Skip to main content
SpringerLink
Log in

Emerging memristors and applications in reservoir computing

  • Topical Review
  • Published:
Frontiers of Physics Aims and scope Submit manuscript

Abstract

Recently, with the emergence of ChatGPT, the field of artificial intelligence has garnered widespread attention from various sectors of society. Reservoir Computing (RC) is a neuromorphic computing algorithm used to analyze time-series data. Unlike traditional artificial neural networks that require the weight values of all nodes in the trained network, RC only needs to train the readout layer. This makes the training process faster and more efficient, and it has been used in various applications, including speech recognition, image classification, and control systems. Its flexibility and efficiency make it a popular choice for processing large amounts of complex data. A recent research trend is to develop physical RC, which utilizes the nonlinear dynamic and short-term memory properties of physical systems (photonic modules, spintronic devices, memristors, etc.) to construct a fixed random neural network structure for processing input data to reduce computing time and energy. In this paper, we introduced the recent development of memristors and demonstrated the remarkable data processing capability of RC systems based on memristors. Not only do they possess excellent data processing ability comparable to digital RC systems, but they also have lower energy consumption and greater robustness. Finally, we discussed the development prospects and challenges faced by memristors-based RC systems.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References and note

  1. J. Misra and I. Saha, Artificial neural networks in hardware: A survey of two decades of progress, Neurocomputing 74(1–3), 239 (2010)

    Article  Google Scholar 

  2. Z. Liu, J. Tang, B. Gao, X. Li, P. Yao, Y. Lin, D. Liu, B. Hong, H. Qian, and H. Wu, Multichannel parallel processing of neural signals in memristor arrays, Sci. Adv. 6(41), eabc4797 (2020)

    Article  ADS  Google Scholar 

  3. W. Wang and G. Zhou, Moisture influence in emerging neuromorphic device, Front. Phys. 18(5), 53601 (2023)

    Article  ADS  Google Scholar 

  4. S. Ke, L. Jiang, Y. Zhao, Y. Xiao, B. Jiang, G. Cheng, F. Wu, G. Cao, Z. Peng, M. Zhu, and C. Ye, Brainlike synaptic memristor based on lithium-doped silicate for neuromorphic computing, Front. Phys. 17(5), 53508 (2022)

    Article  ADS  Google Scholar 

  5. J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, Proc. Natl. Acad. Sci. USA 79(8), 2554 (1982)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  6. P. J. Werbos, Backpropagation through time: What it does and how to do it, Proc. IEEE 78(10), 1550 (1990)

    Article  Google Scholar 

  7. M. Lukoševičius and H. Jaeger, Reservoir computing approaches to recurrent neural network training, Comput. Sci. Rev. 3(3), 127 (2009)

    Article  MATH  Google Scholar 

  8. H. Jaeger, The “echo state” approach to analysing and training recurrent neural networks–with an Erratum note

  9. P. Antonik, F. Duport, M. Hermans, A. Smerieri, M. Haelterman, and S. Massar, Online training of an optoelectronic reservoir computer applied to real-time channel equalization, IEEE Trans. Neural Netw. Learn. Syst. 28(11), 2686 (2017)

    Article  Google Scholar 

  10. J. Lao, M. Yan, B. Tian, C. Jiang, C. Luo, Z. Xie, Q. Zhu, Z. Bao, N. Zhong, X. Tang, L. Sun, G. Wu, J. Wang, H. Peng, J. Chu, and C. Duan, Ultralow-power machine vision with self-powered sensor reservoir, Adv. Sci. (Weinh.) 9(15), 2106092 (2022)

    Google Scholar 

  11. M. Zhang, Z. Liang, and Z. R. Huang, Hardware optimization for photonic time-delay reservoir computer dynamics, Neuromorph. Comput. Eng. 3(1), 014008 (2023)

    Article  Google Scholar 

  12. R. Nakane, G. Tanaka, and A. Hirose, Reservoir computing with spin waves excited in a garnet film, IEEE Access 6, 4462 (2018)

    Article  Google Scholar 

  13. A. Papp, G. Csaba, and W. Porod, Characterization of nonlinear spin-wave interference by reservoir-computing metrics, Appl. Phys. Lett. 119(11), 112403 (2021)

    Article  ADS  Google Scholar 

  14. J. Moon, W. Ma, J. H. Shin, F. Cai, C. Du, S. H. Lee, and W. D. Lu, Temporal data classification and forecasting using a memristor-based reservoir computing system, Nat. Electron. 2(10), 480 (2019)

    Article  Google Scholar 

  15. H. Coy, R. Cabrera, N. Sepulveda, and F. E. Fernández, Optoelectronic and all-optical multiple memory states in vanadium dioxide, J. Appl. Phys. 108(11), 113115 (2010)

    Article  ADS  Google Scholar 

  16. K. Liu, C. Cheng, J. Suh, R. Tang-Kong, D. Fu, S. Lee, J. Zhou, L. O. Chua, and J. Wu, Powerful, multifunctional torsional micromuscles activated by phase transition, Adv. Mater. 26(11), 1746 (2014)

    Article  Google Scholar 

  17. W. Yi, K. K. Tsang, S. K. Lam, X. Bai, J. A. Crowell, and E. A. Flores, Biological plausibility and stochasticity in scalable VO2 active memristor neurons, Nat. Commun. 9(1), 4661 (2018)

    Article  ADS  Google Scholar 

  18. M. Ismail, H. Abbas, C. Choi, and S. Kim, Controllable analog resistive switching and synaptic characteristics in ZrO2/ZTO bilayer memristive device for neuromorphic systems, Appl. Surf. Sci. 529, 147107 (2020)

    Article  Google Scholar 

  19. M. Ismail, H. Abbas, C. Choi, and S. Kim, Stabilized and RESET-voltage controlled multi-level switching characteristics in ZrO2-based memristors by inserting a-ZTO interface layer, J. Alloys Compd. 835, 155256 (2020)

    Article  Google Scholar 

  20. S. G. Hu, Y. Liu, T. P. Chen, Z. Liu, Q. Yu, L. J. Deng, Y. Yin, and S. Hosaka, Emulating the Ebbinghaus forgetting curve of the human brain with a NiO-based memristor, Appl. Phys. Lett. 103(13), 133701 (2013)

    Article  ADS  Google Scholar 

  21. Y. Li, J. Chu, W. Duan, G. Cai, X. Fan, X. Wang, G. Wang, and Y. Pei, Analog and digital bipolar resistive switching in solution-combustion-processed NiO memristor, ACS Appl. Mater. Interfaces 10(29), 24598 (2018)

    Article  Google Scholar 

  22. V. Q. Le, T. H. Do, J. R. D. Retamal, P. W. Shao, Y. H. Lai, W. W. Wu, J. H. He, Y. L. Chueh, and Y. H. Chu, Van der Waals heteroepitaxial AZO/NiO/AZO/muscovite (ANA/muscovite) transparent flexible memristor, Nano Energy 56, 322 (2019)

    Article  Google Scholar 

  23. L. Zhang, Z. Tang, J. Fang, X. Jiang, Y. P. Jiang, Q. J. Sun, J. M. Fan, X. G. Tang, and G. Zhong, Synaptic and resistive switching behaviors in NiO/Cu2O hetero-junction memristor for bioinspired neuromorphic computing, Appl. Surf. Sci. 606, 154718 (2022)

    Article  Google Scholar 

  24. T. Chang, S. H. Jo, K. H. Kim, P. Sheridan, S. Gaba, and W. Lu, Synaptic behaviors and modeling of a metal oxide memristive device, Appl. Phys. A 102(4), 857 (2011)

    Article  ADS  Google Scholar 

  25. J. Moon, W. Ma, J. H. Shin, F. Cai, C. Du, S. H. Lee, and W. D. Lu, Temporal data classification and forecasting using a memristor-based reservoir computing system, Nat. Electron. 2(10), 480 (2019)

    Article  Google Scholar 

  26. J. Shin, M. Kang, and S. Kim, Gradual conductance modulation of Ti/WOx/Pt memristor with self-rectification for a neuromorphic system, Appl. Phys. Lett. 119(1), 012102 (2021)

    Article  ADS  Google Scholar 

  27. Y. Tao, Z. Wang, H. Xu, W. Ding, X. Zhao, Y. Lin, and Y. Liu, Moisture-powered memristor with interfacial oxygen migration for power-free reading of multiple memory states, Nano Energy 71, 104628 (2020)

    Article  Google Scholar 

  28. L. Zhang, Z. Tang, D. Yao, Z. Fan, S. Hu, Q. J. Sun, X. G. Tang, Y. P. Jiang, X. Guo, M. Huang, G. Zhong, and J. Gao, Synaptic behaviors in flexible Au/WOx/Pt/mica memristor for neuromorphic computing system, Mater. Today Phys. 23, 100650 (2022)

    Article  Google Scholar 

  29. C. H. Huang, J. S. Huang, S. M. Lin, W. Y. Chang, J. H. He, and Y. L. Chueh, ZnO1–x nanorod arrays/ZnO thin film bilayer structure: from homojunction diode and high-performance memristor to complementary 1D1R application, ACS Nano 6(9), 8407 (2012)

    Article  Google Scholar 

  30. J. Park, S. Lee, J. Lee, and K. Yong, A light incident angle switchable ZnO nanorod memristor: Reversible switching behavior between two non-volatile memory devices, Adv. Mater. 25(44), 6423 (2013)

    Article  Google Scholar 

  31. A. Kumar, M. Das, V. Garg, B. S. Sengar, M. T. Htay, S. Kumar, A. Kranti, and S. Mukherjee, Forming-free high-endurance Al/ZnO/Al memristor fabricated by dual ion beam sputtering, Appl. Phys. Lett. 110(25), 253509 (2017)

    Article  ADS  Google Scholar 

  32. S. Dirkmann, J. Kaiser, C. Wenger, and T. Mussen-brock, Filament growth and resistive switching in hafnium oxide memristive devices, ACS Appl. Mater. Interfaces 10(17), 14857 (2018)

    Article  Google Scholar 

  33. B. Ku, Y. Abbas, S. Kim, A. S. Sokolov, Y. R. Jeon, and C. Choi, Improved resistive switching and synaptic characteristics using Ar plasma irradiation on the Ti/HfO2 interface, J. Alloys Compd. 797, 277 (2019)

    Article  Google Scholar 

  34. G. S. Kim, H. Song, Y. K. Lee, J. H. Kim, W. Kim, T. H. Park, H. J. Kim, K. Min Kim, and C. S. Hwang, Defect-engineered electroforming-free analog HfOx memristor and its application to the neural network, ACS Appl. Mater. Interfaces 11(50), 47063 (2019)

    Article  Google Scholar 

  35. M. J. Lee, C. B. Lee, D. Lee, S. R. Lee, M. Chang, J. H. Hur, Y. B. Kim, C. J. Kim, D. H. Seo, S. Seo, U. I. Chung, I. K. Yoo, and K. Kim, A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O5–x/TaO2–x bilayer structures, Nat. Mater. 10(8), 625 (2011)

    Article  ADS  Google Scholar 

  36. J. Joshua Yang, M. X. Zhang, M. D. Pickett, F. Miao, J. Paul Strachan, W. D. Li, W. Yi, D. A. A. Ohlberg, B. Joon Choi, W. Wu, J. H. Nickel, G. Medeiros-Ribeiro, and R. S. Williams, Engineering nonlinearity into memristors for passive crossbar applications, Appl. Phys. Lett. 100(11), 113501 (2012)

    Article  ADS  Google Scholar 

  37. F. Miao, W. Yi, I. Goldfarb, J. J. Yang, M. X. Zhang, M. D. Pickett, J. P. Strachan, G. Medeiros-Ribeiro, and R. S. Williams, Continuous electrical tuning of the chemical composition of TaOx-based memristors, ACS Nano 6(3), 2312 (2012)

