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Applied Physics A

, 125:203 | Cite as

Tunnel current model of asymmetric MIM structure levying various image forces to analyze the characteristics of filamentary memristor

  • Jeetendra SinghEmail author
  • Balwinder Raj
Article
  • 39 Downloads

Abstract

Electro-formation in metal insulator metal (MIM) structure causes the emergence of the conductive filament and also leaves a tiny insulating gap amid upper electrode and conductive filament. Since the material property of conductive filament is unlike that of the upper electrode, it is vital to consider these dissimilarities of electrodes in tunneling phenomena. In this paper, the trapezoidal potential barrier of an insulating film sandwiched between two dissimilar electrodes (asymmetric MIM) is accounted for and superimposed with triangular, parabolic and rectangular image force potentials, to derive the corresponding mean barrier heights. Then, the impact of the insulating film thickness, metal’s work function difference, and the dielectric constant of the insulating film on tunneling current density are investigated, employing the same mean barrier potential. Finally, the obtained results are implemented in conductive filament-based memristor model to analyze its physical behavior through pinched hysteresis IV characteristics. It is ascertained that symmetric results are obtained in forward and reverse biased conditions. The incorporation of triangular image potential suppresses the barrier foremost, whereas parabolic image potential (PIP) has moderate effects on barrier lowering and rectangular image potential exhibits least lowering of the potential barrier, which renders minimum current. The switching dynamics of memristor apprises that PIP manifests only 0.5% deviation with experimental results in OFF switching and also shows good agreement in ON switching. The design is endorsed by comparing the outcomes with experimental and simulated data. This model enables reconfigurable and neuromorphic computing applications of the memristor.

