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Since the experimental discovery of magnetic skyrmions achieved one decade ago, there have been significant efforts to bring the virtual particles into all-electrical fully functional devices, inspired by their fascinating physical and…

Magnetic skyrmions have attracted considerable interest, especially after their recent experimental demonstration at room temperature in multilayers. The robustness, nanoscale size and non-volatility of skyrmions have triggered a…

We present a design-scheme for ultra-low power neuromorphic hardware using emerging spin-devices. We propose device models for 'neuron', based on lateral spin valves and domain wall magnets that can operate at ultra-low terminal voltage of…

Disordered Systems and Neural Networks · Physics 2012-07-19 Mrigank Sharad , Charles Augustine , Georgios Panagopoulos , Kaushik Roy

Neuromorphic computing promises revolutionary improvements over conventional systems for applications that process unstructured information. To fully realize this potential, neuromorphic systems should exploit the biomimetic behavior of…

Neuromorphic computing uses brain-inspired principles to design circuits that can perform computational tasks with superior power efficiency to conventional computers. Approaches that use traditional electronic devices to create artificial…

Applied Physics · Physics 2020-07-14 J. Grollier , D. Querlioz , K. Y. Camsari , K. Everschor-Sitte , S. Fukami , M. D. Stiles

Solitonic magnetic excitations such as domain walls and, specifically, skyrmionics enable the possibility of compact, high density, ultrafast,all-electronic, low-energy devices, which is the basis for the emerging area of skyrmionics. The…

Spintronic-based brain-inspired neuromorphic computing has recently attracted significant attention due to the exceptional properties of magnetic microstructures, including nanoscale dimensions, high stability, and low energy consumption.…

Computational Physics · Physics 2025-12-05 Anmol Sharma , Ranjeet Kumar Brajpuriya , Vivek K. Malik , Vishakha Kaushik , Sachin Pathak

Synthetic ferrimagnetic (SFiM) multilayers offer a versatile platform for hosting skyrmions with tunable magnetic properties, combining the advantages of ferromagnets and antiferromagnets. Unlike synthetic antiferromagnets, SFiMs retain a…

Neuromorphic computing based on spiking neural networks has the potential to significantly improve on-line learning capabilities and energy efficiency of artificial intelligence, specially for edge computing. Recent progress in…

Applied Physics · Physics 2021-11-04 Yann Beilliard , Fabien Alibart

Spintronics, a transformative field of research, leverages the spin of electron to revolutionize electronic devices, offering significant advantages over traditional charge-based systems. This chapter highlights the critical role of novel…

Materials Science · Physics 2025-02-03 A. Sud , A. Kumar , M. Cubukcu

The ever-increasing amount of data from ubiquitous smart devices fosters data-centric and cognitive algorithms. Traditional digital computer systems have separate logic and memory units, resulting in a huge delay and energy cost for…

Applied Physics · Physics 2025-03-17 Qiming Shao , Zhongrui Wang , Yan Zhou , Shunsuke Fukami , Damien Querlioz , Leon O. Chua

Ferromagnets are key materials for sensing and memory applications. In contrast, antiferromagnets that represent the more common form of magnetically ordered materials, have so far found less practical application beyond their use for…

Mesoscale and Nanoscale Physics · Physics 2017-05-31 J. Železný , P. Wadley , K. Olejník. A. Hoffmann , H. Ohno

The human brain achieves exceptional energy efficiency by co-locating memory and processing, yet reproducing this principle in hardware remains challenging because many neuromorphic devices require standby power, offer limited…

Magnetic domain wall motion has recently garnered significant interest as a physical mechanism to enable energy-efficient, next-generation brain-inspired computing architectures. However, realizing all behaviors required for neuromorphic…

Materials Science · Physics 2025-08-21 Jeffrey A. Brock , Aleksandr Kurenkov , Aleš Hrabec , Laura J. Heyderman

Antiferromagnets have recently emerged as attractive platforms for spintronics applications, offering fundamentally new functionalities compared to their ferromagnetic counterparts. While nanoscale thin film materials are key to the…

Mesoscale and Nanoscale Physics · Physics 2019-02-19 Patrick Appel , Brendan J. Shields , Tobias Kosub , René Hübner , Jürgen Faßbender , Denys Makarov , Patrick Maletinsky

Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking…

Emerging Technologies · Computer Science 2016-11-15 Abhronil Sengupta , Yong Shim , Kaushik Roy

Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could…

We propose a spintronics-based hardware implementation of neuromorphic computing, specifically, the spiking neural network, using topological winding textures in one-dimensional antiferromagnets. The consistency of such a network is…

Mesoscale and Nanoscale Physics · Physics 2020-11-18 Shu Zhang , Yaroslav Tserkovnyak

During the past ten years nanostructures have been subject of active research. Fabrication of such systems follows well developed methods. The increase in the number of materials available for research and applications requires that the…

Materials Science · Physics 2007-05-23 G. V. Kurlyandskaya , S. M. Bhagat

Spin textures such as magnetic domain walls and skyrmions have the potential to revolutionize electronic devices by encoding information bits. Although recent advancements in ferromagnetic films have led to promising device prototypes,…

Materials Science · Physics 2023-07-18 Kang Wang , Vineetha Bheemarasetty , Gang Xiao
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