Mimicking Synaptic Behaviors with Cross-Point Structured TiOx/TiOy-Based Filamentary RRAM for Neuromorphic Applications

  • Jongtae Kim
  • Sanghoon Cho
  • Taeheon Kim
  • James Jungho PakEmail author
Original Article


This paper presents the fabrication and characterization of the cross-point structure 20 × 20 μm2 RRAM with TiOx/TiOy bi-layer insulator for synaptic application in neuromorphic systems. The measured oxygen concentration of the TiOx/TiOy switching layers of the fabricated devices using X-ray photoelectron spectroscopy analysis showed that the oxygen concentration ratio between TiOx and TiOy is ~ 1.5. After electroforming at ~ 5.62 V, the on/off ratio was ~ 76 at 0.2 V with the DC sweep voltage scheme. Synaptic behaviors including long-term potentiation (LTP) and long-term depression (LTD) were performed with 50 identical pulses for the implementation of RRAM into neuromorphic systems based on convolutional neural networks. Also, linearly increased (or decreased) 25 pulses were applied to the device so that the conductance changes linearly. The resulting linear LTP and LTD characteristics were mirror-symmetric, which could maximize the accuracy. For Hebbian learning, the device also mimicked the spike-timing-dependent plasticity properties with a conductance change from − 77.79% to 96.07% using a time-division multiplexing approach.


Cross-point Neuromorphic Synaptic application RRAM TiOx/TiOy bi-layer 



  1. 1.
    Burr GW et al (2017) Neuromorphic computing using non-volatile memory. Adv Phys X 2(1):89–124MathSciNetGoogle Scholar
  2. 2.
    Rajendran B, Alibart F (2016) Neuromorphic computing based on emerging memory technologies. IEEE J Emerg Sel Top Circuits Syst 6(2):198–211CrossRefGoogle Scholar
  3. 3.
    Abbott LF, Nelson SB (2000) Synaptic plasticity: taming the beast. Nat Neurosci 3(11s):1178–1183CrossRefGoogle Scholar
  4. 4.
    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(13):133504CrossRefGoogle Scholar
  5. 5.
    Alamgir Z, Beckmann K, Holt J, Cady NC (2017) Pulse width and height modulation for multi-level resistance in bi-layer TaOx-based RRAM. Appl Phys Lett 111(6):063111CrossRefGoogle Scholar
  6. 6.
    Prakash A, Deleruyelle D, Song J, Bocquet M, Hwang H (2015) Resistance controllability and variability improvement in a TaOx-based resistive memory for multilevel storage application. Appl Phys Lett 106(23):233104CrossRefGoogle Scholar
  7. 7.
    Bousoulas P, Stathopoulos S, Tsialoukis D, Tsoukalas D (2016) Low-power and highly uniform 3-b multilevel switching in forming free TiO2-x-based RRAM with embedded pt nanocrystals. IEEE Electron Device Lett 37(7):874–877CrossRefGoogle Scholar
  8. 8.
    Bousoulas P, Giannopoulos I, Asenov P, Karageorgiou I, Tsoukalas D (2017) Investigating the origins of high multilevel resistive switching in forming free Ti/TiO2−x-based memory devices through experiments and simulations. J Appl Phys 121(9):094501CrossRefGoogle Scholar
  9. 9.
    Stathopoulos S et al (2017) Multibit memory operation of metal-oxide Bi-layer memristors. Sci Rep 7(1):17532CrossRefGoogle Scholar
  10. 10.
    Schönhals A, Waser R, Wouters DJ (2017) Improvement of SET variability in TaOx-based resistive RAM devices. Nanotechnology 28(46):465203CrossRefGoogle Scholar
  11. 11.
    Sokolov AS et al (2017) Comparative study of Al2O3, HfO2, and HfAlOx for improved self-compliance bipolar resistive switching. J Am Ceram Soc 100(12):5638–5648CrossRefGoogle Scholar
  12. 12.
    Beckmann K, Holt J, Olin-Ammentorp W, Alamgir Z, Van Nostrand J, Cady NC (2017) The effect of reactive ion etch (RIE) process conditions on ReRAM device performance. Semicond Sci Technol 32(9):095013CrossRefGoogle Scholar
  13. 13.
    Kim KM, Lee SR, Kim S, Chang M, Hwang CS (2015) Self-limited switching in Ta2O5/TaOx memristors exhibiting uniform multilevel changes in resistance. Adv Funct Mater 25(10):1527–1534CrossRefGoogle Scholar
  14. 14.
    Bousoulas P, Asenov P, Karageorgiou I, Sakellaropoulos D, Stathopoulos S, Tsoukalas D (2016) Engineering amorphous-crystalline interfaces in TiO2-x/TiO2-y-based bilayer structures for enhanced resistive switching and synaptic properties. J Appl Phys 120(15):154501CrossRefGoogle Scholar
  15. 15.
    Garbin D et al (2015) HfO2-based OxRAM devices as synapses for convolutional neural networks. IEEE Trans Electron Devices 62(8):2494–2501CrossRefGoogle Scholar
  16. 16.
    Kuzum D, Yu S, Philip Wong HS (2013) Synaptic electronics: materials, devices and applications. Nanotechnology 24(38):382001CrossRefGoogle Scholar
  17. 17.
    Seok Jeong I, Kim I, Ziegler M, Kohlstedt H (2013) Towards artificial neurons and synapses: a materials point of view. RSC Adv 3(10):3169–3183CrossRefGoogle Scholar
  18. 18.
    Woo J et al (2016) Improved synaptic behavior under identical pulses using AlOx/HfO2 bilayer RRAM array for neuromorphic systems. IEEE Electron Device Lett 37(8):994–997CrossRefGoogle Scholar
  19. 19.
    Kim S, Kim H, Hwang S, Kim MH, Chang YF, Park BG (2017) Analog synaptic behavior of a silicon nitride memristor. ACS Appl Mater Interfaces 9(46):40420–40427CrossRefGoogle Scholar
  20. 20.
    Jang JW, Park S, Burr GW, Hwang H, Jeong YH (2015) Optimization of conductance change in Pr1-xCaxMnO3-based synaptic devices for neuromorphic systems. IEEE Electron Device Lett 36(5):457–459CrossRefGoogle Scholar
  21. 21.
    Saïghi S et al (2015) Plasticity in memristive devices for spiking neural networks. Front Neurosci 9:1–16CrossRefGoogle Scholar
  22. 22.
    Yu S, Member S, Wu Y, Jeyasingh R, Kuzum D, Wong HP (2011) An electronic synapse device based on metal oxide resistive switching memory for neuromorphic computation. IEEE Trans Electron Devices 58(8):2729–2737CrossRefGoogle Scholar
  23. 23.
    Snider GS (2008) Spike-timing-dependent learning in memristive nanodevices. IEEE/ACM Int Symp Nanoscale Archit NANOARCH 2008:85–92Google Scholar
  24. 24.
    Bi GQ, Poo MM (1998) Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci 18(24):10464–10472CrossRefGoogle Scholar

Copyright information

© The Korean Institute of Electrical Engineers 2019

Authors and Affiliations

  • Jongtae Kim
    • 1
  • Sanghoon Cho
    • 1
  • Taeheon Kim
    • 2
  • James Jungho Pak
    • 1
    • 2
    Email author
  1. 1.Department of Semiconductor Systems EngineeringKorea UniversitySeoulKorea
  2. 2.School of Electrical and EngineeringKorea UniversitySeoulKorea

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