Autors: Kirilov, S. M.
Title: A Simple Memristor Model for Memory Devices
Keywords: enhanced memristor model, LTSPICE model, memristor arrays, memristors, nonlinear dopant drift

Abstract: Memristors are favorable and beneficial electronic components, having respectable memorizing and switching behavior. Owing to their minimal energy requirements, nano-scale dimensions, and seamless integration with CMOS high-density integrated circuits, memristors hold a potential for application in neural nets, memory arrays, and various electronic configurations. This paper presents an enhanced and simplified model for metal-oxide memristive elements, operating with increased speed and efficiency. For analysis and application in memory matrices, LTSPICE library model is created. The offered memristor model effectively operates in high-frequency mode, representing the key patterns of memristors. Its suitable functioning in complex electronic circuits is analyzed and confirmed.

References

  1. Strukov, D.B., Snider, G.S., Stewart, D.R., Williams, S., „The missing memristor found,“Nature 2008, vol. 453, pp. 80 – 83.
  2. Ryndin E, Andreeva N, Luchinin V., “Compact Model for Bipolar and Multilevel Resistive Switching in Metal-Oxide Memristors,”. Micromachines. 2022; vol. 13, issue (1), pp. 1-14.
  3. James, A., “Memristors - Circuits and Applications of Memristor Devices,” IntechOpen, ISBN 978-1-78984-074-2, 2019.
  4. Kolka, Z., V. Biolkova, D. Biolek, "Simplified SPICE model of TiO2 Memristor,” Int. Conf. MEMRISYS, Cyprus, 2015, pp. 1-2.
  5. Campbell, K.A., “Self-directed channel memristor for high temperature operation,” Microelectronics journal, 2017, 59, pp.10-14.
  6. Amer, S., Sayyaparaju, S., Rose, G.S., Beckmann, K., Cady, N.C., “A practical hafnium-oxide memristor model suitable for circuit design and simulation,” In 2017 IEEE ISCAS Conf., May 28, pp. 1-4.
  7. Ascoli, A., R. Tetzlaff, F. Corinto, M. Gilli, "PSpice switch-based versatile memristor model," 2013 ISCAS, China, 2013, pp. 205-208.
  8. Mladenov, V. “Analysis and Simulations of Hybrid Memory Scheme Based on Memristors,” Electronics, 2018; vol. 7, issue 11, pp. 1-11.
  9. Molter, T. W., M. A. Nugent, "The Generalized Metastable Switch Memristor Model," CNNA 2016, Dresden, Germany, 2016, pp. 1-2.
  10. Lehtonen, E., Laiho, M., “CNN using memristors for neighborhood connections,” In Proc.IEEE CNNA 2010 Conf., USA, 2010, pp. 1–4.
  11. Mladenov, V., "A Unified and Open LTSPICE Memristor Model Library," MDPI Electronics, 2021, vol. 10, no. 13.
  12. Biolek, D., Z. Kolka, V. Biolkova and Z. Biolek, "Memristor models for SPICE simulation of extremely large memristive networks," IEEE Symposium ISCAS, Montreal, Canada, 2016, pp. 389-392.
  13. Vontobel, P.O., Robinett, W., Kuekes, P.J., Stewart, D.R., Straznicky, J. and Williams, R.S., 2009. “Writing to and reading from a nano-scale crossbar memory based on memristors,” Nanotechnology, vol. 20, issue 42, pp. 1 – 21.
  14. Yang, Y., Lee, S.C., “Circuit Systems with MATLAB and PSpice,” John Wiley & Sons: Hoboken, USA, 2008, ISBN 978-04-7082-240-1.
  15. Solovyeva, E. B., Azarov, V. A., "Comparative Analysis of Memristor Models with a Window Function Described in LTspice," 2021 IEEE ElConRus, 2021, pp. 1097-1101.
  16. Dautovic, S., N. Samardzic, A. Juhas, A. Ascoli, R. Tetzlaff, "Simscape and LTspice models of HP ideal generic memristor based on finite closed form solution for window functions," 2021 28th IEEE International Conference ICECS, Dubai, 2021, pp. 1-6.
  17. Yakopcic, C., T. M. Taha, G. Subramanyam, R. E. Pino, "Memristor SPICE model and crossbar simulation based on devices with nanosecond switching time," 2013 IJCNN, USA, 2013, pp. 1-7.
  18. Justo, J. F., W. Beccaro, "Generalized Adaptive Polynomial Window Function," in IEEE Access, vol. 8, pp. 187584-187589, 2020.
  19. Zafar, M., Awais, M., Shehzad, M., “Computationally efficient memristor model based on Hann window function,” Microelectronics Journal, Vol. 125, 2022, pp. 1-12.
  20. Ascoli, A., Tetzlaff, R., Biolek, Z., Kolka, Z., Biolkova, V., Biolek, D. “The Art of Finding Accurate Memristor Model Solutions,” IEEE J. Emerg. Sel. Top. Circuits Syst., 2015, vol. 5, pp. 133–142.
  21. Pedretti, G., E. Ambrosi, D. Ielmini, "Conductance variations and their impact on the precision of in-memory computing with resistive switching memory (RRAM)," 2021 IEEE IRPS, USA, 2021, pp. 1-8.
  22. Zhevnenko, D.A., Meshchaninov, F.P., Kozhevnikov, V.S., Shamin, E.S., Telminov, O.A. and Gornev, E.S., 2021. “Research and development of parameter extraction approaches for memristor models,” Micromachines, vol. 12, issue (10), pp. 1-16.

Issue

2024 13th International Conference on Modern Circuits and Systems Technologies, MOCAST 2024 - Proceedings, 2024, , https://doi.org/10.1109/MOCAST61810.2024.10615437

Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus