Abstract
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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Big data needs a hardware revolution. Nature 554, 145–146 (2018).
Furber, S. Large-scale neuromorphic computing systems. J. Neural Eng. 13, 051001 (2016).
Indiveri, G. et al. Neuromorphic silicon neuron circuits. Neuromorphic Eng. 5, 73 (2011).
Locatelli, N., Cros, V. & Grollier, J. Spin-torque building blocks. Nat. Mater. 13, 11–20 (2014).
Grollier, J., Querlioz, D. & Stiles, M. D. Spintronic nanodevices for bioinspired computing. Proc. IEEE 104, 2024–2039 (2016).
Schuman, C. D. et al. A survey of neuromorphic computing and neural networks in hardware. Preprint at https://arxiv.org/abs/1705.06963 (2017).
Chung, S. W. et al. 4Gbit density STT-MRAM using perpendicular MTJ realized with compact cell structure. In 2016 IEEE Int. Electron Devices Meeting (IEDM) 27.1.1–27.1.4 (IEEE, 2016).
Jarollahi, H. et al. A nonvolatile associative memory-based context-driven search engine using 90 nm CMOS/MTJ-hybrid logic-in-memory architecture. IEEE J. Emerg. Sel. Top. Circuits Syst. 4, 460–474 (2014).
Ma, Y. et al. A 600-μW ultra-low-power associative processor for image pattern recognition employing magnetic tunnel junction-based nonvolatile memories with autonomic intelligent power-gating scheme. Jpn. J. Appl. Phys. 55, 04EF15 (2016).
Zhou, P., Zhao, B., Yang, J. & Zhang, Y. Energy reduction for STT-RAM using early write termination. In 2009 IEEE/ACM Int. Conference on Computer-Aided Design - Digest of Technical Papers 264–268 (IEEE, 2009).
Faisal, A. A., Selen, L. P. J. & Wolpert, D. M. Noise in the nervous system. Nat. Rev. Neurosci. 9, 292–303 (2008).
Bottou, L. & Bousquet, O. The tradeoffs of large scale learning. In Proc. 20th Int. Conference on Neural Information Processing Systems 161–168 (Curran Associates, 2007).
Locatelli, N., Vincent, A. F. & Querlioz, D. Use of magnetoresistive random-access memory as approximate memory for training neural networks. In 25th IEEE Int. Conference on Electronics, Circuits and Systems (ICECS) 553–556 (IEEE, 2018).
Senn, W. & Fusi, S. Convergence of stochastic learning in perceptrons with binary synapses. Phys. Rev. E 71, 061907 (2005).
Bill, J. & Legenstein, R. A compound memristive synapse model for statistical learning through STDP in spiking neural networks. Neuromorphic Eng. 8, 412 (2014).
Vincent, A. F. et al. Spin-transfer torque magnetic memory as a stochastic memristive synapse for neuromorphic systems. IEEE Trans. Biomed. Circuits Syst. 9, 166–174 (2015).
Widrow, B. An Adaptive ‘Adaline’ Neuron Using Chemical ‘Memistors’ (Stanford Electronics Laboratories, 1960).
Chua, L. Memristor-the missing circuit element. IEEE Trans. Circuit Theory 18, 507–519 (1971).
Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80–83 (2008).
Yang, J. J., Strukov, D. B. & Stewart, D. R. Memristive devices for computing. Nat. Nanotechnol. 8, 13–24 (2013).
Likharev, K. K. CrossNets: neuromorphic hybrid CMOS/nanoelectronic networks. Sci. Adv. Mater. 3, 322–331 (2011).
Sharad, M., Augustine, C., Panagopoulos, G. & Roy, K. Spin-based neuron model with domain-wall magnets as synapse. IEEE Trans. Nanotechnol. 11, 843–853 (2012).
Wang, X., Chen, Y., Xi, H., Li, H. & Dimitrov, D. Spintronic memristor through spin-torque-induced magnetization motion. IEEE Electron Device Lett. 30, 294–297 (2009).
Yamaguchi, A. et al. Real-space observation of current-driven domain wall motion in submicron magnetic wires. Phys. Rev. Lett. 92, 077205 (2004).
Grollier, J. et al. Switching a spin valve back and forth by current-induced domain wall motion. Appl. Phys. Lett. 83, 509–511 (2003).