    Article  Google Scholar 

  38. Z. Wang, M. Yin, T. Zhang, Y. Cai, Y. Wang, Y. Yang, and R. Huang, Engineering incremental resistive switching in TaOx based memristors for brain-inspired computing, Nanoscale 8(29), 14015 (2016)

    Article  ADS  Google Scholar 

  39. L. H. Li, K. H. Xue, L. Q. Zou, J. H. Yuan, H. Sun, and X. Miao, Multilevel switching in Mg-doped HfOx memristor through the mutual-ion effect, Appl. Phys. Lett. 119(15), 153505 (2021)

    Article  ADS  Google Scholar 

  40. J. H. Ryu, C. Mahata, and S. Kim, Long-term and short-term plasticity of Ta2O5/HfO2 memristor for hardware neuromorphic application, J. Alloys Compd. 850, 156675 (2021)

    Article  Google Scholar 

  41. A. Saleem, F. M. Simanjuntak, S. Chandrasekaran, S. Rajasekaran, T. Y. Tseng, and T. Prodromakis, Transformation of digital to analog switching in TaOx-based memristor device for neuromorphic applications, Appl. Phys. Lett. 118(11), 112103 (2021)

    Article  ADS  Google Scholar 

  42. L. Du, Z. Wang, and G. Zhao, Novel intelligent devices: Two-dimensional materials based memristors, Front. Phys. 17(2), 23602 (2022)

    Article  ADS  Google Scholar 

  43. Z. Zhou, F. Yang, S. Wang, L. Wang, X. Wang, C. Wang, Y. Xie, and Q. Liu, Emerging of two-dimensional materials in novel memristor, Front. Phys. 17(2), 23204 (2022)

    Article  ADS  Google Scholar 

  44. Y. T. Chan, Y. Fu, L. Yu, F. Y. Wu, H. W. Wang, T. H. Lin, S. H. Chan, M. C. Wu, and J. C. Wang, Compacted self-assembly graphene with hydrogen plasma surface modification for robust artificial electronic synapses of gadolinium oxide memristors, Adv. Mater. Interfaces 7(20), 2000860 (2020)

    Article  Google Scholar 

  45. X. Zhao, J. Ma, X. Xiao, Q. Liu, L. Shao, D. Chen, S. Liu, J. Niu, X. Zhang, Y. Wang, R. Cao, W. Wang, Z. Di, H. Lv, S. Long, and M. Liu, Breaking the current-retention dilemma in cation-based resistive switching devices utilizing graphene with controlled defects, Adv. Mater. 30(14), 1705193 (2018)

    Article  Google Scholar 

  46. J. Lee, C. Du, K. Sun, E. Kioupakis, and W. D. Lu, Tuning ionic transport in memristive devices by graphene with engineered nanopores, ACS Nano 10(3), 3571 (2016)

    Article  Google Scholar 

  47. M. Naqi, et al., Multilevel artificial electronic synaptic device of direct grown robust MoS2 based memristor array for in-memory deep neural network, npj 2D Mater. Appl. 6, 53 (2022)

    Article  Google Scholar 

  48. X. Yan, Q. Zhao, A. P. Chen, J. Zhao, Z. Zhou, J. Wang, H. Wang, L. Zhang, X. Li, Z. Xiao, K. Wang, C. Qin, G. Wang, Y. Pei, H. Li, D. Ren, J. Chen, and Q. Liu, Vacancy-induced synaptic behavior in 2D WS2 nanosheet-based memristor for low-power neuromorphic computing, Small 15(24), 1901423 (2019)

    Article  Google Scholar 

  49. Z. Xie, Y. Duo, Z. Lin, T. Fan, C. Xing, L. Yu, R. Wang, M. Qiu, Y. Zhang, Y. Zhao, X. Yan, and H. Zhang, The rise of 2D photothermal materials beyond graphene for clean water production, Adv. Sci. (Weinh.) 7(5), 1902236 (2020)

    Google Scholar 

  50. S. Manzeli, D. Ovchinnikov, D. Pasquier, O. V. Yazyev, and A. Kis, 2D transition metal dichalco-genides, Nat. Rev. Mater. 2(8), 17033 (2017)

