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 neurons and synapses are, however, currently limited by the energy and area requirements of these components. Spintronic nanodevices, which exploit both the magnetic and electrical properties of electrons, can increase the energy efficiency and decrease the area of these circuits, and magnetic tunnel junctions are of particular interest as neuromorphic computing elements because they are compatible with standard integrated circuits and can support multiple functionalities. Here, we review the development of spintronic devices for neuromorphic computing. We examine how magnetic tunnel junctions can serve as synapses and neurons, and how magnetic textures, such as domain walls and skyrmions, can function as neurons. We also explore spintronics-based implementations of neuromorphic computing tasks, such as pattern recognition in an associative memory, and discuss the challenges that exist in scaling up these systems.
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Work by M.D.S. was supported by the Quantum Materials for Energy Efficient Neuromorphic Computing, an Energy Frontier Research Center funded by DOE, Office of Science, BES under award no. DE-SC0019273. S.F. is funded by JSPS Grant-in-Aid for Scientific Research 19H05622 and JST-OPERA JPMJOP1611. K.E.S. is funded by the German Research Foundation (DFG) under the Project No. EV 196/2-1 and acknowledges support through the Emergent AI Center, funded by the Carl-Zeiss-Stiftung. Work by J.G. was supported by the European Research Council ERC under Grant bioSPINspired 682955. Work by D.Q. was supported by the European Research Council grant NANOINFER (reference: 715872). S.F. acknowledges discussion with H. Ohno. K.E.S. acknowledges discussions with D. Pinna. K.Y.C. acknowledges useful discussions with S. Datta.
The authors declare no competing interests.
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Grollier, J., Querlioz, D., Camsari, K.Y. et al. Neuromorphic spintronics. Nat Electron 3, 360–370 (2020). https://doi.org/10.1038/s41928-019-0360-9
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