Chanthbouala, A. et al. Vertical-current-induced domain-wall motion in MgO-based magnetic tunnel junctions with low current densities. Nat. Phys. 7, 626–630 (2011).
Lequeux, S. et al. A magnetic synapse: multilevel spin-torque memristor with perpendicular anisotropy. Sci. Rep. 6, 31510 (2016).
Huang, Y., Kang, W., Zhang, X., Zhou, Y. & Zhao, W. Magnetic skyrmion-based synaptic devices. Nanotechnology 28, 08LT02 (2017).
Wadley, P. et al. Electrical switching of an antiferromagnet. Science 351, 587–590 (2016).
Grzybowski, M. J. et al. Imaging current-induced switching of antiferromagnetic domains in CuMnAs. Phys. Rev. Lett. 118, 057701 (2017).
Miron, I. M. et al. Perpendicular switching of a single ferromagnetic layer induced by in-plane current injection. Nature 476, 189–193 (2011).
Liu, L. et al. Spin-torque switching with the giant spin Hall effect of tantalum. Science 336, 555–558 (2012).
Fukami, S., Anekawa, T., Zhang, C. & Ohno, H. A spin–orbit torque switching scheme with collinear magnetic easy axis and current configuration. Nat. Nanotechnol. 11, 621–625 (2016).
Fukami, S., Zhang, C., DuttaGupta, S., Kurenkov, A. & Ohno, H. Magnetization switching by spin-orbit torque in an antiferromagnet-ferromagnet bilayer system. Nat. Mater. 15, 535–541 (2016).
Kurenkov, A., Zhang, C., DuttaGupta, S., Fukami, S. & Ohno, H. Device-size dependence of field-free spin-orbit torque induced magnetization switching in antiferromagnet/ferromagnet structures. Appl. Phys. Lett. 110, 092410 (2017).
Hoppensteadt, F. C. & Izhikevich, E. M. Oscillatory neurocomputers with dynamic connectivity. Phys. Rev. Lett. 82, 2983–2986 (1999).
Engel, A. K., Fries, P. & Singer, W. Dynamic predictions: oscillations and synchrony in top–down processing. Nat. Rev. Neurosci. 2, 704–716 (2001).
Buzsaki, G. Rhythms of the Brain (Oxford Univ. Press, 2011).
Gerstner, W., Kistler, W. M., Naud, R. & Paninski, L. Neuronal Dynamics (Cambridge Univ. Press, 2014).
Kiselev, S. I. et al. Microwave oscillations of a nanomagnet driven by a spin-polarized current. Nature 425, 380–383 (2003).
Rippard, W. H., Pufall, M. R., Kaka, S., Russek, S. E. & Silva, T. J. Direct-current induced dynamics in Co90Fe10/Ni80Fe20 point contacts. Phys. Rev. Lett. 92, 027201 (2004).
Sengupta, A., Panda, P., Wijesinghe, P., Kim, Y. & Roy, K. Magnetic tunnel junction mimics stochastic cortical spiking neurons. Sci. Rep. 6, 30039 (2016).
Tsunegi, S. et al. Evaluation of memory capacity of spin torque oscillator for recurrent neural networks. Jpn. J. Appl. Phys. 57, 120307 (2018).
Slavin, A. & Tiberkevich, V. Nonlinear auto-oscillator theory of microwave generation by spin-polarized current. IEEE Trans. Magn. 45, 1875–1918 (2009).
Kaka, S. et al. Mutual phase-locking of microwave spin torque nano-oscillators. Nature 437, 389–392 (2005).
Mancoff, F. B., Rizzo, N. D., Engel, B. N. & Tehrani, S. Phase-locking in double-point-contact spin-transfer devices. Nature 437, 393–395 (2005).
Houshang, A. et al. Spin-wave-beam driven synchronization of nanocontact spin-torque oscillators. Nat. Nanotechnol. 11, 280–286 (2016).
Locatelli, N. et al. Efficient synchronization of dipolarly coupled vortex-based spin transfer nano-oscillators. Sci. Rep. 5, 17039 (2015).
Awad, A. A. et al. Long-range mutual synchronization of spin Hall nano-oscillators. Nat. Phys. 13, 292–299 (2017).
Lebrun, R. et al. Mutual synchronization of spin torque nano-oscillators through a long-range and tunable electrical coupling scheme. Nat. Commun. 8, 15825 (2017).
