access icon openaccess Insight into physics-based RRAM models – review

This article presents a review of physical, analytical, and compact models for oxide-based RRAM devices. An analysis of how the electrical, physical, and thermal parameters affect resistive switching and the different current conduction mechanisms that exist in the models is performed. Two different physical mechanisms that drive resistive switching; drift diffusion and redox which are widely adopted in models are studied. As for the current conduction mechanisms adopted in the models, Schottky and generalised hopping mechanisms are investigated. It is shown that resistive switching is strongly influenced by the electric field and temperature, while the current conduction is weakly dependent on the temperature. The resistive switching and current conduction mechanisms in RRAMs are highly dependent on the geometry of the conductive filament (CF). 2D and 3D models which incorporate the rupture/formation of the CF together with the variation of the filament radius present accurate resistive switching behaviour.

Inspec keywords: electrical conductivity transitions; resistive RAM; integrated circuit modelling; oxidation; reduction (chemical); electrical resistivity

Other keywords: electrical parameters; electric field; current conduction mechanisms; 2D models; resistive switching behaviour; conductive filament; drift diffusion; filament radius; generalised hopping mechanisms; physics-based RRAM models; oxide-based RRAM devices; conductive filament geometry; 3D models; resistive switching; compact models; thermal parameters; Schottky hopping mechanisms; redox

Subjects: Semiconductor integrated circuit design, layout, modelling and testing; Digital circuit design, modelling and testing; Memory circuits; Semiconductor storage

