J. Semicond. > 2025, Volume 46 > Issue 2 > 021401
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SPECIAL ISSUE REVIEWS

Electrolyte-gated optoelectronic transistors for neuromorphic applications

Jinming Bi1, Yanran Li1, Rong Lu1, Honglin Song1 and Jie Jiang1, 2,

+ Author Affiliations

 Corresponding author: Jie Jiang, jiangjie@csu.edu.cn

DOI: 10.1088/1674-4926/24090042CSTR: 32376.14.1674-4926.24090042

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Abstract: The traditional von Neumann architecture has demonstrated inefficiencies in parallel computing and adaptive learning, rendering it incapable of meeting the growing demand for efficient and high-speed computing. Neuromorphic computing with significant advantages such as high parallelism and ultra-low power consumption is regarded as a promising pathway to overcome the limitations of conventional computers and achieve the next-generation artificial intelligence. Among various neuromorphic devices, the artificial synapses based on electrolyte-gated transistors stand out due to their low energy consumption, multimodal sensing/recording capabilities, and multifunctional integration. Moreover, the emerging optoelectronic neuromorphic devices which combine the strengths of photonics and electronics have demonstrated substantial potential in the neuromorphic computing field. Therefore, this article reviews recent advancements in electrolyte-gated optoelectronic neuromorphic transistors. First, it provides an overview of artificial optoelectronic synapses and neurons, discussing aspects such as device structures, operating mechanisms, and neuromorphic functionalities. Next, the potential applications of optoelectronic synapses in different areas such as artificial visual system, pain system, and tactile perception systems are elaborated. Finally, the current challenges are summarized, and future directions for their developments are proposed.

Key words: neuromorphic computing, electrolyte-gated transistors, artificial synapses, optoelectronic devices



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Fig. 1.  (Color online) Schematic of an electrolyte-gated transistor for neuromorphic applications such as synaptic plasticity, spatiotemporal integration, and artificial perceptual systems.

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Fig. 2.  (Color online) (a) Schematic diagram of neuron structure[42]. (b) Schematic diagram of ion transport in biological synapses[43]. (c) Schematic of the biological synapse structure and two types of synaptic plasticity[42]. (d) Double-pulse facilitation behavior[44].

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Fig. 3.  (Color online) Schematic diagrams of (a) a top gate EDLT and (b) a side gate EDLT[55].

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Fig. 4.  (Color online) (a) Models of the electrical double layer at a positively charged surface: the inner Helmholtz plane (IHP) and outer Helmholtz plane (OHP)[56]. (b) Schematic of the MoS2/PTCDA hybrid heterojunction modulated by electrical or optical spike[59]. (c) Schematic diagram of the process of potential-induced hysteresis behavior based on Li+ embedded in α-MoO3 nanosheets[60].

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Fig. 5.  (Color online) (a) Chemical structures of oligomeric ionic liquids IL4TFSI and IL2TFSI, and monomeric ionic liquids BMITFSI and DEMETFSI as references[62]. (b) Graphene-molecule–graphene single-molecule junctions with ionic liquid gate dielectric and a brief scheme of energy level shifts under different gate voltages[64]. (c) Schematic of In2O3 synaptic transistors and repeatability of long-term potentiation and depression[65]. (d) Full schematics of an ionic liquid gated FET and schematic illustration of the conduction and valence band edges of monolayer MoSe2[66]. (e) Schematic structure of MoS2-EDLT based on DEME-TFSI modulation; transfer characteristic curves; I−V curves at different temperatures[67].

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Fig. 6.  (Color online) (a) Ion gel films prepared by spin-coating and drop-casting, respectively. (b) Relationship between membrane thickness and rotational speed, specific capacitance and frequency for ionic gel of different thicknesses[69]. (c) Schematic structure of 2H-MoTe2-EDLT. (d) Transfer curves of the bottom gate and the ion gates[70]. (e) Schematic structure of the ion doped MoS2 in-surface homogeneous PN junction. (f) Side-gate modulation curves at different bias voltages. (g) Bottom-gate transfer characteristic curves at different side-gate voltages[71].

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Fig. 7.  (Color online) (a) Transmission characteristics of P3HT under polymer electrolyte gate control[73]. (b) A schematic diagram of the oxide transistor array connected to the test system. (c) The EPSC response and Vth of pain perception are strongly dependent on the projection[74]. (d) Schematic diagrams for obtaining a InZnO EDL transistor on the graphene coated PET substrate. (e) A schematic diagram for the measurement of PPF[75]. (f) Schematic diagram of a neuron transistor based on SnO2 nanowires and an artificial neural network structure[76].