Pufall, M. R. et al. Physical implementation of coherently coupled oscillator networks. IEEE J. Explor. Solid-State Comput. Devices Circuits 1, 76–84 (2015).
Yogendra, K., Fan, D., Jung, B. & Roy, K. Magnetic pattern recognition using injection-locked spin-torque nano-oscillators. IEEE Trans. Electron Devices 63, 1674–1680 (2016).
Fell, J. & Axmacher, N. The role of phase synchronization in memory processes. Nat. Rev. Neurosci. 12, 105–118 (2011).
Torrejon, J. et al. Neuromorphic computing with nanoscale spintronic oscillators. Nature 547, 428–431 (2017).
Riou, M. et al. Neuromorphic computing through time-multiplexing with a spin-torque nano-oscillator. In 2017 IEEE Int. Electron Devices Meeting (IEDM) 36.3.1–36.3.4 (IEEE, 2017).
Appeltant, L. et al. Information processing using a single dynamical node as complex system. Nat. Commun. 2, 468 (2011).
Liberman, M. et al. TI-46 Word LDC93S9 (Linguistic Data Consortium, 1993); https://catalog.ldc.upenn.edu/LDC93S9
Stein, R. B., Gossen, E. R. & Jones, K. E. Neuronal variability: noise or part of the signal? Nat. Rev. Neurosci. 6, 389–397 (2005).
Conrad, M., Engl, E. & Jolivet, R. B. Energy use constrains brain information processing. In 2017 IEEE Int. Electron Devices Meeting (IEDM) 11.3.1–11.3.3 (IEEE, 2017).
Pouget, A., Dayan, P. & Zemel, R. Information processing with population codes. Nat. Rev. Neurosci. 1, 125–132 (2000).
Vodenicarevic, D. et al. Low-energy truly random number generation with superparamagnetic tunnel junctions for unconventional computing. Phys. Rev. Appl. 8, 054045 (2017).
Locatelli, N. et al. Noise-enhanced synchronization of stochastic magnetic oscillators. Phys. Rev. Appl. 2, 034009 (2014).
Mizrahi, A. et al. Controlling the phase locking of stochastic magnetic bits for ultra-low power computation. Sci. Rep. 6, 30535 (2016).
Camsari, K. Y., Faria, R., Sutton, B. M. & Datta, S. Stochastic p-bits for invertible logic. Phys. Rev. X 7, 031014 (2017).
Camsari, K. Y., Salahuddin, S. & Datta, S. Implementing p-bits with embedded MTJ. IEEE Electron Device Lett. 38, 1767–1770 (2017).
Pufall, M. R. et al. Large-angle, gigahertz-rate random telegraph switching induced by spin-momentum transfer. Phys. Rev. B 69, 214409 (2004).
Fábián, A. et al. Current-induced two-level fluctuations in pseudo-spin-valve (Co/Cu/Co) nanostructures. Phys. Rev. Lett. 91, 257209 (2003).
Parks, B. et al. Superparamagnetic perpendicular magnetic tunnel junctions for true random number generators. AIP Adv. 8, 055903 (2017).
Yamanouchi, M., Chiba, D., Matsukura, F. & Ohno, H. Current-induced domain-wall switching in a ferromagnetic semiconductor structure. Nature 428, 539–542 (2004).
Thomas, L., Moriya, R., Rettner, C. & Parkin, S. S. P. Dynamics of magnetic domain walls under their own inertia. Science 330, 1810–1813 (2010).
Woo, S. et al. Observation of room-temperature magnetic skyrmions and their current-driven dynamics in ultrathin metallic ferromagnets. Nat. Mater. 15, 501–506 (2016).
Allwood, D. A. et al. Magnetic domain-wall logic. Science 309, 1688–1692 (2005).
Fernández-Pacheco, A. et al. Three-dimensional nanomagnetism. Nat. Commun. 8, 15756 (2017).
Hayashi, M. et al. Dependence of current and field driven depinning of domain walls on their structure and chirality in permalloy nanowires. Phys. Rev. Lett. 97, 207205 (2006).
Hayward, T. J. Intrinsic nature of stochastic domain wall pinning phenomena in magnetic nanowire devices. Sci. Rep. 5, 13279 (2015).
Zázvorka, J. et al. Thermal skyrmion diffusion used in a reshuffler device. Nat. Nanotechnol. 14, 658–661 (2019).