    Article  ADS  Google Scholar 

  51. Y. Shi, X. Liang, B. Yuan, V. Chen, H. Li, F. Hui, Z. Yu, F. Yuan, E. Pop, H. S. P. Wong, and M. Lanza, Electronic synapses made of layered two-dimensional materials, Nat. Electron. 1(8), 458 (2018)

    Article  Google Scholar 

  52. C. Moreno, C. Munuera, S. Valencia, F. Kronast, X. Obradors, and C. Ocal, Reversible resistive switching and multilevel recording in La0.7Sr0.3MnO3 thin films for low cost nonvolatile memories, Nano Lett. 10(10), 3828 (2010)

    Article  ADS  Google Scholar 

  53. D. Liu, N. Wang, G. Wang, Z. Shao, X. Zhu, C. Zhang, and H. Cheng, Nonvolatile bipolar resistive switching in amorphous Sr-doped LaMnO3 thin films deposited by radio frequency magnetron sputtering, Appl. Phys. Lett. 102(13), 134105 (2013)

    Article  ADS  Google Scholar 

  54. D. Liu, H. Cheng, X. Zhu, G. Wang, and N. Wang, Analog memristors based on thickening/thinning of Ag nanofilaments in amorphous manganite thin films, ACS Appl. Mater. Interfaces 5(21), 11258 (2013)

    Article  Google Scholar 

  55. N. Lee, Y. Lansac, H. Hwang, and Y. H. Jang, Switching mechanism of Al/La1–xSrxMnO3 resistance random access memory. I. Oxygen vacancy formation in perovskites, RSC Advances 5(124), 102772 (2015)

    Article  ADS  Google Scholar 

  56. K. Szot, W. Speier, G. Bihlmayer, and R. Waser, Switching the electrical resistance of individual dislocations in single-crystalline SrTiO3, Nat. Mater. 5(4), 312 (2006)

    Article  ADS  Google Scholar 

  57. Z. Hu, Q. Li, M. Li, Q. Wang, Y. Zhu, X. Liu, X. Zhao, Y. Liu, and S. Dong, Ferroelectric memristor based on Pt/BiFeO3/Nb-doped SrTiO3 heterostructure, Appl. Phys. Lett. 102(10), 102901 (2013)

    Article  ADS  Google Scholar 

  58. F. Messerschmitt, M. Kubicek, S. Schweiger, and J. L. M. Rupp, Memristor kinetics and diffusion characteristics for mixed anionic-electronic SrTiO3–δ bits: The memris-tor-based cottrell analysis connecting material to device performance, Adv. Funct. Mater. 24(47), 7448 (2014)

    Article  Google Scholar 

  59. Z. H. Shen, W. H. Li, X. G. Tang, J. Hu, K. Y. Wang, Y. P. Jiang, and X. B. Guo, An artificial synapse based on Sr(Ti, Co)O3 films, Mater. Today Commun. 33, 104754 (2022)

    Article  Google Scholar 

  60. X. Yan, X. Han, Z. Fang, Z. Zhao, Z. Zhang, J. Sun, Y. Shao, Y. Zhang, L. Wang, S. Sun, Z. Guo, X. Jia, Y. Zhang, Z. Guan, and T. Shi, Reconfigurable memristor based on SrTiO3 thin-film for neuromorphic computing, Front. Phys. 18(6), 63301 (2023)

    Article  ADS  Google Scholar 

  61. J. Q. Yang, R. Wang, Z. P. Wang, Q. Y. Ma, J. Y. Mao, Y. Ren, X. Yang, Y. Zhou, and S. T. Han, Leaky integrate-and-fire neurons based on perovskite memristor for spiking neural networks, Nano Energy 74, 104828 (2020)

    Article  Google Scholar 

  62. L. Wang, J. Sun, Y. Zhang, J. Niu, Z. Zhao, Z. Guo, Z. Zhang, Y. Shao, S. Sun, X. Jia, X. Han, and X. Yan, Ferroelectric memristor based on Li-doped BiFeO3 for information processing, Appl. Phys. Lett. 121(24), 241901 (2022)

