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Study of short-term synaptic plasticity in Ion-Gel gated graphene electric-double-layer synaptic transistors

Chenrong Gong, Lin Chen, Weihua Liu and Guohe Zhang

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 Corresponding author: Guohe Zhang, zhangguohe@xjtu.edu.cn

DOI: 10.1088/1674-4926/42/1/014101

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Abstract: Multi-terminal electric-double-layer transistors have recently attracted extensive interest in terms of mimicking synaptic and neural functions. In this work, an Ion-Gel gated graphene synaptic transistor was proposed to mimic the essential synaptic behaviors by exploiting the bipolar property of graphene and the ionic conductivity of Ion-Gel. The Ion-Gel dielectrics were deposited onto the graphene film by the spin coating process. We consider the top gate and graphene channel as a presynaptic and postsynaptic terminal, respectively. Basic synaptic functions were successfully mimicked, including the excitatory postsynaptic current (EPSC), the effect of spike amplitude and duration on EPSC, and paired-pulse facilitation (PPF). This work may facilitate the application of graphene synaptic transistors in flexible electronics.

Key words: Ion-Gel, graphene, synaptic transistors, short-term plasticity (STP)



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Jiang J, Hu W, Xie D, et al. 2D electric-double-layer phototransistor for photoelectronic and spatiotemporal hybrid neuromorphic integration. Nanoscale, 2019, 11(3), 1360 doi: 10.1039/C8NR07133K
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Cho J H, Lee J, Xia Y, et al. Printable ion-gel gate dielectrics for low-voltage polymer thin-film transistors on plastic. Nat Mater, 2008, 7(11), 900 doi: 10.1038/nmat2291
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Liu J, Qian Q, Zou Y, et al. Enhanced performance of graphene transistor with ion-gel top gate. Carbon, 2014, 68, 480 doi: 10.1016/j.carbon.2013.11.024
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Kim B J, Jang H, Lee S, et al. High-performance flexible graphene field effect transistors with ion gel gate dielectrics. Nano Lett, 2010, 10(9), 3464 doi: 10.1021/nl101559n
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Chen L, Gong C, Zhang G, et al. Graphene synaptic transistor based on Ion-Gel dielectric. IEEE International Conference on Electron Devices and Solid-State Circuits, 2019, 1
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Wang H, Wu Y, Cong C, et al. Hysteresis of electronic transport in graphene transistors. Acs Nano, 2010, 4(12), 7221 doi: 10.1021/nn101950n
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Atluri P P, Regehr W G. Determinants of the time course of facilitation at the granule cell to purkinje cell synapse. J Neurosci, 1996, 16(18), 5661 doi: 10.1523/JNEUROSCI.16-18-05661.1996
Fig. 1.  (Color online) (a) An illustration of the process flow for the fabrication of graphene EDLTs. (b) Schematic and the top-view optical image of the graphene synaptic transistor. (c) Structure of the corresponding biological synapse.

DownLoad: Full-Size Img PowerPoint
Fig. 2.  (Color online) (a) Transfer curve (left) of the graphene transistor and the leakage current (right) through Ion-Gel. Fixed bias VDS = 0.1 V. (b) A presynaptic spike (top) applied on the top-gate electrode and EPSC (bottom) triggered by the spike are shown versus time.

DownLoad: Full-Size Img PowerPoint
Fig. 3.  (Color online) The working mechanisms of the synaptic device under positive voltage. Charge distributions (a) before the pulse is applied, (b) when a positive voltage is just applied, (c) after applied spike stabilization, (d) when the spike is just removed, and (e) after removing the pulse for a while are shown respectively. (f) Drain current corresponding to the mechanism.

DownLoad: Full-Size Img PowerPoint
Fig. 4.  (Color online) (a) EPSCs triggered by different spike duration for the same spike amplitude of 2 V are shown versus time. The spike duration increases from 100 to 600 ms. Inset: ΔEPSCs versus spike duration are plotted. (b) EPSCs triggered by different spike amplitude for the same spike duration of 100 ms are shown versus time. The spike amplitude increases from 0.5 to 3 V. Inset: ΔEPSCs versus spike amplitude are plotted.

