World Scientific
Skip main navigation

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×
Our website is made possible by displaying certain online content using javascript.
In order to view the full content, please disable your ad blocker or whitelist our website www.worldscientific.com.

System Upgrade on Mon, Jun 21st, 2021 at 1am (EDT)

During this period, the E-commerce and registration of new users may not be available for up to 6 hours.
For online purchase, please visit us again. Contact us at [email protected] for any enquiries.
Special Section on Spintronics for In-Memory Processing; Guest Editors: Wang Kang, Yue Zhang, Weisheng Zhao (Beihang University, China), Mehdi Tahoori (Karlsruhe Institute of Technology (KIT), Germany) and Joseph S. Friedman (The University of Texas at Dallas, USA)No Access

Three Artificial Spintronic Leaky Integrate-and-Fire Neurons

    Due to their nonvolatility and intrinsic current integration capabilities, spintronic devices that rely on domain wall (DW) motion through a free ferromagnetic track have garnered significant interest in the field of neuromorphic computing. Although a number of such devices have already been proposed, they require the use of external circuitry to implement several important neuronal behaviors. As such, they are likely to result in either a decrease in energy efficiency, an increase in fabrication complexity, or even both. To resolve this issue, we have proposed three individual neurons that are capable of performing these functionalities without the use of any external circuitry. To implement leaking, the first neuron uses a dipolar coupling field, the second uses an anisotropy gradient and the third uses shape variations of the DW track.