References

    1. 1)
      • 30. Larentis, S., Nardi, F., Balatti, S., et al: ‘Resistive switching by voltage-driven ion migration in bipolar RRAM—part i: experimental study’, IEEE Trans. Electron Devices, 2012, 59, (9), pp. 24682475.
    2. 2)
      • 23. Zhao, Y.D., Huang, P., Liu, C., et al: ‘Simulation of TaOX-RRAM with Ta2O5−X/TaO2−Xstack engineering’. Proc. Int. Conf. Simulation Semiconductor Processes Devices (SISPAD), Washington, DC., USA, October 2015, pp. 285288.
    3. 3)
      • 28. Bocquet, M., Deleruyelle, D., Aziza, H., et al: ‘Robust compact model for bipolar oxide-based resistive switching memories’, IEEE Trans. Electron Devices, 2014, 61, (3), pp. 674681.
    4. 4)
      • 22. Huang, P., Liu, X.Y., Chen, B., et al: ‘A physics-based compact model of metal-oxide-based RRAM DC and AC operations’, IEEE Trans. Electron Devices, 2013, 60, (12), pp. 40904097.
    5. 5)
      • 33. Simmons, J.: ‘Richardson-Schottly effects in solids’, Phys. Rev. Lett., 1965, 15, (25), pp. 967968.
    6. 6)
      • 6. Hatem, F.O., Ho, P.W.C., Kumar, T.N., et al: ‘Modeling of bipolar resistive switching of a nonlinear MISM memristor’, Semicond. Sci. Technol., 2015, 30, (11), p. 115009.
    7. 7)
      • 10. Saremi, M., Barnaby, H.J., Edwards, A., et al: ‘Analytical relationship between anion formation and carrier-trap statistics in chalcogenide glass films’, ECS Electrochem.Lett., 2015, 4, (7), pp. H29H31.
    8. 8)
      • 20. Kim, S., Choi, S., Lu, W.: ‘Comprehensive physical model of dynamic resistive switching in an oxide memristor’, ACS Nano, 2014, 8, (3), pp. 23692376.
    9. 9)
      • 7. Kumar, D., Aluguri, R., Chand, U., et al: ‘Metal oxide resistive switching memory: materials, properties and switching mechanisms’, Ceramics Int., 2017, 43, pp. S547S556.
    10. 10)
      • 25. Jagath, A.L., Kumar, T.N., Almurib, H.A.F.: ‘Modeling of current conduction during RESET phase of Pt/Ta2O5/TaOx/Pt bipolar resistive RAM devices’. Proc. 7th IEEE Non-Volatile Mem. Syst. Appl. Symp. (NVMSA), Hakodate, Japan, 2018, pp. 5560.
    11. 11)
      • 1. El-Hassan, N.H., Kumar, T.N., Almurib, H.A.F.: ‘Phase change memory cell emulator circuit design’, Microelectron. J., 2017, 62, (February), pp. 6571.
    12. 12)
      • 32. Graves, C.E., Dávila, N., Merced-Grafals, E.J., et al: ‘Temperature and field-dependent transport measurements in continuously tunable tantalum oxide memristors expose the dominant state variable’, Appl. Phy.Lett., 2017, 110, (12), p. 123501.
    13. 13)
      • 5. Fang, Z., Yu, H.Y., Li, X., et al: ‘Hfox/TiOx/HfOx/TiOx multilayer-based forming-free RRAM devices with excellent uniformity’, IEEE Electron Device Lett., 2011, 32, (4), pp. 566568.
    14. 14)
      • 18. Ambrogio, S., Balatti, S., Gilmer, D.C., et al: ‘Analytical modeling of oxide-based bipolar resistive memories and complementary resistive switches’, IEEE Trans. Electron Devices, 2014, 61, (7), pp. 23782386.
    15. 15)
      • 27. Kim, S., Kim, S-J., Kim, K.M., et al: ‘Physical electro-thermal model of resistive switching in bi-layered resistance-change memory’, Sci. Rep., 2013, 3, (1), p. 1680.
    16. 16)
      • 12. Ho, P.W.C., Hatem, F.O., Almurib, H.A.F., et al: ‘Enhanced SPICE memristor model with dynamic ground’. Proc. Int. IEEE Circuits and Systems Symp. (ICSyS), Langkawi, Malaysia, 2015, pp. 130132.
    17. 17)
      • 4. Strukov, D.B., Snider, G.S., Stewart, D.R., et al: ‘The missing memristor found’, Nature, 2008, 453, (7191), pp. 8083.
    18. 18)
      • 11. Saremi, M.: ‘Carrier mobility extraction method in ChGs in the UV light exposure’, Micro Nano Lett., 2016, 11, (11), pp. 762764.
    19. 19)
      • 15. Siemon, A., Menzel, S., Marchewka, A., et al: ‘Simulation of TaOx-based complementary resistive switches by a physics-based memristive model’. Proc. IEEE Int. Symp. Circuits Syst., Melbourne, Australia, 2014, pp. 14201423.
    20. 20)
      • 26. Hur, J.H., Kim, K.M., Chang, M., et al: ‘Modeling for multilevel switching in oxide-based bipolar resistive memory’, Nanotechnology, 2012, 23, (22), p. 225702.
    21. 21)
      • 34. Sze, S.M., Lee, M.K.: ‘Semiconductor devices: physics and technology’ (Wiley, Hoboken, 2012).
    22. 22)
      • 2. Chua, L.O.: ‘Memristor—the missing circuit element’, IEEE Trans. Circuit Theory, 1971, 18, (5), pp. 507519.
    23. 23)
      • 8. Saremi, M., Rajabi, S., Barnaby, H.J., et al: ‘The effects of process variation on the parametric model of the static impedance behavior of programmable metallization cell (PMC)’. Proc. Materials Res. Soc. Symp., San Francisco, USA, 2014, vol. 1692.
    24. 24)
      • 16. Chee, H.L., Kumar, T.N., Almurib, H.A.: ‘Multifilamentary conduction modelling of bipolar Ta2O5/TaOx Bi-layered RRAM’. Proc. 7th IEEE Non-Volatile Mem. Syst. Symp. (NVMSA), Hakodate, Japan, 2018, pp. 113114.
    25. 25)
      • 17. González-Cordero, G., Jiménez-Molinos, F., Roldán, J.B., et al: ‘In-depth study of the physics behind resistive switching in TiN/Ti/HfO2/W structures’, J. Vac. Sci. Technol. B, 2017, 35, (1), p. 01A110.
    26. 26)
      • 21. Zhao, Y., Huang, P., Chen, Z.: ‘Modeling and optimization of bilayered TaOx RRAM based on defect evolution and phase transition effects’, IEEE Trans. Electron Devices, 2016, 63, (4), pp. 15241532.
    27. 27)
      • 14. Hur, J.H., Lee, M.J., Lee, C.B., et al: ‘Modeling for bipolar resistive memory switching in transition-metal oxides’, Phys. Rev. B - Condensed Matter Mater. Phys., 2010, 82, (15), p. 155321.
    28. 28)
      • 9. Saremi, M.: ‘A physical-based simulation for the dynamic behavior of photodoping mechanism in chalcogenide materials used in the lateral programmable metallization cells’, Solid State Ion., 2016, 290, pp. 15.
    29. 29)
      • 13. Hatem, F.O., Kumar, T.N., Almurib, H.: ‘A SPICE model of the Ta2O5/TaOx Bi-layered RRAM’, IEEE Trans. Circuits Syst., 2016, 63, (9), pp. 14871498.
    30. 30)
      • 3. Xia, Q., Robinett, W., Cumbie, M.W., et al: ‘Memristor − CMOS hybrid integrated circuits for reconfigurable logic’, Nano Lett., 2009, 9, (10), pp. 36403645.
    31. 31)
      • 35. Chang, T., Jo, S.H., Kim, K.H., et al: ‘Synaptic behaviors and modeling of a metal oxide memristive device’, Appl. Phys. A Mater. Sci. Process., 2011, 102, (4), pp. 857863.
    32. 32)
      • 24. Li, H., Jiang, Z., Huang, P., et al: ‘Variation-aware, reliability-emphasized design and optimization of RRAM using SPICE model’. Proc. Des. Automation Test Europe Conf. Exhibition (DATE), Grenoble, France, 2015, pp. 14251430.
    33. 33)
      • 31. Villena, M.A., Jiménez-Molinos, F., Roldán, J. B., et al: ‘An in-depth simulation study of thermal reset transitions in resistive switching memories’, J. Appl. Phys., 2013, 114, (14), p. 144505.
    34. 34)
      • 29. Jiang, Z., Wu, Y., Yu, S., et al: ‘A compact model for metal-oxide resistive random access memory with experiment verification’, IEEE Trans. Electron Devices, 2016, 63, (5), pp. 18841892.
    35. 35)
      • 19. Larentis, S., Nardi, F., Balatti, S., et al: ‘Resistive switching by voltage-driven ion migration in bipolar RRAM—part II: modeling’, IEEE Trans. Electron Devices, 2012, 59, (9), pp. 24682475.
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2018.5234
Loading

Related content

content/journals/10.1049/joe.2018.5234
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading