Skip to main content
Log in

The FAPbI3 perovskite memristor with a PMMA passivation layer as an artificial synapse

  • Published:
Applied Physics A Aims and scope Submit manuscript

Abstract

Memristors have received widespread attention as a new type of nonvolatile memory device, which are promising to mimic synapse dynamics efficiently. In this work, a memristor with the structure of PMMA/Ag/FAPbI3/FTO was fabricated and memristive behavior was investigated. With PMMA as the passivation layer in this structure, the performance as well as stability of the FAPbI3 memristor was significantly improved, as compared with non-passivated device. The results show that the device with passivation layer has better stability in the air (20 days) and excellent artificial synaptic functions, such as spike timing-dependent plasticity (STDP), long-term potentiation (LTP) and long-term depression (LTD). This work demonstrates the great potential of PMMA-passivated perovskite memristor in neuromorphic computing.

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

Data availability

The data can be provided by the authors upon reasonable request.

References

  1. M. Davies et al., Loihi: a neuromorphic many core processor with on-chip learning. IEEE Micro 38(1), 82–99 (2018)

    Google Scholar 

  2. J.-W. Jang, S. Park, G.W. Burr, H. Hwang, Y.-H. Jeong, Optimization of conductance change in Pr1–xCaxMnO3-based synaptic devices for neuromorphic systems. IEEE Electron Device Lett. 36(5), 457–459 (2015)

    ADS  Google Scholar 

  3. G. Snider, R. Amerson, D. Carter, H. Abdalla, M.S. Qureshi, J. Léveillé, M. Versace, H. Ames, S. Patrick, B. Chandler, A. Gorchetchnikov, From synapses to circuitry: using memristive memory to explore the electronic brain. J. Comput. 44(2), 21–28 (2011)

    Google Scholar 

  4. G. Indiveri, B. Linares-Barranco, R. Legenstein, G. Deligeorgis, T. Prodromakis, Integration of nanoscale memristor synapses in neuromorphic computing architectures. Nanotechnology 24(38), 384010 (2013)

    ADS  Google Scholar 

  5. Y.V. Pershin, M. Di Ventra, Memcapacitive neural networks. Electronics Lett. 50(3), 141–143 (2014)

    ADS  Google Scholar 

  6. B.C. Jang et al., Polymer analog memristive synapse with atomic-scale conductive filament for flexible neuromorphic computing system. Nano Lett. 19(2), 839–849 (2019)

    ADS  Google Scholar 

  7. D. Kuzum, S. Yu, H.S. PhilipWong, Synaptic electronics: materials, devices and applications. Nanotechnology 24(38), 382001 (2013)

    Google Scholar 

  8. A.N. Matsukatova et al., Scalable nanocomposite parylene-based memristors: multifilamentary resistive switching and neuromorphic applications. Nano Res. 16, 3207–3214 (2022)

    ADS  Google Scholar 

  9. D.B. Strukov, G.S. Snider, D.R. Stewart, R.S. Williams, The missing memristor found. Nature 453(7191), 80–83 (2008)

    ADS  Google Scholar 

  10. Q. Huo et al., A computing-in-memory macro based on three-dimensional resistive random-access memory. Nat. Electron. 5(7), 469–477 (2022)

    Google Scholar 

  11. Q. Liu, S. Gao, L. Xu et al., Nanostructured perovskites for nonvolatile memory devices. Chem. Soc. Rev. 51, 3341–3379 (2022)

    Google Scholar 

  12. K. Qian, G. Cai, V.C. Nguyen, T. Chen, P.S. Lee, Direct observation of conducting filaments in tungsten oxide based transparent resistive switching memory. ACS Appl Mater Interfaces 8(41), 27885–27891 (2016)

    Google Scholar 

  13. J. Hou, S.S. Nonnenmann, W. Qin, D.A. Bonnel, Size dependence of resistive switching at nanoscale metal-oxide interfaces. Adv. Funct. Mater. 24(26), 4113–4118 (2014)

