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Spike-dependent plasticity modulation in TiO2-based synaptic device

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Abstract

The realization of highly efficient neuromorphic computing necessitates the development of artificial synaptic devices. This article reports a titanium dioxide (TiO2)-based transparent artificial synaptic device that exhibits almost all functionalities of a biological synapse. The fabricated device exhibits potentiation and depression at very low voltage amplitude (500 mV) input stimuli. The plasticity of the artificial synapse is also studied by varying the number of input stimuli and interval between the pulses (spike-rate-dependent plasticity—SRDP). The transition from short-term plasticity (STP) to long-term potentiation (LTP) is observed for a higher number of input stimuli and shorter interval between pulses. The device possesses excellent paired-pulse facilitation (PPF) with varying width, interval, and amplitude of the input stimuli. The spike-timing-dependent plasticity (STDP), one of the essential characteristics of a biological synapse, is also demonstrated.

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References

  1. Y. van de Burgt, E. Lubberman, E.J. Fuller, S.T. Keene, G.C. Faria, S. Agarwal, M.J. Marinella, A. Alec Talin, A. Salleo, Nat. Mater. 16, 414 (2017)

    Article  Google Scholar 

  2. M. Hu, H. Li, Y. Chen, Q. Wu, G.S. Rose, R.W. Linderman, IEEE Trans. Neural Netw. Learn. Syst. 25, 1864 (2014)

    Article  Google Scholar 

  3. S. Park, J. Noh, M. Choo, A.M. Sheri, M. Chang, Y.-B. Kim, C.J. Kim, M. Jeon, B.-G. Lee, B.H. Lee, H. Hwang, Nanotechnology 24, 384009 (2013)

    Article  Google Scholar 

  4. A. Sebastian, M. Le Gallo, R. Khaddam-Aljameh, E. Eleftheriou, Nat. Nanotechnol. 15(7), 529–544 (2020)

    Article  CAS  Google Scholar 

  5. D. Ielmini, H.-S.P. Wong, Nat. Electron. 1, 333 (2018)

    Article  Google Scholar 

  6. D. Ielmini, Microelectron. Eng. 190, 44 (2018)

    Article  CAS  Google Scholar 

  7. B. Chen, F. Cai, J. Zhou, W. Ma, P. Sheridan, and W. D. Lu, in 2015 IEEE Int. Electron Devices Meet., pp. 17.5.1–17.5.4 (2015)

  8. R. B. Hur and S. Kvatinsky, in 2016 IEEE/ACM Int. Symp. Nanoscale Archit., pp. 171–172 (2016).

  9. B. Rajendran, F. Alibart, IEEE J. Emerg. Sel. Top. Circuits Syst. 6, 198 (2016)

    Article  Google Scholar 

  10. M. Tsigkourakos, P. Bousoulas, V. Aslanidis, E. Skotadis, D. Tsoukalas, Phys. Status Solidi 214, 1700570 (2017)

    Article  Google Scholar 

  11. Q. Luo, X. Xu, T. Gong, H. Lv, D. Dong, H. Ma, P. Yuan, J. Gao, J. Liu, Z. Yu, J. Li, S. Long, Q. Liu, and M. Liu, in 2017 IEEE Int. Electron Devices Meet., pp. 2.7.1–2.7.4 (2017)

  12. Q. Luo, X. Xu, H. Liu, H. Lv, T. Gong, S. Long, Q. Liu, H. Sun, W. Banerjee, L. Li, J. Gao, N. Lu, M. Liu, Nanoscale 8, 15629 (2016)

