Abstract
As a key building block of the biological cortex, synapses are powerful information processing units that enable highly complex nonlinear computations. The realization of artificial synapses with similar capabilities has important implications for building intelligent, neuromorphic systems. Here, we demonstrate an artificial synapse based on NbOx nonvolatile memristor to mimic multifunctional bionic applications such as nociceptor and associative learning. Combined experimental characterization with COMSOL simulation, the traditional resistance switching characteristics, which are the decisive factor for the synapse properties are in-depth analyzed. It can be proposed that the I–V characteristics of Pt/NbOx/TiN memristor are governed by core-shell filaments consisting of the shell region of sub-stoichiometric Nb2O5−δ and the core of NbO2. On the basis of the core-shell filament model, it can be reasonably explained that Ohmic conduction and Poole-Frenkel conduction take turns to dominate the current flowing in the memristive device, leading to the zigzag evolution of current during the operation process of NbOx-based device. The simulations of synaptic plasticity, including long-term potentiation/depression (LTP/LTD), paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP), exhibiting that the NbOx can be utilized for an artificial synapse. Furthermore, bionic functions such as hyperalgesia and allodynia of a nociceptor and a series of associative learning behaviors in Pavlovian dog experiment are mimicked, illustrating that the Pt/NbOx/TiN have great potential for highly simplified artificial neural network applications.
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References
Liu X, Nandi S K, Venkatachalam D K, et al. Finite element modeling of resistive switching in Nb2O5-based memory device. In: 2014 Conference on Optoelectronic and Microelectronic Materials & Devices. Perth, 2014. 280–282
Kundozerova T V, Grishin A M, Stefanovich G B, et al. Anodic Nb2O5 nonvolatile RRAM. IEEE Trans Electron Devices, 2012, 59: 1144–1148
Deswal S, Kumar A, Kumar A. Investigating unipolar switching in Niobium oxide resistive switches: Correlating quantized conductance and mechanism. AIP Adv, 2018, 8: 085014
Jeon D S, Dongale T D, Kim T G. Low power Ti-doped NbO2-based selector device with high selectivity and low OFF current. J Alloys Compd, 2021, 884: 161041
Kwon O, Lee H, Kim S. Effects of oxygen flow rate on metal-to-insulator transition characteristics in NbOx-based selectors. Materials, 2022, 15: 8575
Nandi S K, Liu X, Venkatachalam D K, et al. Self-assembly of an NbO2 interlayer and configurable resistive switching in Pt/Nb/HfO2/Pt structures. Appl Phys Lett, 2015, 107: 132901
Slesazeck S, Mähne H, Wylezich H, et al. Physical model of threshold switching in NbO2 based memristors. RSC Adv, 2015, 5: 102318–102322
Wang W, Wang R, Shi T, et al. A self-rectification and quasi-linear analogue memristor for artificial neural networks. IEEE Electron Device Lett, 2019, 40: 1407–1410
Nandi S K, Das S K, Cui Y, et al. Thermal conductivity of amorphous NbOx thin films and its effect on volatile memristive switching. ACS Appl Mater Interfaces, 2022, 14: 21270–21277
Zhu J, Zhang X, Wang M, et al. An artificial spiking nociceptor integrating pressure sensors and memristors. IEEE Electron Device Lett, 2022, 43: 962–965
Zhu J, Wu Z, Zhang X, et al. A flexible LIF neuron based on NbOx memristors for neural interface applications. In: 2021 5th IEEE Electron Devices Technology & Manufacturing Conference (EDTM). Chengdu, 2021. 1–3
Zhu J, Zhang X, Wang R, et al. A heterogeneously integrated spiking neuron array for multimode-fused perception and object classification. Adv Mater, 2022, 34: e2200481
Zhang X, Zhuo Y, Luo Q, et al. An artificial spiking afferent nerve based on Mott memristors for neurorobotics. Nat Commun, 2020, 11: 51
Kim G, In J H, Kim Y S, et al. Self-clocking fast and variation tolerant true random number generator based on a stochastic mott memristor. Nat Commun, 2021, 12: 2906
Chen P H, Lin C Y, Chang T C, et al. Investigating selectorless property within niobium devices for storage applications. ACS Appl Mater Interfaces, 2022, 14: 2343–2350
Wang Y, Xu H, Wang W, et al. A configurable artificial neuron based on a threshold-tunable TiN/NbO/Pt memristor. IEEE Electron Device Lett, 2022, 43: 631–634
Gold M S, Gebhart G F. Nociceptor sensitization in pain pathogenesis. Nat Med, 2010, 16: 1248–1257
Aufray M, Menuel S, Fort Y, et al. New synthesis of nanosized niobium oxides and lithium niobate particles and their characterization by XPS analysis. J Nanosci Nanotechnol, 2009, 9: 4780–4785
Gibson G A, Musunuru S, Zhang J, et al. An accurate locally active memristor model for S-type negative differential resistance in NbOx. Appl Phys Lett, 2016, 108: 023505
Duan Q, Jing Z, Zou X, et al. Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks. Nat Commun, 2020, 11: 3399
Feldman D E. The spike-timing dependence of plasticity. Neuron, 2012, 75: 556–571
Bi G, Poo M. Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci, 1998, 18: 10464–10472
Kim Y, Kwon Y J, Kwon D E, et al. Nociceptive memristor. Adv Mater, 2018, 30: 1704320
Yoon J H, Wang Z, Kim K M, et al. An artificial nociceptor based on a diffusive memristor. Nat Commun, 2018, 9: 417
Ge J, Zhang S, Liu Z, et al. Flexible artificial nociceptor using a biopolymer-based forming-free memristor. Nanoscale, 2019, 11: 6591–6601
Pei Y, Zhou Z, Chen A P, et al. A carbon-based memristor design for associative learning activities and neuromorphic computing. Nanoscale, 2020, 12: 13531–13539
Liu L, Cheng Z, Jiang B, et al. Optoelectronic artificial synapses based on two-dimensional transitional-metal trichalcogenide. ACS Appl Mater Interfaces, 2021, 13: 30797–30805
Wu C, Kim T W, Guo T, et al. Mimicking classical conditioning based on a single flexible memristor. Adv Mater, 2017, 29: 1602890
Zhong Z, Jiang Z, Huang J, et al. ‘Stateful’ threshold switching for neuromorphic learning. Nanoscale, 2022, 14: 5010–5021
Wang J, Cao G, Sun K, et al. Alloy electrode engineering in memristors for emulating the biological synapse. Nanoscale, 2022, 14: 1318–1326
Luo P, Liu C, Lin J, et al. Molybdenum disulfide transistors with enlarged van der Waals gaps at their dielectric interface via oxygen accumulation. Nat Electron, 2022, 5: 849–858
Lin J, Chen X, Duan X, et al. Ultra-steep-slope high-gain MoS2 transistors with atomic threshold-switching gate. Adv Sci, 2022, 9: 2104439
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This work was supported by the National Natural Science Foundation of China (Grant Nos. 62274058, 62104065), the Open Project of China-Poland Belt and Road Joint Laboratory of Measurement and Control Technology (Grant No. MCT202104), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB44000000), the Hubei Province Key Research and Development Program (Grant No. 2022BAA020), and the Wuhan Key Research and Development Program (Grant Nos. 2022012202015055, 2023010402010612).
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In-depth analysis of core-shell filaments in nonvolatile NbOx memristive device as an artificial synapse for multifunctional bionic applications
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Jiang, B., Ke, S., Tao, Z. et al. In-depth analysis of core-shell filaments in nonvolatile NbOx memristive device as an artificial synapse for multifunctional bionic applications. Sci. China Technol. Sci. 66, 3596–3603 (2023). https://doi.org/10.1007/s11431-023-2469-8
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DOI: https://doi.org/10.1007/s11431-023-2469-8