Extremely energy-efficient, magnetic field-free, skyrmion-based memristors for neuromorphic computing

Joy, A and Satheesh, c and Anil Kumar, PS (2023) Extremely energy-efficient, magnetic field-free, skyrmion-based memristors for neuromorphic computing. In: Applied Physics Letters, 123 (21).

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Abstract

The human brain can process information more efficiently than computers due to the dynamics of neurons and synapses. Mimicking such a system can lead to the practical implementation of artificial spiking neural networks. Spintronic devices have been shown to be an ideal solution for realizing the hardware required for neuromorphic computing. Skyrmions prove to be an effective candidate as information carriers owing to their topological protection and particle-like nature. Ferrimagnet and antiferromagnet-based spintronics have been employed previously to obtain an ultrafast simulation of artificial synapses and neurons. Here, we have proposed a ferromagnetic device of stack Ta 3 nm Pt 3 nm Cu 0.65 nm Co 0.5 nm Pt 1 nm that is capable of ultrafast simulation of artificial neurons and synapses, owing to the high velocity of the stabilized skyrmions in the system. Electrical pulses of nanosecond pulse width were used to control the accumulation and dissipation of skyrmions in the system, analogous to the variations in the synaptic weights. Lateral structure inversion asymmetry is used to bring about a field-free switching in the system, leading to an energy-efficient switching process. Magnetic field-free deterministic switching and low pulse width current pulses drastically reduce energy consumption by 106 times compared to the existing ferromagnet-based neuromorphic devices. Artificial neuron, synapse, and memristor functionalities have been reproduced on the same device with characteristic time scales and field-free switching, better than any existing ferromagnet-based neuromorphic devices. The results recognize ferromagnet-based skyrmions as viable candidates for ultrafast neuromorphic spintronics capable of executing cognitive tasks with extremely high efficiency. © 2023 Author(s).

Item Type:	Journal Article
Publication:	Applied Physics Letters
Publisher:	American Institute of Physics Inc.
Additional Information:	The copyright for this article belongs to American Institute of Physics Inc.
Keywords:	Energy efficiency; Energy utilization; Ferromagnetic materials; Ferromagnetism; Magnetic fields; Neural networks; Neurons; Spintronics; Superconducting materials, Artificial neurons; Artificial synapse; Energy efficient; Ferromagnets; Magnetic-field; Memristor; Neuromorphic; Neuromorphic computing; Skyrmions; Ultra-fast simulations, Memristors
Department/Centre:	Division of Physical & Mathematical Sciences > Physics
Date Deposited:	29 Feb 2024 06:07
Last Modified:	29 Feb 2024 06:07
URI:	https://eprints.iisc.ac.in/id/eprint/83737

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