DownLoad: Full-Size Img PowerPoint
Fig. 5.  (Color online) (a) A paired presynaptic spikes (2 V, 100 ms) with ΔT of 300 ms (top) applied on the transistor and the typical EPSC curve (bottom) triggered by the spikes are shown. (b) PPF index versus ΔT is plotted. The experimental data are fitted using a double exponential function.

DownLoad: Full-Size Img PowerPoint
[1]
von Neumann J. First draft of a report on the EDVAC. IEEE Ann Hist Comput, 1993, 15(4), 27 doi: 10.1109/85.238389
[2]
Nawrocki R A, Voyles R M, Shaheen S E. A mini review of neuromorphic architectures and implementations. IEEE Trans Electron Devices, 2016, 63(10), 3819 doi: 10.1109/TED.2016.2598413
[3]
Kendall J D, Kumar S. The building blocks of a brain-inspired computer. Appl Phys Rev, 2020, 7(1), 11305 doi: 10.1063/1.5129306
[4]
Yao P, Wu H, Gao B, et al. Fully hardware-implemented memristor convolutional neural network. Nature, 2020, 577(7792), 641 doi: 10.1038/s41586-020-1942-4
[5]
Roy K, Jaiswal A, Panda P. Towards spike-based machine intelligence with neuromorphic computing. Nature, 2019, 575(7784), 607 doi: 10.1038/s41586-019-1677-2
[6]
Pei J, Deng L, Song S, et al. Towards artificial general intelligence with hybrid Tianjic chip architecture. Nature, 2019, 572(7767), 106 doi: 10.1038/s41586-019-1424-8
[7]
Tian H, Mi W, Zhao H, et al. A novel artificial synapse with dual modes using bilayer graphene as the bottom electrode. Nanoscale, 2017, 9(27), 9275 doi: 10.1039/C7NR03106H
[8]
Kandel E, Schwartz J, Jessell T, et al. Principles of neural science. New York: McGraw-Hill, 2013
[9]
Li J, Yang Y, Yin M, et al. Electrochemical and thermodynamic processes of metal nanoclusters enabled biorealistic synapses and leaky-integrate-and-fire neurons. Mater Horiz, 2020, 7(1), 71 doi: 10.1039/C9MH01206K
[10]
Yan X, Zhao Q, Chen A P, et al. Vacancy-induced synaptic behavior in 2D WS2 nanosheet-based memristor for low-power neuromorphic computing. Small, 2019, 15(24), 1901423 doi: 10.1002/smll.201901423
[11]
Ielmini D, Wong H S P. In-memory computing with resistive switching devices. Nat Electron, 2018, 1(6), 333 doi: 10.1038/s41928-018-0092-2
[12]
Wang Z, Joshi S, Savel'ev S E, et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat Mater, 2017, 16(1), 101 doi: 10.1038/nmat4756
[13]
Liu B, Liu Z, Chiu I, et al. Programmable synaptic metaplasticity and below femtojoule spiking energy realized in graphene-based neuromorphic memristor. Acs Appl Mater Interfaces, 2018, 10(24), 20237 doi: 10.1021/acsami.8b04685
[14]
Yao Y, Huang X, Peng S, et al. Reconfigurable artificial synapses between excitatory and inhibitory modes based on single-gate graphene transistors. Adv Electron Mater, 2019, 5(5), 1800887 doi: 10.1002/aelm.201800887
[15]
Huang H Y, Ge C, Zhang Q H, et al. Electrolyte-gated synaptic transistor with oxygen ions. Adv Funct Mater, 2019, 29(29), 1902702 doi: 10.1002/adfm.201902702
[16]
Dai S, Zhao Y, Wang Y, et al. Recent advances in transistor-based artificial synapses. Adv Funct Mater, 2019, 29(42), 1903700 doi: 10.1002/adfm.201903700
[17]
Liu M, Huang G, Feng P, et al. Artificial neuron synapse transistor based on silicon nanomembrane on plastic substrate. J Semicond, 2017, 38(6), 64006 doi: 10.1088/1674-4926/38/6/064006
[18]
Perea G, Navarrete M, Araque A. Tripartite synapses: Astrocytes process and control synaptic information. Trends Neurosci, 2009, 32(8), 421 doi: 10.1016/j.tins.2009.05.001