    Article  ADS  Google Scholar 

  63. F. Luo, W. M. Zhong, X. G. Tang, J. Y. Chen, Y. P. Jiang, and Q. X. Liu, Application of artificial synapse based on all-inorganic perovskite memristor in neuro-morphic computing, Nano Mater. Sci., S258996512300003X (2023)

  64. W. M. Zhong, X. G. Tang, L. L. Bai, J. Y. Chen, H. F. Dong, Q. J. Sun, Y. P. Jiang, and Q. X. Liu, A halide perovskite thin film diode with modulated depletion layers for artificial synapse, J. Alloys Compd. 960, 170773 (2023)

    Article  Google Scholar 

  65. F. Ye, X. G. Tang, J. Y. Chen, W. M. Zhong, L. Zhang, Y. P. Jiang, and Q. X. Liu, Neurosynaptic-like behavior of Ce-doped BaTiO3 ferroelectric thin film diodes for visual recognition applications, Appl. Phys. Lett. 121(17), 171901 (2022)

    Article  ADS  Google Scholar 

  66. W. M. Zhong, X. G. Tang, Q. X. Liu, and Y. P. Jiang, Artificial optoelectronic synaptic characteristics of Bi2FeMnO6 ferroelectric memristor for neuromorphic computing, Mater. Des. 222, 111046 (2022)

    Article  Google Scholar 

  67. R. Su, R. Xiao, C. Shen, D. Song, J. Chen, B. Zhou, W. Cheng, Y. Li, X. Wang, and X. Miao, Oxygen ion migration induced polarity switchable SrFeOx memristor for high-precision handwriting recognition, Appl. Surf. Sci. 617, 156620 (2023)

    Article  Google Scholar 

  68. D. A. Lapkin, A. V. Emelyanov, V. A. Demin, V. V. Erokhin, L. A. Feigin, P. K. Kashkarov, and M. V. Kovalchuk, Polyaniline-based memristive microdevice with high switching rate and endurance, Appl. Phys. Lett. 112(4), 043302 (2018)

    Article  ADS  Google Scholar 

  69. D. A. Lapkin, A. V. Emelyanov, V. A. Demin, T. S. Berzina, and V. V. Erokhin, Spike-timing-dependent plasticity of polyaniline-based memristive element, Microelectron. Eng. 185–186, 43 (2018)

    Article  Google Scholar 

  70. Y. Gerasimov, E. Zykov, N. Prudnikov, M. Talanov, A. Toschev, and V. Erokhin, On the organic memristive device resistive switching efficacy, Chaos Solitons Fractals 143, 110549 (2021)

    Article  MathSciNet  Google Scholar 

  71. S. Li, F. Zeng, C. Chen, H. Liu, G. Tang, S. Gao, C. Song, Y. Lin, F. Pan, and D. Guo, Synaptic plasticity and learning behaviours mimicked through Ag interface movement in an Ag/conducting polymer/Ta memristive system, J. Mater. Chem. C 1(34), 5292 (2013)

    Article  Google Scholar 

  72. S. Ali, J. Bae, K. H. Choi, C. H. Lee, Y. H. Doh, S. Shin, and N. P. Kobayashi, Organic non-volatile memory cell based on resistive elements through elec-tro-hydrodynamic technique, Org. Electron. 17, 121 (2015)

    Article  Google Scholar 

  73. V. C. Nguyen and P. S. Lee, Coexistence of write once read many memory and memristor in blend of Poly(3, 4-ethylenedioxythiophene): Polystyrene sulfonate and polyvinyl alcohol, Sci. Rep. 6(1), 38816 (2016)

    Article  ADS  Google Scholar 

  74. L. P. Ma, J. Liu, and Y. Yang, Organic electrical bistable devices and rewritable memory cells, Appl. Phys. Lett. 80(16), 2997 (2002)

    Article  ADS  Google Scholar 

  75. M. Kano, S. Orito, Y. Tsuruoka, and N. Ueno, Nonvolatile memory effect of an Al/2-Amino-4, 5-dicyanoimidazole/Al structure, Synth. Met. 153(1–3), 265 (2005)

    Article  Google Scholar 

  76. M. Terai, K. Fujita, and T. Tsutsui, Electrical bistability of organic thin-film device using Ag electrode, Jpn. J. Appl. Phys. 45(4B), 3754 (2006)

    Article  ADS  Google Scholar 

  77. Y. Zhao, W. J. Sun, J. Wang, J. H. He, H. Li, Q. F. Xu, N. J. Li, D. Y. Chen, and J. M. Lu, All-inorganic ionic polymer-based memristor for high-performance and flexible artificial synapse, Adv. Funct. Mater. 30(39), 2004245 (2020)

    Article  Google Scholar 

  78. J. Li, Y. Qian, W. Li, Y. H. Lin, H. Qian, T. Zhang, K. Sun, J. Wang, J. Zhou, Y. Chen, J. Zhu, G. Zhang, M. Yi, and W. Huang, Humidity-enabled organic artificial synaptic devices with ultrahigh moisture resistivity, Adv. Electron. Mater. 8(10), 2200320 (2022)

    Article  Google Scholar 

  79. Y. Park and J. S. Lee, Artificial synapses with short-and long-term memory for spiking neural networks based on renewable materials, ACS Nano 11(9), 8962 (2017)

    Article  Google Scholar 

  80. W. M. Zhong, C. L. Luo, X. G. Tang, X. B. Lu, and J. Y. Dai, Dynamic FET-based memristor with relaxor antiferroelectric HfO2 gate dielectric for fast reservoir computing, Materials Today Nano 23, 100357 (2023)

    Article  Google Scholar 

  81. E. Choi, A. Schuetz, W. F. Stewart, and J. Sun, Using recurrent neural network models for early detection of heart failure onset, J. Am. Med. Inform. Assoc. 24(2), 361 (2017)

    Article  Google Scholar 

  82. W. Maass, T. Natschläger, and H. Markram, Real-time computing without stable states: A new framework for neural computation based on perturbations, Neural Comput. 14(11), 2531 (2002)

    Article  MATH  Google Scholar 

  83. G. Zhang, Z. Y. Xiong, Y. Gong, Z. Zhu, Z. Lv, Y. Wang, J. Q. Yang, X. Xing, Z. P. Wang, J. Qin, Y. Zhou, and S. T. Han, Polyoxometalate accelerated cationic migration for reservoir computing, Adv. Funct. Mater. 32(45), 2204721 (2022)

    Article  Google Scholar 

  84. C. Du, F. Cai, M. A. Zidan, W. Ma, S. H. Lee, and W. D. Lu, Reservoir computing using dynamic memristors for temporal information processing, Nat. Commun. 8(1), 2204 (2017)

    Article  ADS  Google Scholar 

  85. G. Milano, G. Pedretti, K. Montano, S. Ricci, S. Hashemkhani, L. Boarino, D. Ielmini, and C. Ricciardi, In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks, Nat. Mater. 21(2), 195 (2022)

    Article  ADS  Google Scholar 

  86. A. N. Matsukatova, N. V. Prudnikov, V. A. Kulagin, S. Battistoni, A. A. Minnekhanov, A. D. Trofimov, A. A. Nesmelov, S. A. Zavyalov, Y. N. Malakhova, M. Parmeggiani, A. Ballesio, S. L. Marasso, S. N. Chvalun, V. A. Demin, A. V. Emelyanov, and V. Erokhin, Combination of organic-based reservoir computing and spiking neuromorphic systems for a robust and efficient pattern classification, Adv. Intell. Syst. 5(6), 2200407 (2023)

    Article  Google Scholar 

  87. N. V. Prudnikov, V. A. Kulagin, S. Battistoni, V. A. Demin, V. V. Erokhin, and A. V. Emelyanov, Polyaniline-based memristive devices as key elements of robust reservoir computing for image classification, Phys. Status Solidi A 220(11), 2200700 (2023) (a)

    Article  ADS  Google Scholar 

  88. A. A. Koroleva, D. S. Kuzmichev, M. G. Kozodaev, I. V. Zabrosaev, E. V. Korostylev, and A. M. Markeev, CMOS-compatible self-aligned 3D memristive elements for reservoir computing systems, Appl. Phys. Lett. 122(2), 022905 (2023)

    Article  ADS  Google Scholar 

  89. L. Appeltant, M. C. Soriano, G. Van der Sande, J. Danckaert, S. Massar, J. Dambre, B. Schrauwen, C. R. Mirasso, and I. Fischer, Information processing using a single dynamical node as complex system, Nat. Commun. 2(1), 468 (2011)

    Article  ADS  Google Scholar 

  90. L. Appeltant, G. Van der Sande, J. Danckaert, and I. Fischer, Constructing optimized binary masks for reservoir computing with delay systems, Sci. Rep. 4(1), 3629 (2014)

    Article  ADS  Google Scholar 

  91. Y. Zhong, J. Tang, X. Li, B. Gao, H. Qian, and H. Wu, Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing, Nat. Commun. 12(1), 408 (2021)

    Article  ADS  Google Scholar 

  92. Y. Zhong, J. Tang, X. Li, X. Liang, Z. Liu, Y. Li, Y. Xi, P. Yao, Z. Hao, B. Gao, H. Qian, and H. Wu, A memristor-based analogue reservoir computing system for real-time and power-efficient signal processing, Nat. Electron.. 5(10), 672 (2022)

    Article  Google Scholar 

  93. X. Zhu, Q. Wang, and W. D. Lu, Memristor networks for real-time neural activity analysis, Nat. Commun. 11(1), 2439 (2020)

    Article  ADS  Google Scholar 

  94. Y. Yang, H. Cui, S. Ke, M. Pei, K. Shi, C. Wan, and Q. Wan, Reservoir computing based on electric-double-layer coupled InGaZnO artificial synapse, Appl. Phys. Lett. 122(4), 043508 (2023)

    Article  ADS  Google Scholar 

  95. L. Jaurigue and K. Lüdge, Connecting reservoir computing with statistical forecasting and deep neural networks, Nat. Commun. 13(1), 227 (2022)

    Article  ADS  Google Scholar 

  96. E. Bollt, On explaining the surprising success of reservoir computing forecaster of chaos? The universal machine learning dynamical system with contrast to VAR and DMD Chaos 31(1), 013108 (2021)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  97. L. Gonon and J. P. Ortega, Reservoir computing universality with stochastic inputs, IEEE Trans. Neural Netw. Learn. Syst. 31(1), 100 (2020)

    Article  MathSciNet  Google Scholar 

  98. A. G. Hart, J. L. Hook, and J. H. P. Dawes, Echo State Networks trained by Tikhonov least squares are L2(μ) approximators of ergodic dynamical systems, Physica D 421, 132882 (2021)

    Article  MATH  Google Scholar 

  99. D. J. Gauthier, E. Bollt, A. Griffith, and W. A. S. Barbosa, Next generation reservoir computing, Nat. Commun. 12(1), 5564 (2021)

    Article  ADS  Google Scholar 

  100. K. Ren, W. Y. Zhang, F. Wang, Z. Y. Guo, and D. S. Shang, Next-generation reservoir computing based on memristor array, Acta Physica Sinica 71(14), 140701 (2022)

    Article  Google Scholar 

Download references

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (Grant Nos. 11574057 and 12172093) and the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2021A1515012607).

Author information

Authors and Affiliations

  1. School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, China

    Hao Chen, Xin-Gui Tang, Zhihao Shen, Wen-Tao Guo, Qi-Jun Sun, Zhenhua Tang & Yan-Ping Jiang

Authors
  1. Hao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xin-Gui Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhihao Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wen-Tao Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qi-Jun Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhenhua Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yan-Ping Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin-Gui Tang.

Ethics declarations

Declarations The authors declare that they have no competing interests and there are no conflicts.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, H., Tang, XG., Shen, Z. et al. Emerging memristors and applications in reservoir computing. Front. Phys. 19, 13401 (2024). https://doi.org/10.1007/s11467-023-1335-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11467-023-1335-x

Keywords

Search

Navigation