2D Spintronics for Neuromorphic Computing with Scalability and Energy Efficiency

The demand for computing power has been growing exponentially with the rise of artificial intelligence (AI), machine learning, and the Internet of Things (IoT). This growth requires unconventional computing primitives that prioritize energy efficiency, while also addressing the critical need for sca...

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Published inJournal of low power electronics and applications Vol. 15; no. 2; p. 16
Main Authors Plummer, Douglas Z., D’Alessandro, Emily, Burrowes, Aidan, Fleischer, Joshua, Heard, Alexander M., Wu, Yingying
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2025
Subjects
2D spintronics
Artificial intelligence
Atoms
Electrons
Energy consumption
Energy efficiency
Energy management systems
Graphene
Heterostructures
Hypothetical particles
Internet of Things
Machine learning
Neural networks
Neuromorphic computing
neuromorphic hardware
nonvolatile devices
Particle theory
Pattern recognition
Retention
Spintronics
Tunnel junctions
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ISSN2079-9268
2079-9268
DOI10.3390/jlpea15020016

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Summary:The demand for computing power has been growing exponentially with the rise of artificial intelligence (AI), machine learning, and the Internet of Things (IoT). This growth requires unconventional computing primitives that prioritize energy efficiency, while also addressing the critical need for scalability. Neuromorphic computing, inspired by the biological brain, offers a transformative paradigm for addressing these challenges. This review paper provides an overview of advancements in 2D spintronics and device architectures designed for neuromorphic applications, with a focus on techniques such as spin-orbit torque, magnetic tunnel junctions, and skyrmions. Emerging van der Waals materials like CrI3, Fe3GaTe2, and graphene-based heterostructures have demonstrated unparalleled potential for integrating memory and logic at the atomic scale. This work highlights technologies with ultra-low energy consumption (0.14 fJ/operation), high switching speeds (sub-nanosecond), and scalability to sub-20 nm footprints. It covers key material innovations and the role of spintronic effects in enabling compact, energy-efficient neuromorphic systems, providing a foundation for advancing scalable, next-generation computing architectures.
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ISSN:2079-9268
2079-9268
DOI:10.3390/jlpea15020016