Magnetic skyrmions and domain walls for logical and neuromorphic computing

Hu, Xuan; Cui, Can; Liu, Samuel; García‐Sánchez, Felipe; Brigner, Wesley H.; Walker, Benjamin W.; Edwards, Alexander J.; Xiao, T. Patrick; Bennett, Christopher H.; Hassan, Naimul; Frank, Michael P.; Incorvia, Jean Anne C.; Friedman, Joseph S.

doi:10.1088/2634-4386/acc6e8

Cited by 8 publications

(4 citation statements)

References 126 publications

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“…The device had a voltage dependent activation function and they used this response to implement machine learning using a noisy version of the Fashion-MNIST dataset and claimed that such devices could be used to implement robust networks for NC. The interested reader can refer to the recent review by Hu et al [235] for more details on such devices. Kumar et al [236] have proposed a DW based synapse using a meandering OOPM nanowire configuration.…”

Section: Superparamagnetic Particle Ensemblesmentioning

confidence: 99%

Magnetic domain walls: types, processes and applications

Venkat,

Allwood,

Hayward

2023

J. Phys. D: Appl. Phys.

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Domain walls (DWs) in magnetic nanowires are promising candidates for a variety of applications including Boolean/unconventional logic, memories, in-memory computing as well as magnetic sensors and biomagnetic implementations. They show rich physical behaviour and are controllable using a number of methods including magnetic fields, charge and spin currents and spin-orbit torques. In this review, we detail types of domain walls in ferromagnetic nanowires and describe processes of manipulating their state. We look at the state of the art of DW applications and give our take on the their current status, technological feasibility and challenges.

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Section: Superparamagnetic Particle Ensemblesmentioning

confidence: 99%

Magnetic domain walls: types, processes and applications

Venkat,

Allwood,

Hayward

2023

J. Phys. D: Appl. Phys.

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“…The domain wall is known to be a kink-like topological soliton [10][11][12][13]. In the heavy-metal-ferromagnetic metal (free layer)-oxide-ferromagnetic metal (fixed layer) device shown in figure 1(a), the ferromagnetic layer (free layer) exhibits perpendicular magnetic anisotropy (PMA), and the domain wall in that layer separates the region with up-polarized magnetic moments and that with down-polarized magnetic moments.…”

Section: Introduction 1motivationmentioning

confidence: 99%

Impact of edge defects on the synaptic characteristic of a ferromagnetic domain-wall device and on on-chip learning

Singh Yadav,

Sadashiva,

Holla

et al. 2023

Neuromorph. Comput. Eng.

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Topological-soliton-based devices, like the ferromagnetic domain-wall device, have been proposed as non-volatile memory (NVM) synapses in electronic crossbar arrays for fast and energy-efficient implementation of on-chip learning of neural networks. High linearity and symmetry in the synaptic weight-update characteristic of the device (long-term potentiation (LTP) and long-term depression (LTD)) are important requirements to obtain high classification/ regression accuracy in such an on-chip learning scheme. However, obtaining such linear and symmetric LTP and LTD characteristics in the ferromagnetic domain-wall device has remained a challenge. Here, we first carry out micromagnetic simulations of the device to show that the incorporation of defects at the edges of the device, with the defects having higher perpendicular magnetic anisotropy (PMA) compared to the rest of the ferromagnetic layer, leads to massive improvement in the linearity and symmetry of the LTP and LTD characteristics of the device. This is because these defects act as pinning centres for the domain wall and prevent it from moving during the delay time between two consecutive programming current pulses, which is not the case when the device doesn't have defects. Next, we carry out system-level simulations of two crossbar arrays with synaptic characteristics of domain-wall synapse devices incorporated in them: one without such defects, and one with such defects. For on-chip learning of both Long Short Term Memory Networks (LSTMs) (using a regression task) and Fully Connected Neural Networks (FCNN) (using a classification task), we show improved performance when the domain-wall synapse devices have defects at the edges. We also estimate the energy consumption in these synaptic devices and project their scaling, with respect to on-chip learning in corresponding crossbar arrays.

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“…Hu et al provide an overarching review of advances in computing systems based on magnetic topological solitons, such as magnetic domain walls and skyrmions, and highlight their importance in logical and neuromorphic computing [5]. The authors carefully describe the innate properties of these solitons-non-volatility, scalability, rich physical interactions, and capacity for nonlinear behavior-and demonstrate their potential use in various concepts aimed at replicating the distinctive features of Unconventional Computing paradigms.…”

mentioning

confidence: 99%

Editorial: Focus issue on topological solitons for neuromorphic systems

Meier,

Íñiguez-González,

Rodrigues

et al. 2024

Neuromorph. Comput. Eng.

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Magnetic skyrmions and domain walls for logical and neuromorphic computing

Cited by 8 publications

References 126 publications

Magnetic domain walls: types, processes and applications

Magnetic domain walls: types, processes and applications

Impact of edge defects on the synaptic characteristic of a ferromagnetic domain-wall device and on on-chip learning

Editorial: Focus issue on topological solitons for neuromorphic systems

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