    References

    • 1. V. Balasubramanian, Proc. IEEE 103, 1346 (2015). CrossrefGoogle Scholar
    • 2. F. Akopyan et al., IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 34, 1537 (2015). CrossrefGoogle Scholar
    • 3. P. A. Merolla et al., Science 345, 668 (2014). CrossrefGoogle Scholar
    • 4. A. Delorme, J. Gautrais, R. V. Rullen and S. Thorpe, Neurocomputing 26–27, 989 (1999). CrossrefGoogle Scholar
    • 5. S. Han, H. Mao and W. J. Dally, arXiv:1510.00149 1-14. Google Scholar
    • 6. B. Sengupta and M. B. Stemmler, Proc. IEEE 102, 738 (2014). CrossrefGoogle Scholar
    • 7. D. B. Strukov, G. S. Snider, D. R. Stewart and R. S. Williams, Nature 453, 80 (2008). CrossrefGoogle Scholar
    • 8. D. Querlioz, W. S. Zhao, P. Dollfus, J.-O. Klein, O. Bichler and C. Gamrat, Bioinspired networks with nanoscale memristive devices that combine the unsupervised and supervised learning approaches, in Proc. of the 2012 IEEE/ACM Int. Symp. on Nanoscale Architectures (NANOARCH 12), Amsterdam, Netherlands, pp. 203–210 (2012). CrossrefGoogle Scholar
    • 9. X. Chen, W. Kang, D. Zhu, X. Zhang, N. Lei, Y. Zhang, Y. Zhou and W. Zhao, Nanoscale 10, 6139 (2018). CrossrefGoogle Scholar
    • 10. H. Yangqi, W. Kang, X. Zhang, Y. Zhou and W. Zhao, Nanotechnology 28, 08LT02 (2017). CrossrefGoogle Scholar
    • 11. S. Dutta, S. A. Siddiqui, F. Buttner, L. Liu, C. A. Ross and M. A. Baldo, A logic-in-memory design with 3-terminal magnetic tunnel junction function evaluators for convolutional neural networks, in 2017 IEEE/ACM Int. Symp. on Nanoscale Architectures (NANOARCH), Newport, Rhode Island, USA, pp. 83–88 (2017). CrossrefGoogle Scholar
    • 12. O. Akinola, E. J. Kim, N. Hassan, J. S. Friedman and J. A. C. Incorvia, Three-terminal magnetic tunnel junction synapse circuits showing spike-timing-dependent plasticity, J. Phys. D: Appl. Phys. 52, 49LT01 (2019). CrossrefGoogle Scholar
    • 13. Y. V. D. Burgt et al., Nature Mater. 16, 414 (2017). CrossrefGoogle Scholar
    • 14. N. Hassan et al., J. Appl. Phys. 124, 152127 (2018). CrossrefGoogle Scholar
    • 15. W. H. Brigner, X. Hu, N. Hassan, C. H. Bennett, J. A. C. Incorvia, F. Garcia-Sanchez and J. S. Friedman, Semi-supervised learning and inference in domain-wall magnetic tunnel junction (DW-MTJ) neural networks, IEEE J. Exploratory Solid-State Comput. Devices Circuits, 5 19–24, (2019). CrossrefGoogle Scholar
    • 16. W. H. Brigner et al., IEEE Trans. Electron Devices 66, 4970 (2019). CrossrefGoogle Scholar
    • 17. Y.-P. Lin, C. H. Bennett, T. Cabaret, D. Vodenicarevic, D. Chabi, D. Querlioz, B. Jousselme, V. Derycke and J.-O. Klein, Sci. Rep. 6, 31932, (2016). CrossrefGoogle Scholar
    • 18. C. H. Bennett, J. A. C. Incorvia, X. Hu, N. Hassan, J. S. Friedman and M. M. Marinella, Semi-supervised learning and inference in domain-wall magnetic tunnel junction (DW-MTJ) neural networks, in Proc. SPIE Spintronics XII, 2019 (invited). CrossrefGoogle Scholar
    • 19. M. Sharad, D. Fan, K. Aitken and K. Roy, IEEE Trans. Nanotechnol. 13, 110903I (2014). CrossrefGoogle Scholar
    • 20. J. A. Currivan, Y. Jang, M. D. Mascaro, M. A. Baldo and C. A. Ross, IEEE Magn. Lett. 3, 3000104 (2012). CrossrefGoogle Scholar
    • 21. J. A. Currivan-Incorvia, S. Siddiqui, S. Dutta, E. R. Evarts, C. A. Ross and M. A. Baldo, Spintronic logic circuit and device prototypes utilizing domain walls in ferromagnetic wires with tunnel junction readout, in 2015 IEEE Int. Electron Devices Meeting (IEDM), Washington, DC, USA, pp. 32.6.1–32.6.4, 2015. CrossrefGoogle Scholar
    • 22. J. S. Friedman and A. V. Sahakian, IEEE Trans. Electron Devices 61, 1207 (2014). CrossrefGoogle Scholar
    • 23. X. Hu, A. Timm, W. H. Brigner, J. A. C. Incorvia and J. S. Friedman, IEEE Trans. Electron Devices 66, 2817 (2019). CrossrefGoogle Scholar
    • 24. A. Vansteenkiste, J. Leliaert, M. Dvornik, M. Helsen, F. Garcia-Sanchez and B. V. Waeyenberge, AIP Adv. 4, 107133 (2014). CrossrefGoogle Scholar
    • 25. S. Li et al., Nanotechnology 28, 31LT01 (2017). CrossrefGoogle Scholar
    • 26. Y. Sun, C. R. Sullivan, W. Li, D. Kopp, F. Johnson and S. T. Taylor, IEEE Trans. Magn. 43, 4060 (2007). CrossrefGoogle Scholar
    • 27. N. N. Phuoc, L. T. Hung and C. K. Ong, J. Alloys Compd. 509, 4010 (2011). CrossrefGoogle Scholar
    • 28. S. Li, Z. Huang, J.-G. Duh and M. Yamaguchi, Appl. Phys. Lett. 92, 092501 (2008). CrossrefGoogle Scholar
    • 29. C. Ma et al., Nano Letters 19, 353 (2019). CrossrefGoogle Scholar
    • 30. Y. Zhang et al., IEEE Trans. Electron Devices 59, 819 (2012). CrossrefGoogle Scholar
    Published: 25 June 2020