Skip to main content
SpringerLink
Log in

Titanium oxide artificial synaptic device: Nanostructure modeling and synthesis, memristive cross-bar fabrication, and resistive switching investigation

  • Research Article
  • Published:
Nano Research Aims and scope Submit manuscript

Abstract

The paper shows the results of the mathematical model development and the numerical simulation of the oxygen vacancies, and the distribution of TiO, Ti2O3, and TiO2 oxides in the titanium oxide nanostructure obtained by local anodic oxidation (anodization). The effect of the anodization voltage pulse duration and amplitude on the titanium oxide composition distribution and the conduction channel formation was shown. Synaptic device prototypes based on electrochemical titanium oxide are fabricated and investigated. It was shown that forming free resistive switching between the low resistances state (LRS) 1.43 ± 0.54 kΩ and the high resistance state (HRS) 28.75 ± 9.75 kΩ were observed during 100,000 switching cycles and LRS 1.49 ± 0.23 kΩ was maintained for 10,000 s. Multilevel resistive switching of the synaptic device prototype was investigated. It was shown that increasing Uset from 0.5 to 1.5 V leads to different LRS from 3.96 ± 0.19 to 0.71 ± 0.10 kΩ. The results obtained can be used in the development of technological foundations for the formation of high-performance multilevel artificial synapses for elements of neuroelectronics and hardware neural networks.

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

  1. Deng, L.; Li, G. Q.; Han, S.; Shi, L. P.; Xie, Y. Model compression and hardware acceleration for neural networks: A comprehensive survey. Proc. IEEE 2020, 108, 485–532.

    Google Scholar 

  2. Compagnoni, C. M.; Goda, A.; Spinelli, A. S.; Feeley, P.; Lacaita, A. L.; Visconti, A. Reviewing the evolution of the NAND flash technology. Proc. IEEE 2017, 105, 1609–1633.

    CAS  Google Scholar 

  3. Shalf, J. The future of computing beyond Moore’s law. Philos. Trans. A Math. Phys. Eng. Sci. 2020, 378, 20190061.

    Google Scholar 

  4. Lobov, S. A.; Zharinov, A. I.; Makarov, V. A.; Kazantsev, V. B. Spatial memory in a spiking neural network with robot embodiment. Sensors 2021, 21, 2678.

    Google Scholar 

  5. Radamson, H. H.; Zhu, H. L.; Wu, Z. H.; He, X. B.; Lin, H. X.; Liu, J. B.; Xiang, J. J.; Kong, Z. Z.; Xiong, W. J.; Li, J. J. et al. State of the art and future perspectives in advanced CMOS technology. Nanomaterials 2020, 10, 1555.

    CAS  Google Scholar 

  6. Cheng, J.; Wang, P. S.; Li, G.; Hu, Q. H.; Lu, H. Q. Recent advances in efficient computation of deep convolutional neural networks. Front. Inf. Technol. Electron. Eng. 2018, 19, 64–77.

    Google Scholar 

  7. Mittal, S. A survey of FPGA-based accelerators for convolutional neural networks. Neural Comput. Appl. 2020, 32, 1109–1139.

    Google Scholar 

  8. Tsai, H.; Ambrogio, S.; Narayanan, P.; Shelby, R. M.; Burr, G. W. Recent progress in analog memory-based accelerators for deep learning. J. Phys. D: Appl. Phys. 2018, 51, 283001.

    Google Scholar 

  9. Abadal, S.; Jain, A.; Guirado, R.; López-Alonso, J.; Alarcón, E. Computing graph neural networks: A survey from algorithms to accelerators. ACM Comput. Surv. 2022, 54, 191.

    Google Scholar 

  10. Ibrahim, Y.; Wang, H. B.; Liu, J. Y.; Wei, J. H.; Chen, L.; Rech, P.; Adam, K.; Guo, G. Soft errors in DNN accelerators: A comprehensive review. Microelectron. Reliab. 2020, 115, 113969.

