Applied Physics A

, 123:288

The basic I–V characteristics of memristor model: simulation and analysis

  • Faten Ouaja Rziga
  • Khaoula Mbarek
  • Sami Ghedira
  • Kamel Besbes
Article
  • 223 Downloads

Abstract

The memristor is fundamental electrical element theoretically postulated by Leon Chua in 1971 and successfully fabricated by HP Labs in 2008. However, its electrical characteristics are not yet fully understood which really leads us to study the behavior of such devices. For this development, it is essential to analyze a simple and flexible memristor model, for that reason SPICE memristor model seems to be frequently usable, especially in recent years, for its highly flexible and product very reliable and suitable for the electronic application. The adjustment of this model is based on the implementation of several parameters, which enables the changing on the IV characteristics of the device. Our aim is to analyze the functioning behavior of memristive devices within different types of the input voltage to demonstrate the flexibility and reliability of our work model. Our simulation results have been committed to prove the basic IV characteristics of such device, the switching behavior of this model for different applications (biological, neuromorphic…).

References

  1. 1.
    L. Chua, Memristor—the missing circuit element. IEEE Trans. Circuit Theory 18(5), 507–519 (1971)CrossRefGoogle Scholar
  2. 2.
    L.O. Chua, S.Mo.. Kang, Memristive devices and systems. Proc. IEEE 64(2), 209–223 (1976)MathSciNetCrossRefGoogle Scholar
  3. 3.
    D.B. Strukov, G.S. Snider, D.R. Stewart, R.S. Williams, The missing memristor found. Nature 453(7191), 80–83 (2008)ADSCrossRefGoogle Scholar
  4. 4.
    J.J. Yang, M.D. Pickett, X. Li, D.A. Ohlberg, D.R. Stewart, R.S. Williams, Memristive switching mechanism for metal/oxide/metal nanodevices. Nat. Nanotechnol. 3(7), 429–433 (2008)CrossRefGoogle Scholar
  5. 5.
    J.J. Yang, F. Miao, M.D. Pickett, D.A. Ohlberg, D.R. Stewart, C.N. Lau, R.S. Williams, The mechanism of electroforming of metal oxide memristive switches. Nanotechnology, 20(21), 215201 (2009)ADSCrossRefGoogle Scholar
  6. 6.
    M.J. Sharifi, Y.M. Banadaki, General spice models for memristor and application to circuit simulation of memristor-based synapses and memory cells. J. Circuits Syst. Comput. 19(02), 407–424 (2010)CrossRefGoogle Scholar
  7. 7.
    M.D. Pickett, Ph.D. thesis, University of California, Berkeley, 2010Google Scholar
  8. 8.
    S.H. Jo, T. Chang, I. Ebong, B.B. Bhadviya, P. Mazumder, W. Lu, Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 10(4), 1297–1301 (2010)ADSCrossRefGoogle Scholar
  9. 9.
    D. Biolek, V. Biolkova, Z. Biolek, SPICE model of memristor with nonlinear dopant drift. Radioengineering, 18(2), 210–214 (2009)Google Scholar
  10. 10.
    G. Howard, L. Bull, B. de Lacy Costello, E. Gale, A. Adamatzky, Evolving spiking networks with variable resistive memories. Evol. Comput. 22(1), 79–103 (2014)CrossRefGoogle Scholar
  11. 11.
    C. Yakopcic, T.M. Taha, G. Subramanyam, R.E. Pino, Generalized memristive device SPICE model and its application in circuit design. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 32(8), 1201–1214 (2013)CrossRefGoogle Scholar
