Filament formation in lithium niobate memristors supports neuromorphic programming capability

  • Chris Yakopcic
  • Shu Wang
  • Weisong Wang
  • Eunsung Shin
  • John Boeckl
  • Guru Subramanyam
  • Tarek M. Taha
Original Article
  • 84 Downloads

Abstract

Memristor crossbars are capable of implementing learning algorithms in a much more energy and area efficient manner compared to traditional systems. However, the programmable nature of memristor crossbars must first be explored on a smaller scale to see which memristor device structures are most suitable for applications in reconfigurable computing. In this paper, we demonstrate the programmability of memristor devices with filamentary switching based on LiNbO3, a new resistive switching oxide. We show that a range of resistance values can be set within these memristor devices using a pulse train for programming. We also show that a neuromorphic crossbar containing eight memristors was capable of correctly implementing an OR function. This work demonstrates that lithium niobate memristors are strong candidates for use in neuromorphic computing.

Keywords

Memristor Neuromorphic Crossbar Circuit 

References

  1. 1.
    Snider GS (2008) Cortical computing with memristive nanodevices, SciDAC reviewGoogle Scholar
  2. 2.
    Jo SH, Kim K-H, Lu W (2009) High-density crossbar arrays based on a–Si memristive system. Nano Lett 9(2):870–874CrossRefGoogle Scholar
  3. 3.
    Yakopcic C, Hasan R, Taha TM (2015) Memristor based neuromorphic circuit for ex-situ training of multi-layer neural network algorithms, IEEE IJCNN, 2015Google Scholar
  4. 4.
    Taha TM, Hasan R, Yakopcic C (2014) Memristor crossbar based multicore neuromorphic processors, IEEE International SOCC, 2014Google Scholar
  5. 5.
    Yakopcic C, Taha TM (2015) Determining optimal switching speed for memristors in a neuromorphic system. Electron Lett 51(21):1637–1639CrossRefGoogle Scholar
  6. 6.
    Alibart F, Zamanidoost E, Strukov DB (2013) Pattern classification by memristive crossbar circuits with ex-situ and in-situ training, Nat Comm, vol 4, June 2013Google Scholar
  7. 7.
    Yakopcic C, Taha TM, Hasan R (2014) Hybrid crossbar architecture for a memristor based memory, IEEE National Aerospace and Electronics Conference (NAECON), pp 237–242, 2014Google Scholar
  8. 8.
    Yakopcic C, Hasan R, Taha TM (2015) Hybrid crossbar architecture for a memristor based cache. Microelectron J 46(11):1020–1032CrossRefGoogle Scholar
  9. 9.
    Wang S, Wang W, Yakopcic C, Shin E, Subramanyam G, Taha TM (2015) Lithium based memristive device, IEEE National Aerospace and Electronics Conference, 2015Google Scholar
  10. 10.
    Li H, Xia Y, Xu B, Guo H, Yin J, Liu Z (2010) Memristive behaviors of LiNbO3 ferroelectric diodes. Appl Phys Lett 97:012902CrossRefGoogle Scholar
  11. 11.
    Pan X, Shuai Y, Wu C, Luo W, Sun X, Zeng H, Zhou S, Bottger R, Ou X, Mikolajick T, Zhang W, Schmidt H (2016) Rectifying filamentary resistive switching in ion-exfoliated LiNbO3 thin films. Appl Phys Lett 108:032904CrossRefGoogle Scholar
  12. 12.
    Liu X, Biju KP, Lee J, Park J, Kim S, Park S, Shin J, Sadaf SMd, Hwang H (2011) Parallel memristive filaments model applicable to bipolar and filamentary resistive switching. Appl Phys Lett 99:113518CrossRefGoogle Scholar
  13. 13.
    Kozicki MN, Balakrishnan M, Gopalan C, Ratnakumar C, Mitkova M (2005) Programmable metallization cell memory based on Ag–Ge–S and Cu–Ge–S solid electrolytes, In: Proceedings of Non-Volatile Memory Technology Symposium, pp 83–89Google Scholar
  14. 14.
    Lu W, Kim K-H, Chang T, Gaba S (2011) Two-terminal resistive switches (memristors) for memory and logic applications, In: Proceedings of the 16th Asia and South Pacific Design Automation Conference, pp 217–223Google Scholar
  15. 15.
    Miao F, Strachan JP, Yang JJ, Zhang M-X, Goldfarb I, Torrezan AC, Eschbach P, Kelley RD, Medeiros-Ribeiro G, Williams RS (2011) Anatomy of a nanoscale conduction channel reveals the mechanism of a high-performance memristor. Adv Mater 23(47):5633–5640CrossRefGoogle Scholar
  16. 16.
    Brivio S, Covi E, Serb A, Prodromakis T, Fanciulli M, Spiga S (2016) Experimental study of gradual/abrupt dynamics of HfO2-based memristive devices. Appl Phys Lett 109:133504. doi:10.1063/1.4963675 CrossRefGoogle Scholar
  17. 17.
    Zhang JJ, Sun HJ, Li Y, Wang Q, Xu XH, Miao XS (2016) AgInSbTe memristor with gradual resistance tuning. Appl Phys Lett 102:183513. doi:10.1063/1.4804983 CrossRefGoogle Scholar
  18. 18.
    Yakopcic C, Taha TM, Subramanyam G, Pino RE (2015) Impact of memristor switching noise in a neuromorphic crossbar IEEE National Aerospace and Electronics Conference, June 2015Google Scholar
  19. 19.
    Yakopcic C, Taha TM, Subramanyam G, Pino RE (2013) Generalized memristive device SPICE model and its application in circuit design. IEEE Trans Comput Aided Des Integr Circuits Syst 32(8):1201–1214CrossRefGoogle Scholar
  20. 20.
    Yakopcic C, Taha TM, Subramanyam G, Pino RE (2013) Memristor SPICE model and crossbar simulation with nanosecond switching time, IEEE International Joint Conference on Neural Networks (IJCNN), August 2013Google Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Chris Yakopcic
    • 1
  • Shu Wang
    • 1
  • Weisong Wang
    • 1
  • Eunsung Shin
    • 1
  • John Boeckl
    • 2
  • Guru Subramanyam
    • 1
  • Tarek M. Taha
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of DaytonDaytonUSA
  2. 2.Air Force Research LaboratoryWright Patterson AFBDaytonUSA

Personalised recommendations