    Google Scholar 

  11. Ghimire, D.; Kil, D.; Kim, S. H. A survey on efficient convolutional neural networks and hardware acceleration. Electronics 2022, 11, 945.

    Google Scholar 

  12. Makarov, V. A.; Lobov, S. A.; Shchanikov, S.; Mikhaylov, A.; Kazantsev, V. B. Toward reflective spiking neural networks exploiting memristive devices. Front. Comput. Neurosci. 2022, 16, 859874.

    Google Scholar 

  13. Voulodimos, A.; Doulamis, N.; Doulamis, A.; Protopapadakis, E. Deep learning for computer vision: A brief review. Comput. Intell. Neurosci. 2018, 2018, 7068349.

    Google Scholar 

  14. Bottou, L.; Curtis, F. E.; Nocedal, J. Optimization methods for large-scale machine learning. SIAM Rev. 2018, 60, 223–311.

    Google Scholar 

  15. Shrestha, A.; Mahmood, A. Review of deep learning algorithms and architectures. IEEE Access 2019, 7, 53040–53065.

    Google Scholar 

  16. Zhou, F. Y.; Jin, L. P.; Dong, J. Review of convolutional neural network. Chin. J. Comput. 2017, 40, 1229–1251.

    Google Scholar 

  17. Tavanaei, A.; Ghodrati, M.; Kheradpisheh, S. R.; Masquelier, T.; Maida, A. Deep learning in spiking neural networks. Neural Netw. 2019, 111, 47–63.

    Google Scholar 

  18. Kwon, O.; Kim, S.; Agudov, N.; Krichigin, A.; Mikhaylov, A.; Grimaudo, R.; Valenti, D.; Spagnolo, B. Non-volatile memory characteristics of a Ti/HfO2/Pt synaptic device with a crossbar array structure. Chaos, Solitons Fractals 2022, 162, 112480.

    Google Scholar 

  19. Li, S.; He, J. B.; Li, Y. M.; Rafique, M. U. Distributed recurrent neural networks for cooperative control of manipulators: A game-theoretic perspective. IEEE Trans. Neural Netw. Learn. Syst. 2017, 28, 415–426.

    Google Scholar 

  20. Islam, R.; Li, H. T.; Chen, P. Y.; Wan, W. E.; Chen, H. Y.; Gao, B.; Wu, H. Q.; Yu, S. M.; Saraswat, K.; Wong, H. S. P. Device and materials requirements for neuromorphic computing. J. Phys. D:Appl. Phys. 2019, 52, 113001.

    Google Scholar 

  21. Kim, J. H.; Ryu, J. R.; Lee, B.; Chae, U.; Son, J. W.; Park, B. H.; Sun, W. Interpreting the entire connectivity of individual neurons in micropatterned neural culture with an integrated connectome analyzer of a neuronal network (iCANN). Front. Neuroanat. 2021, 15, 746057.

    Google Scholar 

  22. Cheng, C. D.; Tiw, P. J.; Cai, Y. M.; Yan, X. Q.; Yang, Y. C.; Huang, R. In-memory computing with emerging nonvolatile memory devices. Sci. China Inf. Sci. 2021, 64, 221402.

    Google Scholar 

  23. Sun, B.; Zhou, G. D.; Xu, K.; Yu, Q.; Duan, S. K. Self-powered memory systems. ACS Materials Lett. 2020, 2, 1669–1690.

    CAS  Google Scholar 

  24. Sung, S. H.; Kim, T. J.; Shin, H.; Namkung, H.; Im, T. H.; Wang, H. S.; Lee, K. J. Memory-centric neuromorphic computing for unstructured data processing. Nano Res. 2021, 14, 3126–3142.

    CAS  Google Scholar 

  25. Han, J. K.; Yun, S. Y.; Lee, S. W.; Yu, J. M.; Choi, Y. K. A review of artificial spiking neuron devices for neural processing and sensing. Adv. Funct. Mater. 2022, 32, 2204102.