    Google Scholar 

  14. Z. Xiao, J. Huang, Energy-efficient hybrid perovskite memristors and synaptic devices. Adv. Electron. Mater. 2(7), 1600100 (2016)

    Google Scholar 

  15. W.-J. Yin, T. Shi, Y. Yan, Unusual defect physics in CH3NH3PbI3 perovskite solar cell absorber. Appl. Phys. Lett. 104(6), 063903 (2014)

    ADS  Google Scholar 

  16. H.-S. Kim et al., Lead Iodide perovskite sensitized all-solid-state submicron thin film mesoscopic solar cell with efficiency exceeding 9%. Sci. Rep. 2(1), 591 (2012)

    MathSciNet  Google Scholar 

  17. Y. Lv et al., High performance perovskite solar cells using TiO2 nanospindles as ultrathin mesoporous layer. J. Energy Chem. 27(4), 951–956 (2018)

    ADS  MathSciNet  Google Scholar 

  18. S.D. Stranks, H.J. Snaith, Metal-halide perovskites for photovoltaic and light-emitting devices. Nat. Nanotechnol. 10(5), 391–402 (2015)

    ADS  Google Scholar 

  19. H. Cho et al., Overcoming the electroluminescence efficiency limitations of perovskite light-emitting diodes. Science 350(6265), 1222 (2015)

    ADS  Google Scholar 

  20. E.J. Yoo, M. Lyu, J.H. Yun, C.J. Kang, Y.J. Choi, L. Wang, Resistive switching behavior in organic-inorganic Hybrid CH3NH3PbI3-xClx perovskite for resistive random access memory devices. Adv Mater 27(40), 6170–6175 (2015)

    Google Scholar 

  21. Y. Fang, S. Zhai, L. Chu, J. Zhong, Advances in halide perovskite memristor from lead-based to lead-free materials. ACS Appl Mater Interfaces 13(15), 17141–17157 (2021)

    Google Scholar 

  22. Y. Sun et al., Competition between metallic and vacancy defect conductive filaments in a CH3NH3PbI3-based memory device. J. Phys. Chem. C 122(11), 6431–6436 (2018)

    Google Scholar 

  23. K. Yan et al., a High-performance perovskite memristor based on methyl ammonium lead halides. J. Mater. Chem. C 4(7), 1375–1381 (2016)

    Google Scholar 

  24. G.E. Eperon, S.D. Stranks, C. Menelaou, M.B. Johnston, L.M. Herz, H.J. Snaith, Formamidinium lead trihalide: a broadly tunable perovskite for efficient planar heterojunction solar cells. Energy Environ. Sci. 7(3), 982 (2014)

    Google Scholar 

  25. T.M. Koh et al., Formamidinium-containing metal-halide: an alternative material for near-IR absorption perovskite solar cells. J. Phys. Chem. C 118(30), 16458–16462 (2013)

    Google Scholar 

  26. N. Pellet et al., Mixed-organic-cation perovskite photovoltaics for enhanced solar-light harvesting. Angew Chem Int Ed Engl 53(12), 3151–3157 (2014)

    Google Scholar 

  27. F. Zhou et al., Perovskite photovoltachromic supercapacitor with all-transparent electrodes. ACS Nano 10(6), 5900–5908 (2016)

    Google Scholar 

  28. J.S. Yun et al., Humidity-induced degradation via grain boundaries of HC(NH2)2PbI3 planar perovskite solar cells. Adv. Funct. Mater. 28(11), 1705363 (2018)

    Google Scholar 

  29. G. Divitini, S. Cacovich, F. Matteocci, L. Cinà, A. Di Carlo, C. Ducati, In situ observation of heat-induced degradation of perovskite solar cells. Nat. Energy 1(2), 1–6 (2016)

    Google Scholar 

  30. P. Zhang, M. Xia, F. Zhuge, Y. Zhou, Z. Wang, B. Dong, Y. Fu, K. Yang, Y. Li, Y. He, R.H. Scheicher, X.S. Miao, Nanochannel-based transport in an interfacial memristor can emulate the analog weight modulation of synapses. Nano Lett. 19(7), 4279–4286 (2019)