Pinna, D. et al. Skyrmion gas manipulation for probabilistic computing. Phys. Rev. Appl. 9, 064018 (2018).
Li, S. et al. Magnetic skyrmion-based artificial neuron device. Nanotechnology 28, 31LT01 (2017).
Chen, X. et al. A compact skyrmionic leaky–integrate–fire spiking neuron device. Nanoscale 10, 6139–6146 (2018).
Du, H. et al. Electrical probing of field-driven cascading quantized transitions of skyrmion cluster states in MnSi nanowires. Nat. Commun. 6, 7637 (2015).
Prychynenko, D. et al. Magnetic skyrmion as a nonlinear resistive element: a potential building block for reservoir computing. Phys. Rev. Appl. 9, 014034 (2018).
Bourianoff, G., Pinna, D., Sitte, M. & Everschor-Sitte, K. Potential implementation of reservoir computing models based on magnetic skyrmions. AIP Adv. 8, 055602 (2018).
Pinna, D., Bourianoff, G. & Everschor-Sitte, K. Reservoir computing with random skyrmion textures. Preprint at https://arxiv.org/abs/1811.12623 (2018).
Hanneken, C. et al. Electrical detection of magnetic skyrmions by tunnelling non-collinear magnetoresistance. Nat. Nanotechnol. 10, 1039–1042 (2015).
Kubetzka, A., Hanneken, C., Wiesendanger, R. & von Bergmann, K. Impact of the skyrmion spin texture on magnetoresistance. Phys. Rev. B 95, 104433 (2017).
Krüger, B. Current-Driven Magnetization Dynamics: Analytical Modeling and Numerical Simulation. PhD thesis, University of Hamburg (2011).
Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61–64 (2015).
Burr, G. W. et al. Large-scale neural networks implemented with non-volatile memory as the synaptic weight element: comparative performance analysis (accuracy, speed, and power). In 2015 IEEE Int. Electron Devices Meeting (IEDM) 4.4.1-4.4.4 (2015).
Borders, WilliamA. et al. Analogue spin–orbit torque device for artificial-neural-network-based associative memory operation. Appl. Phys. Express 10, 013007 (2016).
Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 79, 2554–2558 (1982).
Ackley, D. H., Hinton, G. E. & Sejnowski, T. J. A learning algorithm for Boltzmann machines. Cogn. Sci. 9, 147–169 (1985).
Romera, M. et al. Vowel recognition with four coupled spin-torque nano-oscillators. Nature 563, 230–234 (2018).
Vodenicarevic, D., Locatelli, N., Araujo, F. A., Grollier, J. & Querlioz, D. A nanotechnology-ready computing scheme based on a weakly coupled oscillator network. Sci. Rep. 7, 44772 (2017).
Vodenicarevic, D., Locatelli, N., Grollier, J. & Querlioz, D. Nano-oscillator-based classification with a machine learning-compatible architecture. J. Appl. Phys. 124, 152117 (2018).
Vogel, M. et al. Phase programming in coupled spintronic oscillators. Preprint at https://arxiv.org/abs/1811.02154 (2018).
Mizrahi, A. et al. Neural-like computing with populations of superparamagnetic basis functions. Nat. Commun. 9, 1533 (2018).
Mizrahi, A., Grollier, J., Querlioz, D. & Stiles, M. D. Overcoming device unreliability with continuous learning in a population coding based computing system. J. Appl. Phys. 124, 152111 (2018).
Sutton, B., Camsari, K. Y., Behin-Aein, B. & Datta, S. Intrinsic optimization using stochastic nanomagnets. Sci. Rep. 7, 44370 (2017).
Behin-Aein, B., Diep, V. & Datta, S. A building block for hardware belief networks. Sci. Rep. 6, 29893 (2016).
Behin-Aein, B. Computing multi-magnet based devices and methods for solution of optimization problems. (2014).
Zand, R. et al. Low-energy deep belief networks using intrinsic sigmoidal spintronic-based probabilistic neurons. In Proc. 2018 Great Lakes Symposium on VLSI 15–20 (ACM, 2018).
Farhi, E. et al. A quantum adiabatic evolution algorithm applied to random instances of an NP-complete problem. Science 292, 472–475 (2001).
Peng, X. et al. Quantum adiabatic algorithm for factorization and its experimental implementation. Phys. Rev. Lett. 101, 220405 (2008).
