast, compact oscillator networks which provide programmable connectivities among arbitrary pairs of nodes are highly desirable for the development of practical oscillator-based Ising machines. Here we propose using an electrically coupled array of GHz spin Hall nano-oscillators to realize such a network. By developing a general analytical framework that describes injection locking of spin Hall oscillators with large precession angles, we explicitly show the mapping between the coupled oscillators' properties and the Ising model. We integrate our analytical model into a versatile Verilog-A device that can emulate the coupled dynamics of spin Hall oscillators in circuit simulators. With this abstract model, we analyze the performance of the spin Hall oscillator network at the circuit level using conventional electronic components and considering phase noise and scalability. Our results provide design insights and analysis tools toward the realization of a CMOS-integrated spin Hall oscillator Ising machine operating with a high degree of time, space, and energy efficiency.

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Voltage-driven gigahertz frequency tuning of spin Hall nano-oscillators

et al. 2022

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Spin Hall nano-oscillators (SHNOs) exploiting current-driven magnetization auto-oscillation have recently received much attention because of their potential for neuromorphic computing. Widespread applications of neuromorphic devices with SHNOs require an energy-efficient method of tuning oscillation frequency over broad ranges and storing trained frequencies in SHNOs without the need for additional memory circuitry. While the voltage-driven frequency tuning of SHNOs has been demonstrated, it was volatile and limited to megahertz ranges. Here, we show that the frequency of SHNOs is controlled up to 2.1 GHz by an electric field of 1.25 MV/cm. The large frequency tuning is attributed to the voltage-controlled magnetic anisotropy (VCMA) in a perpendicularly magnetized Ta/Pt/[Co/Ni]n/Co/AlOx structure. Moreover, the non-volatile VCMA effect enables cumulative control of the frequency using repetitive voltage pulses which mimic the potentiation and depression functions of biological synapses. Our results suggest that the voltage-driven frequency tuning of SHNOs facilitates the development of energy-efficient neuromorphic devices.

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Compact Macrospin-Based Model of Three-Terminal Spin-Hall Nano Oscillators

Abstract: This is the accepted version of a paper published in IEEE transactions on magnetics. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination.

Cited by 7 publications

References 42 publications

Tunneling magnetoresistance materials and devices for neuromorphic computing

Tunneling magnetoresistance materials and devices for neuromorphic computing

Ising Machine Based on Electrically Coupled Spin Hall Nano-Oscillators

Voltage-driven gigahertz frequency tuning of spin Hall nano-oscillators

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