    ADS  Google Scholar 

  31. E. Yoo, M. Lyu, J.-H. Yun, C. Kang, Y. Choi, L. Wang, Bifunctional resistive switching behavior in an organolead halide perovskite based Ag/CH3NH3PbI3−xClx/FTO structure. J. Mater. Chem. C 4(33), 7824–7830 (2016)

    Google Scholar 

  32. M. Kund et al., In Conductive bridging RAM (CBRAM): an emerging non-volatile memory technology scalable to sub 20nm, IEEE International Electron Devices Meeting 2005.

  33. R. Waser, M.J.N.M. Aono, Nanoionics-based resistive switching memories. Nat. Mater. 6, 833–840 (2007)

    ADS  Google Scholar 

  34. S.H. Jo, W.J.N.L. Lu, CMOS compatible nanoscale nonvolatile resistance switching memory. Nano Lett. 8(2), 392–397 (2008)

    ADS  Google Scholar 

  35. S.H. Jo, K.H. Kim, W.J.N.L. Lu, Programmable resistance switching in nanoscale two-terminal devices. Nano Lett. 9(1), 496–500 (2009)

    ADS  Google Scholar 

  36. J.J. Yang, M.D. Pickett, X. Li, D.A. Ohlberg, D.R. Stewart, R.S. Williams, Memristive switching mechanism for metal/oxide/metal nanodevices. Nat Nanotechnol 3(7), 429–433 (2008)

    Google Scholar 

  37. S. Yu, Neuro-inspired computing with emerging nonvolatile memorys. Proc IEEE Inst Electr Electron Eng 106(2), 260–285 (2018)

    Google Scholar 

  38. D.O. Hebb, The Organization of Behavior A Neuropsychological Theory (Chapman & Hall, London, 2013)

    Google Scholar 

  39. S. Song, Competitive Hebbian learning through spike timing-dependent plasticity (STDP). Brandeis University (2002)

  40. G.Q. Bi, M.M.J. Poo, Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18(24), 10464–10472 (2012)

    Google Scholar 

  41. C. Du, W. Ma, T. Chang, P. Sheridan, W.D. Lu, Biorealistic implementation of synaptic functions with oxide memristors through internal ionic dynamics. Adv. Funct. Mater. 25(27), 4290–4299 (2015)

    Google Scholar 

  42. J.S. Han et al., Air-stable cesium lead iodide perovskite for ultra-low operating voltage resistive switching. Adv. Funct. Mater. 28(5), 1705783 (2018)

    Google Scholar 

  43. S. Ge et al., Silver iodide induced resistive switching in CsPbI3 perovskite-based memory device. Adv. Mater. Interfaces 6(7), 1802071 (2019)

    Google Scholar 

  44. J.D. Luo et al., Phase-dependent memristive behaviors in FAPbI3-based memristors. Mater. Today Commun. 33, 104186 (2022)

    Google Scholar 

  45. A.N. Matsukatova, A.V. Emelyanov, A.A. Minnekhanov, A.A. Nesmelov, A.Y. Vdovichenko, S.N. Chvalun, V.V. Rylkov, P.A. Forsh, V.A. Demin, P.K. Kashkarov, M.V. Kovalchuk, Resistive switching kinetics and second-order effects in parylene-based memristors. Appl. Phys. Lett. 117(24), 243501 (2020)

    ADS  Google Scholar 

  46. M. Prezioso, F. Merrikh Bayat, B. Hoskins, K. Likharev, D. Strukov, Self-adaptive spike-time-dependent plasticity of metal-oxide memristors. Sci. Rep. 6, 21331 (2016)

    ADS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhen Liu.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Y., Huang, H., Xu, C. et al. The FAPbI3 perovskite memristor with a PMMA passivation layer as an artificial synapse. Appl. Phys. A 129, 364 (2023). https://doi.org/10.1007/s00339-023-06632-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00339-023-06632-y

Keywords

Navigation