[19]
Valtcheva S, Venance L. Astrocytes gate Hebbian synaptic plasticity in the striatum. Nat Commun, 2016, 7(1), 13845 doi: 10.1038/ncomms13845
[20]
He Y, Wan Q. Multi-terminal oxide-based electric-double-layer thin-film transistors for neuromorphic systems. ECS Trans, 2018, 86(11), 177 doi: 10.1149/08611.0177ecst
[21]
Singh M, Manoli K, Tiwari A, et al. The double layer capacitance of ionic liquids for electrolyte gating of ZnO thin film transistors and effect of gate electrodes. J Mater Chem C, 2017, 5(14), 3509 doi: 10.1039/C7TC00800G
[22]
Schmidt E, Shi S, Ruden P P, et al. Characterization of the electric double layer formation dynamics of a metal/ionic liquid/metal structure. Acs Appl Mater Interfaces, 2016, 8(23), 14879 doi: 10.1021/acsami.6b04065
[23]
He Y, Yang Y, Nie S, et al. Electric-double-layer transistors for synaptic devices and neuromorphic systems. J Mater Chem C, 2018, 6(2), 5336 doi: 10.1039/C8TC00530C
[24]
Kong L, Sun J, Qian C, et al. Ion-gel gated field-effect transistors with solution-processed oxide semiconductors for bioinspired artificial synapses. Org Electron, 2016, 39, 64 doi: 10.1016/j.orgel.2016.09.029
[25]
Wan X, Yang Y, Feng P, et al. Short-term plasticity and synaptic filtering emulated in electrolyte-gated IGZO transistors. IEEE Electron Device Lett, 2016, 37(3), 299 doi: 10.1109/LED.2016.2517080
[26]
Jiang J, Hu W, Xie D, et al. 2D electric-double-layer phototransistor for photoelectronic and spatiotemporal hybrid neuromorphic integration. Nanoscale, 2019, 11(3), 1360 doi: 10.1039/C8NR07133K
[27]
Cho J H, Lee J, Xia Y, et al. Printable ion-gel gate dielectrics for low-voltage polymer thin-film transistors on plastic. Nat Mater, 2008, 7(11), 900 doi: 10.1038/nmat2291
[28]
Liu J, Qian Q, Zou Y, et al. Enhanced performance of graphene transistor with ion-gel top gate. Carbon, 2014, 68, 480 doi: 10.1016/j.carbon.2013.11.024
[29]
Kim B J, Jang H, Lee S, et al. High-performance flexible graphene field effect transistors with ion gel gate dielectrics. Nano Lett, 2010, 10(9), 3464 doi: 10.1021/nl101559n
[30]
Chen L, Gong C, Zhang G, et al. Graphene synaptic transistor based on Ion-Gel dielectric. IEEE International Conference on Electron Devices and Solid-State Circuits, 2019, 1
[31]
Rs Z, Wg R. Short-term synaptic plasticity. Annu Rev Physiol, 2002, 64, 355 doi: 10.1146/annurev.physiol.64.092501.114547
[32]
Abbott L F, Regehr W G. Synaptic computation. Nature, 2004, 431(7010), 796 doi: 10.1038/nature03010
[33]
Abraham W C. Metaplasticity: tuning synapses and networks for plasticity. Nat Rev Neurosci, 2008, 9(5), 387 doi: 10.1038/nrn2356
[34]
Jiang J, Guo J, Wan X, et al. 2D MoS2 neuromorphic devices for brain-like computational systems. Small, 2017, 13(29), 1700933 doi: 10.1002/smll.201700933
[35]
Tian H, Mi W, Wang X, et al. Graphene dynamic synapse with modulatable plasticity. Nano Lett, 2015, 15(12), 8013 doi: 10.1021/acs.nanolett.5b03283
[36]
Wang H, Wu Y, Cong C, et al. Hysteresis of electronic transport in graphene transistors. Acs Nano, 2010, 4(12), 7221 doi: 10.1021/nn101950n
[37]
Atluri P P, Regehr W G. Determinants of the time course of facilitation at the granule cell to purkinje cell synapse. J Neurosci, 1996, 16(18), 5661 doi: 10.1523/JNEUROSCI.16-18-05661.1996