Faria, R., Camsari, K. Y. & Datta, S. Low-barrier nanomagnets as p-bits for spin logic. IEEE Magn. Lett. 8, 1–5 (2017).
Camsari, K. Y., Chowdhury, S. & Datta, S. Scalable emulation of sign-problem—free Hamiltonians with room-temperature p-bits. Phys. Rev. Appl. 12, 034061 (2019).
Johnson, M. W. et al. Quantum annealing with manufactured spins. Nature 473, 194–198 (2011).
Troyer, M. & Wiese, U.-J. Computational complexity and fundamental limitations to fermionic quantum monte carlo simulations. Phys. Rev. Lett. 94, 170201 (2005).
Bhanja, S., Karunaratne, D. K., Panchumarthy, R., Rajaram, S. & Sarkar, S. Non-Boolean computing with nanomagnets for computer vision applications. Nat. Nanotechnol. 11, 177–183 (2016).
Debashis, P. et al. Experimental demonstration of nanomagnet networks as hardware for Ising computing. In 2016 IEEE Int. Electron Devices Meeting (IEDM) 34.3.1–34.3.4 (IEEE, 2016).
Nomura, H. et al. Reservoir computing with dipole-coupled nanomagnets. Jpn. J. Appl. Phys. 58, 070901 (2019).
Jensen, J. H., Folven, E. & Tufte, G. Computation in artificial spin ice. In ALIFE 2018: The 2018 Conference on Artificial Life https://doi.org/10.1162/isal_a_00011 (MIT Press, 2018).
Xu, X. et al. Scaling for edge inference of deep neural networks. Nat. Electron. 1, 216–222 (2018).
Khvalkovskiy, A. V. et al. Basic principles of STT-MRAM cell operation in memory arrays. J. Phys. Appl. Phys. 46, 074001 (2013).
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Ambrogio, S. et al. Equivalent-accuracy accelerated neural-network training using analogue memory. Nature 558, 60–67 (2018).
Nagaosa, N. & Tokura, Y. Topological properties and dynamics of magnetic skyrmions. Nat. Nanotechnol. 8, 899–911 (2013).
Wu, S., Li, G., Chen, F. & Shi, L. Training and inference with integers in deep neural networks. Preprint at https://arxiv.org/abs/1802.04680 (2018).
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R. & Bengio, Y. Quantized neural networks: training neural networks with low precision weights and activations. J. Mach. Learn. Res. 18, 1 (2017).
Rastegari, M., Ordonez, V., Redmon, J. & Farhadi, A. Xnor-net: imagenet classification using binary convolutional neural networks. In European Conf. on Computer Vision 525–542 (Springer, 2016).
Mellnik, A. R. et al. Spin-transfer torque generated by a topological insulator. Nature 511, 449–451 (2014).
Chakravarty, A. et al. Supervised learning of an opto-magnetic neural network with ultrashort laser pulses. Appl. Phys. Lett. 114, 192407 (2018).
Davies, C. S. et al. Towards massively parallelized all-optical magnetic recording. J. Appl. Phys. 123, 213904 (2018).
Khymyn, R. et al. Ultra-fast artificial neuron: generation of picosecond-duration spikes in a current-driven antiferromagnetic auto-oscillator. Sci. Rep. 8, 15727 (2018).
Sulymenko, O. et al. Ultra-fast logic devices using artificial “neurons” based on antiferromagnetic pulse generators. J. Appl. Phys. 124, 152115 (2018).
Sato, H. et al. Properties of magnetic tunnel junctions with a MgO/CoFeB/Ta/CoFeB/MgO recording structure down to junction diameter of 11 nm. Appl. Phys. Lett. 105, 062403 (2014).
Piraux, L. et al. Giant magnetoresistance in magnetic multilayered nanowires. Appl. Phys. Lett. 65, 2484–2486 (1994).
Acknowledgements
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.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41928-019-0360-9
This article is cited by
-
Spatially reconfigurable antiferromagnetic states in topologically rich free-standing nanomembranes
Nature Materials (2024)
-
Magnetic whirlpools offer improved data storage
Nature (2024)
-
Nonlinear dynamics of directly coupled skyrmions in ferrimagnetic spin torque nano-oscillators
npj Computational Materials (2024)
-
Current-induced switching of a van der Waals ferromagnet at room temperature
Nature Communications (2024)
-
All-electrical skyrmionic magnetic tunnel junction
Nature (2024)