    CAS  Google Scholar 

  26. Liang, Y.; Lu, L. Q.; Jin, Y. C.; Xie, J. M.; Huang, R. R.; Zhang, J. S.; Lin, W. An efficient hardware design for accelerating sparse CNNs with NAS-based models. IEEE Trans. Comput.—Aided Des. Integr. Circuits Syst. 2022, 41, 597–613.

    Google Scholar 

  27. Guo, K. Y.; Han, S.; Yao, S.; Wang, Y.; Xie, Y.; Yang, H. Z. Software-hardware codesign for efficient neural network acceleration. IEEE Micro 2017, 37, 18–25.

    Google Scholar 

  28. Xiao, T. P.; Bennett, C. H.; Feinberg, B.; Agarwal, S.; Marinella, M. J. Analog architectures for neural network acceleration based on non-volatile memory. Appl. Phys. Rev. 2020, 7, 031301.

    CAS  Google Scholar 

  29. Kim, S.; Gokmen, T.; Lee, H. M.; Haensch, W. E. Analog CMOS-based resistive processing unit for deep neural network training. In 2017 IEEE 60thInternational Midwest Symposium on Circuits and Systems, Boston, USA, 2017, pp 422–425.

  30. Onen, M.; Butters, B. A.; Toomey, E.; Gokmen, T.; Berggren, K. K. Design and characterization of superconducting nanowire-based processors for acceleration of deep neural network training. Nanotechnology 2020, 31, 025204.

    CAS  Google Scholar 

  31. Zanotti, T.; Puglisi, F. M.; Pavan, P. Energy-efficient non-von Neumann computing architecture supporting multiple computing paradigms for logic and binarized neural networks. J. Low Power Electron. Appl. 2021, 11, 29.

    Google Scholar 

  32. Lee, M. J.; Lee, S.; Lee, S.; Balamurugan, K.; Yoon, C.; Jang, J. T.; Kim, S. H.; Kwon, D. H.; Kim, M.; Ahn, J. P. et al. Synaptic devices based on two-dimensional layered single-crystal chromium thiophosphate (CrPS4). NPG Asia Mater. 2018, 10, 23–30.

    CAS  Google Scholar 

  33. Zhu, J. D.; Zhang, T.; Yang, Y. C.; Huang, R. A comprehensive review on emerging artificial neuromorphic devices. Appl. Phys. Rev. 2020, 7, 011312.

    CAS  Google Scholar 

  34. Upadhyay, N. K.; Jiang, H.; Wang, Z. R.; Asapu, S.; Xia, Q. F.; Yang, J. J. Emerging memory devices for neuromorphic computing. Adv. Mater. Technol. 2019, 4, 1800589.

    Google Scholar 

  35. Ielmini, D.; Ambrogio, S. Emerging neuromorphic devices. Nanotechnology 2020, 31, 092001.

    CAS  Google Scholar 

  36. Lu, K. K.; Li, X. M.; Sun, Q. Q.; Pang, X. C.; Chen, J. Z.; Minari, T.; Liu, X. Y.; Song, Y. L. Solution-processed electronics for artificial synapses. Mater. Horiz. 2021, 8, 447–470.

    CAS  Google Scholar 

  37. Pan, X.; Jin, T. Y.; Gao, J.; Han, C.; Shi, Y. M.; Chen, W. et al. Stimuli-enabled artificial synapses for neuromorphic perception: Progress and perspectives. Small 2020, 16, 2001504.

    CAS  Google Scholar 

  38. Luo, L. Q. Architectures of neuronal circuits. Science 2021, 373, eabg7285.

    CAS  Google Scholar 

  39. Dai, S. L.; Zhao, Y. W.; Wang, Y.; Zhang, J. Y.; Fang, L.; Jin, S.; Shao, Y. L.; Huang, J. Recent advances in transistor-based artificial synapses. Adv. Func. Mater. 2019, 29, 1903700.

