Skip to main content
Log in

Circuit-level simulation of resistive-switching random-access memory cross-point array based on a highly reliable compact model

  • Published:
Journal of Computational Electronics Aims and scope Submit manuscript

Abstract

In this study, a simple, reliable, and universal circuit model of bipolar resistive-switching random-access memory (RRAM) is presented for the circuit-level simulation of a high-density cross-point RRAM array. For higher accuracy and reliability, the compact model has been developed to match the measurement data of the fabricated RRAM devices with \(\hbox {SiN}_{{x}}\) and \(\hbox {HfO}_{{x}}\) switching layers showing different reset switching behaviors. In the SPICE simulation, the RRAM cross-point array is virtually realized by embedding the empirically modeled memory cells, by which device performances such as read margin and power consumption in the high-density array are closely investigated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Fackenthal, R., Kitagawa, M., Otsuka, W., Prall, K., Mills, D., Tsutsui, K., Javanifard, J., Tedrow, K., Tsushima, T., Shibahara, Y., Hush, G.: A 16Gb ReRAM with 200 MB/s write and 1 GB/s read in 27 nm technology. In: IEEE International Solid-State Circuits Conference, pp. 338–340 (2014)

  2. Kim, B.Y., Lee, K.J., Chung, S.O., Kim, S.G., Ko, Y.S., Kim, H.S.: Low power switching of Si-doped Ta\(_{2}\)O\(_{5}\) resistive random access memory for high density memory application. Jpn. J. Appl. Phys. 55, 04EE09 (2016)

    Article  Google Scholar 

  3. Sakuragawa, Y., Takagi, Y., Ikai, T., Maeda, K., Dao, T.T., Sakai, H., Murata, H.: Achieving high ON/OFF ratio and good stability in organic nonvolatile resistive memory devices with polyisocyanide bearing oligothiophene. Jpn. J. Appl. Phys. 55, 03DC10 (2016)

    Article  Google Scholar 

  4. Hsiao, Y.-P., Yang, W.-L., Wu, C.-C., Lin, L.-M., Chin, F.-T., Lin, Y.-H., Yang, K.-L.: Improving high-resistance state uniformity and leakage current for polyimide-based resistive switching memory by rubbing post-treatment. Jpn. J. Appl. Phys. 55, 01AA09 (2016)

    Article  Google Scholar 

  5. Wu, C.-Y., You, H.-C., Lin, G.-K., Yang, W.-L.: Improving the operational characteristic stability in Al/Au/ZnO/Al resistive random access memory devices. Jpn. J. Appl. Phys. 55, 044101 (2016)

    Article  Google Scholar 

  6. Su, Y.-T., Chang, T.-C., Tsai, T.-M., Chang, K.-C., Chu, T.-J., Chen, H.-L., Chen, M.-C., Yang, C.-C., Huang, H.-C., Lo, I., Zheng, J.-C., Sze, S.M.: Suppression of endurance degradation by applying constant voltage stress in one-transistor and one-resistor resistive random access memory. Jpn. J. Appl. Phys. 56, 010303 (2017)

    Article  Google Scholar 

  7. Jo, S.H., Chang, T., Ebong, I., Bhadviya, B.B., Mazumder, P., Lu, W.: Nanoscale memristor device as syanpse in neuromorphic systems. Nano Lett. 10, 1297 (2010)

    Article  Google Scholar 

  8. Chang, T., Jo, S.-H., Lu, W.: Short-term memory to long-term memory transition in a nanoscale memristor. ACS Nano 5, 7669 (2011)

    Article  Google Scholar 

  9. Gao, B., Kang, J., Zhou, Z., Chen, Z., Huang, P., Liu, L., Liu, X.: Metal oxide resistive random access memory based synaptic devices for brain-inspired computing. Jpn. J. Appl. Phys. 55, 04EA06 (2016)

    Article  Google Scholar 

  10. Kawahara, A., Azuma, R., Ikeda, Y., Kawai, K., Katoh, Y., Tanabe, K, Nakamura, T., Sumimoto, Y., Yamada, N., Nakai, N., Sakamoto, S., Hayakawa, Y., Tsuji, K., Yoneda, S., Himeno, A., Origasa, K., Shimakawa, K., Takagi, T., Mikawa, T., Aono, K.: An 8 Mb multi-layered cross-point ReRAM macro with 443 MB/s write throughput. In: IEEE International Solid-State Circuits Conference, pp. 432–434 (2012)

  11. Liu, T.-Y., Yan, T.H., Scheuerlein, R., Chen, Y., Lee, J.K., Balakrishnan, G., Yee, G., Zhang, H., Yap, A., Ouyang, J., Sasaki, T, Addepalli, S., Al-Shamma, A., Chen, C.-Y., Gupta, M., Hilton, G., Joshi, S., Kathuria, A., Lai, V., Masiwal, D., Matsumoto, M., Nigam, A., Pai, A., Pakhale, J., Siau, C.-H., Wu, X., Yin, R., Peng, L., Kang, J.-Y., Huynh, S., Wang, H, Nagel, N., Tanaka, Y., Higashitani, M., Minvielle, T., Gorla, C, Tsukamoto, T., Yamaguchi, T. Okajima, M., Okamura, T., Takase, S., Hara, T., Inoue, H., Fasoli, l., Mofidi, M., Shrivastava, R., Quader, K.: A \(130.7\text{mm}^{2}\) 2-layer 32 Gb ReRAM memory device in 24 nm technology. In: IEEE International Solid-State Circuits Conference, pp. 210–212 (2013)