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    Received: 29 May 2020 Revised: 03 September 2020 Online: Accepted Manuscript: 03 November 2020Uncorrected proof: 04 November 2020Published: 09 January 2021

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      Article Navigation >  Journal of Semiconductors  > 2021  >  42(1): 014101
      Chenrong Gong, Lin Chen, Weihua Liu, Guohe Zhang. Study of short-term synaptic plasticity in Ion-Gel gated graphene electric-double-layer synaptic transistors[J]. Journal of Semiconductors, 2021, 42(1): 014101. doi: 10.1088/1674-4926/42/1/014101 ****C R Gong, L Chen, W H Liu, G H Zhang, Study of short-term synaptic plasticity in Ion-Gel gated graphene electric-double-layer synaptic transistors[J]. J. Semicond., 2021, 42(1): 014101. doi: 10.1088/1674-4926/42/1/014101.
      Citation:
      Chenrong Gong, Lin Chen, Weihua Liu, Guohe Zhang. Study of short-term synaptic plasticity in Ion-Gel gated graphene electric-double-layer synaptic transistors[J]. Journal of Semiconductors, 2021, 42(1): 014101. doi: 10.1088/1674-4926/42/1/014101 ****
      C R Gong, L Chen, W H Liu, G H Zhang, Study of short-term synaptic plasticity in Ion-Gel gated graphene electric-double-layer synaptic transistors[J]. J. Semicond., 2021, 42(1): 014101. doi: 10.1088/1674-4926/42/1/014101.
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      Study of short-term synaptic plasticity in Ion-Gel gated graphene electric-double-layer synaptic transistors

      DOI: 10.1088/1674-4926/42/1/014101

      • School of Microelectronics, Xi'an Jiaotong University, Xi'an 710049, China

      More Information
      • Chenrong Gong：got her B.S. degree in 2012 and M.S. degree in 2015 at Xi'an University of Technology. Now she is a Ph.D. student at Xi'an Jiaotong University under the supervision of Prof. Guohe Zhang. Her research focuses on carbon-based devices and their potential applications in the artificial synapse
      • Lin Chen：got her M.S. degree from Xi’an Jiaotong University in 2019. Now she is a junior engineer in the School of Microelectronics, Xi'an Jiaotong University. Her research focuses on carbon-based synaptic devices
      • Weihua Liu：is a professor at Xi’an Jiaotong University. He received the B. S. Degree and Ph. D. degree from Xi’an Jiaotong University in 2002 and 2005, respectively. From 2009 to 2010, he is a visiting scholar at Georgia Institute of Technology. His research interest is carbon nano materials and their application in sensors
      • Guohe Zhang：received his B.S. and Ph.D. degree in 2003 and 2008 respectively from Xi’an Jiaotong University. He is currently a professor at Xi’an Jiaotong University. His research interests include semiconductor device physics and integrated circuits design, image processing and intelligent system, algorithm and hardware co-design and implementation for deep learning and signal processing systems
      • Corresponding author: zhangguohe@xjtu.edu.cn
      • Received Date: 2020-05-29
      • Revised Date: 2020-09-03
      • Published Date: 2021-01-10

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