    Article  CAS  Google Scholar 

  13. E. Linn, R. Rosezin, C. Kügeler, R. Waser, Nat. Mater. 9, 403 (2010)

    Article  CAS  Google Scholar 

  14. M. Zackriya, H.M. Kittur, A. Chin, Sci. Rep. 7, 42375 (2017)

    Article  CAS  Google Scholar 

  15. S. Song, K.D. Miller, L.F. Abbott, Nat. Neurosci. 3, 919 (2000)

    Article  CAS  Google Scholar 

  16. S. Li, F. Zeng, C. Chen, H. Liu, G. Tang, S. Gao, C. Song, Y. Lin, F. Pan, D. Guo, J. Mater. Chem. C 1, 5292 (2013)

    Article  CAS  Google Scholar 

  17. Y. Zhang, Z. Zeng, and S. Wen, in 2014 Int. Jt. Conf. Neural Networks, pp. 2226–2233 (2014).

  18. H. Markram, W. Gerstner, and P. J. Sjöström, Spike-Timing Dependent Plasticity (Frontiers E-books, 2012).

  19. G. Bi, M. Poo, Annu. Rev. Neurosci. 24, 139 (2001)

    Article  CAS  Google Scholar 

  20. W. He, K. Huang, N. Ning, K. Ramanathan, G. Li, Y. Jiang, J. Sze, L. Shi, R. Zhao, J. Pei, Sci. Rep. 4, 4755 (2014)

    Article  Google Scholar 

  21. F. Gul, Appl. Nanosci. 10, 611 (2020)

    Article  CAS  Google Scholar 

  22. K.M. Kim, D.S. Jeong, C.S. Hwang, Nanotechnology 22, 254002 (2011)

    Article  Google Scholar 

  23. R. Waser, R. Dittmann, G. Staikov, K. Szot, Adv. Mater. 21, 2632 (2009)

    Article  CAS  Google Scholar 

  24. D.-H. Kwon, K.M. Kim, J.H. Jang, J.M. Jeon, M.H. Lee, G.H. Kim, X.-S. Li, G.-S. Park, B. Lee, S. Han, M. Kim, C.S. Hwang, Nat. Nanotechnol. 5, 148 (2010)

    Article  CAS  Google Scholar 

  25. R.S. Zucker, W.G. Regehr, Annu. Rev. Physiol. 64, 355 (2002)

    Article  CAS  Google Scholar 

  26. T. Ohno, T. Hasegawa, T. Tsuruoka, K. Terabe, J.K. Gimzewski, M. Aono, Nat. Mater. 10, 591 (2011)

    Article  CAS  Google Scholar 

  27. K.W. Spence, J.T. Spence, Psychology of Learning and Motivation (Elsevier , Texas, 1968).

    Google Scholar 

  28. C. Izawa, On Human Memory: Evolution, Progress, and Reflections on the 30th Anniversary of the Atkinson-Shiffrin Model (Taylor & Francis, New York and London, 1999).

    Book  Google Scholar 

  29. N. Caporale, Y. Dan, Annu. Rev. Neurosci. 31, 25 (2008)

    Article  CAS  Google Scholar 

  30. Y. Dan, M. Poo, Neuron 44, 23 (2004)

    Article  CAS  Google Scholar 

  31. C. Du, W. Ma, T. Chang, P. Sheridan, W.D. Lu, Adv. Funct. Mater. 25, 4290 (2015)

    Article  CAS  Google Scholar 

  32. J. Ge, M. Chaker, ACS Appl. Mater. Interfaces 9, 16327 (2017)

    Article  CAS  Google Scholar 

  33. B. Bharti, S. Kumar, H.-N. Lee, R. Kumar, Sci. Rep. 6, 32355 (2016)

    Article  CAS  Google Scholar 

  34. D.S. Jeong, H. Schroeder, R. Waser, Phys. Rev. B 79, 195317 (2009)

    Article  Google Scholar 

  35. B. Zhao, M. **ao, Y.N. Zhou, Nanotechnology 30, 425202 (2019)

    Article  CAS  Google Scholar 

  36. S. Kim, Y. Choi, IEEE Trans. Electron Devices 56, 3049 (2009)

    Article  CAS  Google Scholar 

  37. G. Khurana, N. Kumar, J. F. Scott, and R. S. Katiyar, in Memristor and Memristive Neural Networks, edited by A. P. James (IntechOpen, Rijeka, 2018).

  38. M.-C. Chen, T.-C. Chang, S.-Y. Huang, S.-C. Chen, C.-W. Hu, C.-T. Tsai, S.M. Sze, Electrochem. Solid-State Lett. 13, H191 (2010)

    Article  CAS  Google Scholar 

  39. A. Younis, D. Chu, S. Li, J. Phys. D. Appl. Phys. 45, 355101 (2012)

    Article  Google Scholar 

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Acknowledgements

The author SPS acknowledges the University Grants Commission (UGC), Government of India for research fellowship. The authors acknowledge DST-FIST for FESEM and ellipsometry facility at the Department of Physics, Cochin University of Science and Technology, Kochi, Kerala, India.

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UGC, Government of India.

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Correspondence to M. K. Jayaraj.

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Subin, P.S., Asha, A.S., Saji, K.J. et al. Spike-dependent plasticity modulation in TiO2-based synaptic device. J Mater Sci: Mater Electron 32, 13051–13061 (2021). https://doi.org/10.1007/s10854-021-05710-2

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