  12. 12.
    M.A. Nugent, T.W. Molter, AHaH computing-from metastable switches to attractors to machine learning. PloS One 9(2), e85175 (2014)ADSCrossRefGoogle Scholar
  13. 13.
    C. Yakopcic, Ph.D. thesis, University of Dayton, Dayton, Ohio 2011Google Scholar
  14. 14.
    C. Yakopcic, T.M. Taha, G. Subramanyam, R.E. Pino, in Neural Networks (IJCNN) 2013, the 2013 International Joint Conference on. IEEE, University of Dayton, Dayton 4–9 Aug. 2013, pp. 1–7Google Scholar
  15. 15.
    L. Chen, C. Li, T. Huang, X. Hu, Y. Chen, The bipolar and unipolar reversible behavior on the forgetting memristor model. Neurocomputing 171, 1637–1643 (2016)CrossRefGoogle Scholar
  16. 16.
  17. 17.
    E. Gale, A. Adamatzky, B. De Lacy Costello, Slime mould memristors. BioNanoScience, 5(1), 1–8 (2015)CrossRefGoogle Scholar
  18. 18.
    A. Ascoli, R. Tetzlaff, F. Corinto, M. Gilli, in 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013). IEEE, 19–23 May 2013, pp. 205–208Google Scholar
  19. 19.
    Q. Li, A. Serb, T. Prodromakis, H. Xu, A memristor SPICE model accounting for synaptic activity dependence. PloS One 10(3), e0120506 (2015)CrossRefGoogle Scholar
  20. 20.
    D. Biolek, M. Di Ventra, Y.V. Pershin, Reliable SPICE simulations of memristors, memcapacitors and meminductors. arXiv preprint arXiv: 1307.2717 (2013)Google Scholar
  21. 21.
    Y.V. Pershin, M. Di Ventra. SPICE model of memristive devices with threshold. arXiv preprint arXiv:1204.2600 (2012)Google Scholar
  22. 22.
    K. Xu, Y. Zhang, L. Wang, M. Yuan, W.T. Joines, Q.H. Liu, in ISPDI 2013-Fifth International Symposium on Photoelectronic Detection and Imaging. International Society for Optics and Photonics, 25 June 2013, ed. by M. Gu, X. Yuan, M. Qiu. Proc. of SPIE (Beijing, China), pp. 89110 H–89110 H-7Google Scholar
  23. 23.
    L. Chen, C. Li, T. Huang, Y. Chen, S. Wen, J. Qi, A synapse memristor model with forgetting effect. Phys. Lett. A 377(45), 3260–3265 (2013)ADSCrossRefMATHGoogle Scholar
  24. 24.
    F. Xu-Dong, T. Yu-Hua, W. Jun-Jie, SPICE modeling of memristors with multilevel resistance states. Chin. Phys. B 21(9), 098901 (2012)ADSCrossRefGoogle Scholar
  25. 25.
    H. Abdalla, M.D. Pickett, in 2011 IEEE International Symposium of Circuits and Systems (ISCAS), 15–18 May 2011, pp. 1832–1835Google Scholar
  26. 26.
    T. Singh, Hybrid Memristor-CMOS (MeMOS) based Logic Gates and Adder Circuits. arXiv preprint arXiv:1506.06735, (2015)Google Scholar
  27. 27.
    Z. Kolka, D. Biolek, V. Biolkova, On steadystate analysis of circuits with memristors. in Radioelektronika (RADIOELEKTRONIKA), 2011 21st International Conference IEEE, 19–20 April 2011, pp. 1–4Google Scholar
  28. 28.
    K. Zaplatilek, in Proceeding ECC’11 Proceedings of the 5th European conference on European computing conference, 28–30 April 2011, (Paris, France), pp. 62–67Google Scholar
  29. 29.
    S.P. Mohanty, Ph.D. thesis, University of North Texas, Denton, Texas, 2013Google Scholar
  30. 30.
    A. Ascoli, R. Tetzlaff, Z. Biolek, Z. Kolka, V. Biolkovà, D. Biolek, The art of finding accurate memristor model solutions. IEEE J. Emerg. Sel. Top. Circuits Syst. 5(2), 133–142 (2015)CrossRefGoogle Scholar
  31. 31.
    H. Elgabra, I. A. Farhat, A. S. Al Hosani, D. Homouz, B. Mohammad, in Innovations in Information Technology (IIT), 2012 International Conference on. IEEE, 18–20 March 2012, pp. 156–161 (2012)Google Scholar
  32. 32.
    Y. Halawani, B. Mohammad, D. Homouz, M. Al-Qutayri, H. Saleh, Modeling and optimization of memristor and STT-RAM-Based memory for low-power applications. IEEE Trans. Very Large Scale Integr. VLSI Syst. 24(3), 1003–1014 (2016)CrossRefGoogle Scholar
  33. 33.
    Y. Yang, J. Mathew, R. A. Shafik, D. K. Pradhan, VerilogA based effective complementary resistive switch model for simulations and analysis. IEEE Embed. Syst. Lett. 6(1), 12–15 (2014)CrossRefGoogle Scholar
  34. 34.
    A. Shahim-Aeen, G. Karimi, Triplet-based spike timing dependent plasticity (TSTDP) modeling using VHDL-AMS. Neurocomputing 149, 1440–1444 (2015)CrossRefGoogle Scholar
  35. 35.
    J. Haase, A. Lange, Hybrid Dynamical Systems for Memristor Modelling. In: Languages, Design Methods, and Tools for Electronic System Design (pp. 87–101). Springer International Publishing (2015). Volume 311 of the series Lecture Notes in Electrical Engineering, 22 August 2014, pp 87–101Google Scholar
  36. 36.
    A. Rezgui, L. Gerbaud, B. Delinchant, Unified modeling technique using VHDL-AMS and software components. Math. Comput. Simul. 90, 266–276 (2013)MathSciNetCrossRefGoogle Scholar
  37. 37.
    J. Haase, A. Lange, in Specification and Design Languages (FDL), 2013 Forum on, 24–26 Sept. 2013, pp. 1636–9874Google Scholar
  38. 38.
    L. Zhang, N. Ge, J.J. Yang, Z. Li, R.S. Williams, Y. Chen, Low voltage two-state-variable memristor model of vacancy-drift resistive switches. Appl. Phys. A 119(1), 1–9 (2015)ADSCrossRefGoogle Scholar
  39. 39.
    A.S. Oblea, A. Timilsina, D. Moore, K.A. Campbell, in 2010 International Joint Conference on Neural Networks (IJCNN), 2010, pp. 1–3Google Scholar
  40. 40.
    F. Miao, J.P. Strachan, J.J. Yang, M.X. Zhang, I. Goldfarb, A.C. Torrezan, P. Eschbach, R.D. Kelley, G. Medeiros-Ribeiro, R.S. Williams, Anatomy of a nanoscale conduction channel reveals the mechanism of a high performance memristor. Adv. Mater. 23(47), 5633–5640 (2011)CrossRefGoogle Scholar
  41. 41.
    K.J. Miller, Ph.D. thesis, Iowa State University, 2010Google Scholar
  42. 42.
    S.H. Jo, W. Lu, CMOS compatible nanoscale nonvolatile resistance switching memory. Nano Lett. 8(2), 392–397 (2008)ADSCrossRefGoogle Scholar
  43. 43.
    J.P. Strachan, A.C. Torrezan, G. Medeiros-Ribeiro, R.S. Williams, Measuring the switching dynamics and energy efficiency of tantalum oxide memristors. Nanotechnology 22(50), 505402 (2011)ADSCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Faten Ouaja Rziga
    • 1
  • Khaoula Mbarek
    • 2
  • Sami Ghedira
    • 2
  • Kamel Besbes
    • 2
    • 3
  1. 1.Physics of Semiconductors and Electronics Components LabUniversity of MonastirMonastirTunisia
  2. 2.Microelectronics and Instrumentation LabUniversity of MonastirMonastirTunisia
  3. 3.Centre for Research on Microelectronics and NanotechnologySousse Technology ParkSousseTunisia

Personalised recommendations