Advertisement

Microscopic KMC Modeling of Oxide RRAMs

  • Toufik SadiEmail author
  • Asen Asenov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11189)

Abstract

We investigate the microscopic behavior of oxide-based resistive random-access memory (RRAM) cells by using a unique three-dimensional (3D) physical simulator. RRAMs are attracting substantial attention and are considered as the next generation of non-volatile memory technologies. We study the operation of RRAM cells based on silica-rich silicon (SiO\(_x\)) and hafnia (HfO\(_x\)), by employing the stochastic kinetic Monte Carlo (KMC) approach for charge transport. The simulator self-consistently couples electron and ionic transport to the heat generation and diffusion phenomena and includes carefully the physics and random nature of vacancy generation and recombination, and trapping mechanisms. It models the dynamics of conductive filaments (CFs) in the 3D real space and captures correctly resistance switching regimes, including the CF formation (electroforming), set and reset processes. We describe the stochastic simulation process used for device analysis. We discuss the differences in the origin of switching between silica and hafnia based devices, and address the influence of the initial vacancy population on resistance switching in silicon rich silica RRAMs. We also emphasis the need for using 3D models and including thermal self-consistency to capture accurately the memristive nature of device switching.

Keywords

Kinetic Monte Carlo RRAM Nano-devices Charge transport Thermal effects 

Notes

Acknowledgment

The authors thank the Engineering and Physical Sciences Research Council (EPSRC−UK) for funding under grant agreement EP/K016776/1.

References

  1. 1.
    The ITRS Report 2013. http://www.itrs2.net/2013-itrs.html. Accessed 5 June 2018
  2. 2.
    Buckwell, M., Montesi, L., Hudziak, S., Mehonic, A., Kenyon, A.J.: Conductance tomography of conductive filaments in intrinsic silicon-rich silica RRAM. Nanoscale 7(43), 18030–18035 (2015)CrossRefGoogle Scholar
  3. 3.
    Sadi, T., Mehonic, A., Montesi, L., Buckwell, M., Kenyon, A., Asenov, A.: Investigation of resistance switching in SiO\(_x\) RRAM cells using a 3D multi-scale kinetic Monte Carlo simulator. J. Phys. Condens. Matter 30(8), 084005 (2018)CrossRefGoogle Scholar
  4. 4.
    Sadi, T., et al.: Advanced physical modeling of SiO\(_x\) resistive random access memories. In: International Conference on Simulation of Semiconductor Processes and Devices (SISPAD 2016), 6–8 September 2016, Nuremberg, Germany, pp. 149–152 (2016)Google Scholar
  5. 5.
    Chae, S.C., et al.: Random circuit breaker network model for unipolar resistance switching. Adv. Mater. 20, 1154–1159 (2008)CrossRefGoogle Scholar
  6. 6.
    Brivio, S., Spiga, S.: Stochastic circuit breaker network model for bipolar resistance switching memories. J. Comput. Electron. 16(4), 1154–1166 (2017)CrossRefGoogle Scholar
  7. 7.
    Yu, S., Guan, X., Wong, H.-S.P.: On the stochastic nature of resistive switching in metal oxide RRAM: physical modeling, Monte Carlo simulation, and experimental characterization. In: 2011 IEEE International Electron Devices Meeting (IEDM), Washington, DC, USA, 5–7 December 2011, p. 17.3.1 (2011)Google Scholar
  8. 8.
    Kim, S., et al.: Physical electro-thermal model of resistive switching in bi-layered resistance-change memory. Sci. Rep. 3, 1680 (2013)CrossRefGoogle Scholar
  9. 9.
    Jegert, G.C.: Modeling of leakage currents in high-k dielectrics. PhD Dissertation, Technical University Munich, Germany (2011). http://www.iaea.org/inis/collection/NCLCollectionStore/_Public/44/011/44011419.pdf. Accessed 5 June 2018
  10. 10.
    Vandelli, L., Padovani, A., Larcher, L., Southwick, R.G., Knowlton, W.B., Bersuker, G.: A physical model of the temperature dependence of the current through SiO2/HfO2 stacks. IEEE Trans. Electron. Devices 58, 2878–2887 (2011)CrossRefGoogle Scholar
  11. 11.
    Mehonic, A., et al.: Resistive switching in silicon sub-oxide films. J. Appl. Phys. 111, 074507 (2012)CrossRefGoogle Scholar
  12. 12.
    Mehonic, A., et al.: Structural changes and conductance thresholds in metal-free intrinsic SiO\(_x\) resistive random access memory. J. Appl. Phys. 117, 124505 (2015)CrossRefGoogle Scholar
  13. 13.
    McPherson, J., Kim, J.-Y., Shanware, A., Mogul, H.: Thermochemical description of dielectric breakdown in high dielectric constant materials. Appl. Phys. Lett. 82, 2121–2123 (2003)CrossRefGoogle Scholar
  14. 14.
    Sadi, T., Thobel, J.-L., Dessenne, F.: Self-consistent electrothermal Monte Carlo simulation of single InAs nanowire channel metal-insulator field-effect transistors. J. Appl. Phys. 108, 084506 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Neuroscience and Biomedical EngineeringAalto UniversityAaltoFinland
  2. 2.School of Engineering, Electronic and Nanoscale EngineeringUniversity of GlasgowGlasgowScotland, UK

Personalised recommendations