    CAS  Google Scholar 

  40. Han, H.; Yu, H. Y.; Wei, H. H.; Gong, J. D.; Xu, W. T. Recent progress in three-terminal artificial synapses: From device to system. Small 2019, 15, 1900695.

    Google Scholar 

  41. Ren, Y.; Yang, X. Y.; Zhou, L.; Mao, J. Y.; Han, S. T.; Zhou, Y. Recent advances in ambipolar transistors for functional applications. Adv. Funct. Mater. 2019, 29, 1902105.

    Google Scholar 

  42. Ling, H. F.; Koutsouras, D. A.; Kazemzadeh, S.; Van De Burgt, Y.; Yan, F.; Gkoupidenis, P. Electrolyte-gated transistors for synaptic electronics, neuromorphic computing, and adaptable biointerfacing. Appl. Phys. Rev. 2020, 7, 011307.

    CAS  Google Scholar 

  43. Shao, L.; Zhao, Y.; Liu, Y. Q. Organic synaptic transistors: The evolutionary path from memory cells to the application of artificial neural networks. Adv. Funct. Mater. 2021, 31, 2101951.

    CAS  Google Scholar 

  44. Sun, K. X.; Chen, J. S.; Yan, X. B. The future of memristors: Materials engineering and neural networks. Adv. Funct. Mater. 2021, 31, 2006773.

    CAS  Google Scholar 

  45. Wang, C. L.; Li, Y. Z.; Wang, Y. C.; Xu, X. D.; Fu, M. Y.; Liu, Y. Y.; Lin, Z. Q.; Ling, H. F.; Gkoupidenis, P.; Yi, M. D. et al. Thin-film transistors for emerging neuromorphic electronics: Fundamentals, materials, and pattern recognition. J. Mater. Chem. C 2021, 9, 11464–11483.

    CAS  Google Scholar 

  46. Li, C. W.; Xiong, T. Y.; Yu, P.; Fei, J. J.; Mao, L. Q. Synaptic iontronic devices for brain-mimicking functions: Fundamentals and applications. ACS Appl. Bio Mater. 2021, 4, 71–84.

    CAS  Google Scholar 

  47. Tominov, R. V.; Vakulov, Z. E.; Avilov, V. I.; Khakhulin, D. A.; Polupanov, N. V.; Smirnov, V. A.; Ageev, O. A. The effect of growth parameters on electrophysical and memristive properties of vanadium oxide thin films. Molecules 2021, 26, 118.

    CAS  Google Scholar 

  48. Xia, Q. F.; Yang, J. J. Memristive crossbar arrays for brain-inspired computing. Nat. Mater. 2019, 18, 309–323.

    CAS  Google Scholar 

  49. Kim, S. G.; Han, J. S.; Kim, H.; Kim, S. Y.; Jang, H. W. Recent advances in memristive materials for artificial synapses. Adv. Mater. Technol. 2018, 3, 1800457.

    Google Scholar 

  50. Avilov, V. I.; Polupanov, N. V.; Tominov, R. V.; Solodovnik, M. S.; Konoplev, B. G.; Smirnov, V. A.; Ageev, O. A. Resistive switching of GaAs oxide nanostructures. Materials 2020, 13, 3451.

    CAS  Google Scholar 

  51. Yang, R.; Huang, H. M.; Guo, X. Memristive synapses and neurons for bioinspired computing. Adv. Electron. Mater. 2019, 5, 1900287.

    Google Scholar 

  52. Tominov, R. V.; Vakulov, Z. E.; Avilov, V. I.; Khakhulin, D. A.; Fedotov, A. A.; Zamburg, E. G.; Smirnov, V. A.; Ageev, O. A. Synthesis and memristor effect of a forming-free ZnO nanocrystalline films. Nanomaterials 2020, 10, 1007.