  12. Eryilmaz, S.B., Kuzum, D., Yu, S., Wong, H.-S.P.: Device and system level design considerations for analog-non-volatile-memory based neuromorphic architectures. In: IEEE International Electron Devices Meeting, pp. 64–67 (2015)

  13. Burr, G.W., Naraynan, P., Shelby, R.M., Boybat, S.I., Nolfo, C., Leblebici, Y.: Large-scale neural networks implemented with non-volatile memory as the synaptic weight element: comparative performance analysis (accuracy, speed, and power). In: IEEE International Electron Devices Meeting, pp. 76–79 (2015)

  14. Yu, S., Chen, P.-Y., Cao, Y., Xia, L., Wang, Y., Wu, H.: Scaling-up resistive synaptic arrays for neuro-inspired architecture: challenges and prospect. In: IEEE International Electron Devices Meeting, pp. 451–454 (2015)

  15. Prezioso, M., Kataeva, I., Merrikh-Bayat, F., Hoskins, B., Adam, G., Sota, T., Likharev, K., Strukov, D.: Modeling and implementation of firing-rate neuromorphic-network classifiers with Bilayer \(\text{ Pt }/\text{ Al }_{2}\text{ O }_{3}/\text{ TiO }_{{\rm 2-x}}/\text{ Pt }\) memristors. In: IEEE International Electron Devices Meeting, pp. 455–458 (2015)

  16. Chen, B., Cai, F., Zhou, J., Ma, W., Sheridan, P., Lu, W.D.: Efficient in-memory computing architecture based on crossbar arrays. In: IEEE International Electron Devices Meeting, pp. 459–462 (2015)

  17. Moon, K., Chan, E., Park, J., Gi, S., Chu, M., Baek, K.: High density neuromorphic system with \(\text{ Mo }/\text{ Pr }_{0.7}\text{ Ca }_{0.3}\text{ MnO }_{3}\) synapse and \(\text{ NbO }_{2}\) IMT oscillator neuron. In: IEEE International Electron Devices Meeting, pp. 463–466 (2015)

  18. Guan, X., Yu, S., Wong, H.-S.P.: A SPICE compact model of metal oxide resistive switching memory with variations. IEEE Electron Device Lett. 33, 1405 (2012)

    Article  Google Scholar 

  19. Li, H., Huang, P., Gao, B., Chen, B., Liu, X., Kang, J.: A SPICE model of resistive random access memory for large-scale memory array simulation. IEEE Electron Device Lett. 35, 211 (2014)

    Article  Google Scholar 

  20. Chang, H.-L., Li, H.-C., Liu, C.W., Chen, F., Tsai, M.-J.: A parameterized SPICE macromodel of resistive random access memory and circuit demonstration. In: Proceedings of SISPAD, vol. 163 (2011)

  21. Blaso, J., Jančovič, P., Fröhlich, K., Suñé, J., Miranda, E.: Modeling of the switching I–V characteristics in ultrathin (5 nm) atomic layer deposited HfO2 films using the logistic hysteron. J. Vac. Sci. Technol. B 33, 01A102 (2015)

    Article  Google Scholar 

  22. Kim, Y.S., Min, K.-S.: Behavioral current–voltage model with intermediate states for unipolar resistive memories. J. Semicond. Technol. Sci. 13, 539 (2013)

    Article  Google Scholar 

  23. Kim, S., Cho, S., Ryoo, K.-C., Park, B.-G.: Effects of conducting defects on resistive switching characteristics of \(\text{ SiN }_{x}\)-based resistive random-access memory with MIS structure. J. Vac. Sci. Technol. B 33, 6 (2015)

    Google Scholar 

  24. Kim, S., Cho, S., Park, B.-G.: Sub-100-nA-operating Si-compatible \(\text{ Ni }/\text{ Ti }/\text{ HfO }_{2}/\text{ SiO }_{2}/\text{ Si }\) RRAM device for high-density integration and low-power applications. J. Nanosci. Nanotechnol. 16, 10 (2016)

    Google Scholar 

  25. Yang, Y., Gao, P., Gaba, S., Chang, T., Pan, X., Lu, W.: Observation of conducting filament growth in nanoscale resistive memories. Nat. Commun. 3, 732 (2012)

    Article  Google Scholar 

  26. Krishnan, K., Tsuruoka, T., Aono, M.: Direct observation of anodic dissolution and filament growth behavior in polyethylene-oxide-based atomic switch structures. Jpn. J. Appl. Phys. 55, 06GK02 (2016)

    Article  Google Scholar 

  27. Kim, S., Jung, S., Kim, M.-H., Cho, S., Park, B.-G.: Gradual bipolar resistive switching in \(\text{ Ni }/\text{ Si }_{3}\text{ N }_{4}/\text{ n }^{+}\text{-Si }\) resistive-switching memory device for high-density integration and low-power applications. Appl. Phys. Lett. 106, 212106 (2015)

    Article  Google Scholar 

  28. Kim, S., Park, B.-G.: Improved multi-level capability in \(\text{ Si }_{3}\text{ N }_{4}\)-based resistive switching memory using continuous gradual reset switching. J. Phys. D Appl. Phys. 50, 2 (2016)

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (2015R1A2A1A01007307) and in part by the Brain Korea 21 Plus Project in 2017.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Byung-Gook Park.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, MH., Kim, S., Ryoo, KC. et al. Circuit-level simulation of resistive-switching random-access memory cross-point array based on a highly reliable compact model. J Comput Electron 17, 273–278 (2018). https://doi.org/10.1007/s10825-017-1116-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10825-017-1116-2

Keywords

Navigation