Notes

References

  1. 1.
    J.J. Yang, D.B. Strukov, D.R. Stewart, Memristive devices for computing. Nat. Nanotechnol. 8(1), 13–24 (2013)CrossRefGoogle Scholar
  2. 2.
    L. Chua, Memristor-the missing circuit element. IEEE Trans. Circuit Theory 18(5), 507–519 (1971)CrossRefGoogle Scholar
  3. 3.
    J. Singh, B. Raj, Comparative analysis of memristor model for memories design. J. Semicond. IOP Sci. 39(7), 1–12 (2018)Google Scholar
  4. 4.
    L. Gao, F. Alibart, D.B. Strukov, Programmable CMOS/memristor threshold logic. IEEE Trans. Nanotechnol. 12(2), 115–119 (2013)CrossRefGoogle Scholar
  5. 5.
    C. Liu, Q. Yang, C. Zhang, H. Jiang, Q. Wu, H.H. Li, A memristor-based neuromorphic engine with a current sensing scheme for artificial neural network applications, in Design Automation Conference (ASP-DAC), 2017 22nd Asia and South Pacific (IEEE, 2017), pp. 647–652Google Scholar
  6. 6.
    L.O. Chua, S.M. Kang, Memristive devices and systems. Proc. IEEE 64(2), 209–223 (1976)MathSciNetCrossRefGoogle Scholar
  7. 7.
    L. Chua, Resistance switching memories are memristors. Appl. Phys. A 102(4), 765–783 (2011)CrossRefGoogle Scholar
  8. 8.
    D.B. Strukov, G.S. Snider, D.R. Stewart, R.S. Williams, The missing memristor found. Nature 453, 80–83 (2008)CrossRefGoogle Scholar
  9. 9.
    D.B. Strukov, J.L. Borghetti, R.S. Williams, Coupled ionic and electronic transport model of thin-film semiconductor memristive behavior. Small 5(9), 1058–1063 (2009)CrossRefGoogle Scholar
  10. 10.
    D.B. Strukov, R. Stanley Williams, Exponential ionic drift: fast switching and low volatility of thin-film memristors. Appl. Phys. A 94(3), 515–519 (2009)CrossRefGoogle Scholar
  11. 11.
    D. Biolek, V. Biolkova, Spice model of memristor with nonlinear dopant drift. Radioengineering 18(2), 210–214 (2009)Google Scholar
  12. 12.
    T. Prodromakis, B.P. Peh, C. Papavassiliou, C. Toumazou, A versatile memristor model with nonlinear dopant kinetics. Electron Devices IEEE Trans. 58(9), 3099–3105 (2011)CrossRefGoogle Scholar
  13. 13.
    J. Zha, H. Huang, Y. Liu, A novel window function for memristor model with application in programming analog circuits. IEEE Trans. Circuits Syst. II Express Briefs 63(5), 423–427 (2016)CrossRefGoogle Scholar
  14. 14.
    M.D. Pickett, D.B. Strukov, J.L. Borghetti, J.J. Yang, G.S. Snider, D.R. Stewart, R.S. Williams, Switching dynamics in titanium dioxide memristive devices. J. Appl. Phys. 106(7), 1–6 (2009)CrossRefGoogle Scholar
  15. 15.
    H. Abdalla, M.D. Pickett, SPICE modeling of memristors, in 2011 IEEE International Symposium of Circuits and Systems (ISCAS) (2011)Google Scholar
  16. 16.
    S. Kvatinsky, E.G. Friedman, A. Kolodny, C. Uri, Weiser, TEAM: ThrEshold adaptive memristor model. Circuits Syst. I Regul. Pap. IEEE Trans. 60(1), 211–221 (2013)MathSciNetCrossRefGoogle Scholar
  17. 17.
    A.M. Hassan, H.A.H. Fahmy, N.H. Rafat, Enhanced model of conductive filament-based memristor via including trapezoidal electron tunneling barrier effect. IEEE Trans. Nanotechnol. 15(3), 484–491 (2016)CrossRefGoogle Scholar
  18. 18.
    J. Borghetti, D.B. Strukov, M.D. Pickett, J.J. Yang, D.R. Stewart, R.S. Williams, Electrical transport and thermometry of electroformed titanium dioxide memristive switches. J. Appl. Phys. 106(12), 124504 (2009)CrossRefGoogle Scholar
  19. 19.
    J.G. Simmons, Generalized formula for the electric tunnel effect between similar electrodes separated by a thin insulating film. J. Appl. Phys. 34(6), 1793–1803 (1963)CrossRefGoogle Scholar
  20. 20.
    J.G. Simmons, Electric tunnel effect between dissimilar electrodes separated by a thin insulating film. J. Appl. Phys. 34(9), 2581–2590 (1963)CrossRefGoogle Scholar
  21. 21.
    J. Singh, B. Raj, Modeling of mean barrier height levying various image forces of metal insulator metal structure to enhance the performance of conductive filament based memristor model. IEEE Trans. Nanotechnol. 17(2), 268–275 (2018)CrossRefGoogle Scholar
  22. 22.
    A. Sommerfeld, H. Bethe, Handbuch der Physik von Geiger und Scheel, vol 24(2) (Verlag, Julius Springer, Berlin, 1933), p. 405fGoogle Scholar
  23. 23.
    R. Holm, The electric tunnel effect across thin insulator films in contacts. J. Appl. Phys. 22(5), 569–574 (1951)CrossRefGoogle Scholar
  24. 24.
    R. Stratton, Volt–current characteristics for tunneling through insulating films. J. Phys. Chem. Solids 23(9), 1177–1190 (1962)CrossRefGoogle Scholar
  25. 25.
    S.M. Sze, K.K. Ng, Physics of Semiconductor Devices, 3rd edn (Wiley-Interscience, New York, 2006)CrossRefGoogle Scholar
  26. 26.
    I.I.I. Cowell, E.W. Muir, S.W. Keszler, D.A. and J.F. Wager, Barrier height estimation of asymmetric metal–insulator–metal tunneling diodes. J. Appl. Phys. 114(21), 213703 (2013)CrossRefGoogle Scholar
  27. 27.
    E.H. Rhoderick, Metal–semiconductor contacts. IEEE Proc. I Solid State Electron Devices 129(1), 1 (1982)CrossRefGoogle Scholar
  28. 28.
    R. William, Smythe Static and Dynamic Electricity, Chap IV (McGraw-Hill Book Company Inc., New York, 1950)Google Scholar
  29. 29.
    E.W. Lim, R. Ismail, Conduction mechanism of valence change resistive switching memory: a survey. Electronics 4(3), 586–613 (2015)CrossRefGoogle Scholar
  30. 30.
    C. Wang, H. Wu, B. Gao, T. Zhang, Y. Yang, H. Qian, Conduction mechanisms, dynamics and stability in ReRAMs. Microelectron. Eng. 187, 121–133 (2018)Google Scholar
  31. 31.
    F.-C. Chiu, A review on conduction mechanisms in dielectric films. Adv. Mater. Sci. Eng. 2014, 1–18 (2014)Google Scholar
  32. 32.
    R.H. Fowler, L. Nordheim, 1928. Electron emission in intense electric fields. Proc. R. Soc. Lond. A Ser. Contain. Pap. Math. Phys. Charact. 119(781), 173–181CrossRefGoogle Scholar
  33. 33.
    M. Janousch, G.I. Meijer, U. Staub, B. Delley, S.F. Karg, B.P. Andreasson, Role of oxygen vacancies in Cr-doped SrTiO3 for resistance-change memory. Adv. Mater. 19(17), 2232–2235 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.VLSI Lab, Department of ECEDr. B R Ambedkar National Institute of Technology JalandharJalandharIndia

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