    CAS  Google Scholar 

  53. Tominov, R. V.; Vakulov, Z. E.; Polupanov, N. V.; Saenko, A. V.; Avilov, V. I.; Ageev, O. A.; Smirnov, V. A. Nanoscale-resistive switching in forming-free zinc oxide memristive structures. Nanomaterials 2022, 12, 455.

    CAS  Google Scholar 

  54. Guo, T.; Sun, B.; Ranjan, S.; Jiao, Y. X.; Wei, L.; Zhou, Y. N.; Wu, Y. A. From memristive materials to neural networks. ACS Appl. Mater. Interfaces 2020, 12, 54243–54265.

    CAS  Google Scholar 

  55. Zhou, G. D.; Wang, Z. R.; Sun, B.; Zhou, F. C.; Sun, L. F.; Zhao, H. B.; Hu, X. F.; Peng, X. Y.; Yan, J.; Wang, H. M. et al. Volatile and nonvolatile memristive devices for neuromorphic computing. Adv. Electron. Mater. 2022, 8, 2101127.

    CAS  Google Scholar 

  56. Shamsi, J.; Amirsoleimani, A.; Mirzakuchaki, S.; Ahmadi, M. Modular neuron comprises of memristor-based synapse. Neural Comput. Appl. 2017, 28, 1–11.

    Google Scholar 

  57. Schmitt, R.; Spring, J.; Korobko, R.; Rupp, J. L. M. Design of oxygen vacancy configuration for memristive systems. ACS Nano 2017, 11, 8881–8891.

    CAS  Google Scholar 

  58. Li, Y. Y.; Fuller, E. J.; Sugar, J. D.; Yoo, S.; Ashby, D. S.; Bennett, C. H.; Horton, R. D.; Bartsch, M. S.; Marinella, M. J.; Lu, W. D. et al. Filament-free bulk resistive memory enables deterministic analogue switching. Adv. Mater. 2020, 32, 2003984.

    CAS  Google Scholar 

  59. Heisig, T.; Baeumer, C.; Gries, U. N.; Mueller, M. P.; La Torre, C.; Luebben, M.; Raab, N.; Du, H. C.; Menzel, S.; Mueller, D. N. et al. Oxygen exchange processes between oxide memristive devices and water molecules. Adv. Mater. 2018, 30, 1800957.

    Google Scholar 

  60. Khot, A. C.; Desai, N. D.; Khot, K. V.; Salunkhe, M. M.; Chougule, M. A.; Bhave, T. M.; Kamat, R. K.; Musselman, K. P.; Dongale, T. D. Bipolar resistive switching and memristive properties of hydrothermally synthesized TiO2 nanorod array: Effect of growth temperature. Mater. Des. 2018, 151, 37–47.

    CAS  Google Scholar 

  61. Aglieri, V.; Zaffora, A.; Lullo, G.; Santamaria, M.; Di Franco, F.; Lo Cicero, U.; Mosca, M.; Macaluso, R. Resistive switching in microscale anodic titanium dioxide-based memristors. Superlattices Microstruct. 2018, 113, 135–142.

    CAS  Google Scholar 

  62. Abbas, H.; Abbas, Y.; Truong, S. N.; Min, K. S.; Park, M. R.; Cho, J.; Yoon, T. S.; Kang, C. J. A memristor crossbar array of titanium oxide for non-volatile memory and neuromorphic applications. Semicond. Sci. Technol. 2017, 32, 065014.

    Google Scholar 

  63. Ryu, J. H.; Kim, S. Artificial synaptic characteristics of TiO2/HfO2 memristor with self-rectifying switching for brain-inspired computing. Chaos, Solitons Fractals 2020, 140, 110236.

    Google Scholar 

  64. Tominov, R.; Avilov, V.; Vakulov, Z.; Khakhulin, D.; Ageev, O.; Valov, I.; Smirnov, V. Forming-free resistive switching of electrochemical titanium oxide localized nanostructures: Anodization, chemical composition, nanoscale size effects, and memristive storage. Adv. Electron. Mater. 2022, 8, 2200215.

    CAS  Google Scholar 

  65. Avilov, V. I.; Smirnov, V. A.; Tominov, R. V.; Sharapov, N. A.; Avakyan, A. A.; Polyakova, V. V.; Ageev, O. A. Atomic force microscopy of titanium oxide nanostructures with forming-free resistive switching. IOP Conf. Ser.: Mater. Sci. Eng. 2019, 699, 012004.

    CAS  Google Scholar 

  66. Avilov, V. I.; Tominov, R. V.; Sharapov, N. A.; Smirnov, V. A.; Ageev, O. A. Local anodic oxidation proceses influence and temterature stability on the memristive propherties of titanium oxide nanostructures for ReRAM development. In 2020 Moscow Workshop on Electronic and Networking Technologies, Moscow, Russia, 2020, pp 1–5.

  67. Dukhan, D. D.; Tominov, R. V.; Avilov, V. I.; Zamburg, E. G.; Smirnov, V. A.; Ageev, O. A. Investigation of resistive switching effect in nanocrystalline TiO2 thin film for neuromorphic system manufacturing. J. Phys.: Conf. Ser. 2019, 1400, 055032.

    CAS  Google Scholar 

  68. Karen’Kih, O. G.; Avilov, V. I.; Smirnov, V. A.; Fedotov, A. A.; Sharapov, N. A.; Polupanov, N. A. Modelling of local anodic oxidation of titanium oxide nanostructures formation process. IOP Conf. Ser.: Mater. Sci. Eng. 2018, 443, 012013.

    Google Scholar 

  69. Avilov, V. I.; Ageev, O. A.; Blinov, Y. F.; Konoplev, B. G.; Polyakov, V. V.; Smirnov, V. A.; Tsukanova, O. G. Simulation of the formation of nanosize oxide structures by local anode oxidation of the metal surface. Tech. Phys. 2015, 60, 717–723.

    CAS  Google Scholar 

  70. Bharti, B.; Kumar, S.; Lee, H. N.; Kumar, R. Formation of oxygen vacancies and Ti3+ state in TiO2 thin film and enhanced optical properties by air plasma treatment. Sci. Rep. 2016, 6, 32355.

    CAS  Google Scholar 

Download references

Acknowledgements

The reported study was funded by the Russian Federation Government (Agreement No. 075-15-2022-1123) (mathematical model development and theoretical calculations). The fabrication of memristor structures and their resistive switching investigation were supported by a grant from the Russian Science Foundation No. 22-79-10215, https://rscf.ru/project/22-79-10215/, at Southern Federal University. Multilevel switching was researched with the financial support of the grant of the President of the Russian Federation MK-2290.2022.4.

Author information

Authors and Affiliations

  1. Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Southern Federal University, Taganrog, 347922, Russia

    Vadim I. Avilov, Roman V. Tominov, Zakhar E. Vakulov, Lev G. Zhavoronkov & Vladimir A. Smirnov

  2. Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, Taganrog, 347922, Russia

    Vadim I. Avilov, Roman V. Tominov, Zakhar E. Vakulov, Lev G. Zhavoronkov & Vladimir A. Smirnov

Authors
  1. Vadim I. Avilov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roman V. Tominov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zakhar E. Vakulov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lev G. Zhavoronkov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vladimir A. Smirnov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vladimir A. Smirnov.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Avilov, V.I., Tominov, R.V., Vakulov, Z.E. et al. Titanium oxide artificial synaptic device: Nanostructure modeling and synthesis, memristive cross-bar fabrication, and resistive switching investigation. Nano Res. 16, 10222–10233 (2023). https://doi.org/10.1007/s12274-023-5639-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12274-023-5639-5

Keywords

Search

Navigation