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Fig. 8.  (Color online) (a) 3D schematic of the fabricated YSH-based EGFET structure. (b) Retention characteristics and ANN operating accuracy at different yttrium concentrations in YSH[80]. (c) Schematic illustration of the measurement of synaptic characteristics. (d) Plot of linearity and the asymmetric ratio of 0.32[81]. (e) Schematic diagram of the device, EPSC triggered by longer spikes and channel current[82].

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Fig. 9.  (Color online) (a) Schematic diagram of the PEDOT: PSS organic electrochemical EGTs device. (b) Simulation of IPSC response. (c) Realization of low-pass filtering characteristics[86]. (d) Chitosan electrolyte-based ITO EGTs and their pulse test protocol for simulating STDP behavior. (e) Simulation of STDP behavior. (f) Simulation of SM and STM. (g) Simulation of memory level LTM[87]. (h) Schematic diagram of MoS2 EGTs with multiple signaling modes. (i) LTP and LTD behavior in different signaling modes. (j) Regulation of STDP behavior by ion signal in electrical mode and electrical signals in the ionic signaling mode[88].

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Fig. 10.  (Color online) (a) Schematic of PEDOT: PSS EGTs device with 3 × 3 coplanar Au electrodes[94]. (b) Distribution of EPSC current response for gate triggering at different positions. (c) Polar plots of EPSCs for input pulses with different spatial orientations[95]. (d) Schematic diagram of a neural system consisting of photodetectors and synaptic devices of EGTs[96, 97]. (e) Realization of the spatial localization function of the human ear[98]. (f) Results of Pavlov’s learning, time difference between the training spike applied at G1 and G2 (ΔT) as a function of the ΔWpeak[99].

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Fig. 11.  (Color online) (a) Schematic diagrams of the human eye structure and the photosensitive principle of the human visual system, structure diagram of In2O3 transistor. (b) Electrical enhancement and light depression function of an In2O3 transistor. (c) Self-adapted transistor arrays for artificial visual perception[104]. (d) The schematic diagram of an artificial synaptic opto-electronic transistor under light illumination. (e) Potentiation and depression emulated by an artificial opto-electronic synaptic transistor under various pulse widths[105]. (f) Schematic of the EGT triggered by voltage pulses and chiral light irradiation[43].

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Fig. 12.  (Color online) (a) A schematic picture of the HVVHT. (b) 3D image of SRDP and the Z index[106]. (c) The 3D device structure of sub-10-nm vertical coplanar-multiterminal flexible transient ITO phototransistor network, threshold properties of VN behavior and the statistics of detailed Pth[107]. (d) A sensory neuron (top) compared to our NeuTap (bottom)[108]. (e) Piezoresistor–nociceptor system, the response of nociceptor under variable degrees of forces. (f) Transition of the device to LTM mode after five consecutive light pulses. SNDP test at different numbers of light pulses[109].

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    Received: 22 September 2024 Revised: 15 October 2024 Online: Accepted Manuscript: 04 November 2024Uncorrected proof: 31 December 2024Published: 15 February 2025

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      Article Navigation >  Journal of Semiconductors  > 2025  >  46(2): 021401
      Jinming Bi, Yanran Li, Rong Lu, Honglin Song, Jie Jiang. Electrolyte-gated optoelectronic transistors for neuromorphic applications[J]. Journal of Semiconductors, 2025, 46(2): 021401. doi: 10.1088/1674-4926/24090042 ****J M Bi, Y R Li, R Lu, H L Song, and J Jiang, Electrolyte-gated optoelectronic transistors for neuromorphic applications[J]. J. Semicond., 2025, 46(2), 021401 doi: 10.1088/1674-4926/24090042
      Citation:
      Jinming Bi, Yanran Li, Rong Lu, Honglin Song, Jie Jiang. Electrolyte-gated optoelectronic transistors for neuromorphic applications[J]. Journal of Semiconductors, 2025, 46(2): 021401. doi: 10.1088/1674-4926/24090042 ****
      J M Bi, Y R Li, R Lu, H L Song, and J Jiang, Electrolyte-gated optoelectronic transistors for neuromorphic applications[J]. J. Semicond., 2025, 46(2), 021401 doi: 10.1088/1674-4926/24090042
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      Electrolyte-gated optoelectronic transistors for neuromorphic applications

      DOI: 10.1088/1674-4926/24090042
      CSTR: 32376.14.1674-4926.24090042
      • 1.

        Hunan Key Laboratory of Nanophotonics and Devices, School of Physics, Central South University, Changsha 410083, China

      • 2.

        State Key Laboratory of Precision Manufacturing for Extreme Service Performance, College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China

      More Information
      • Jinming Bi got his bachelor's degree from Yanbian University in 2021 and his master's degree from Hainan University in 2024. He is currently a research assistant at the School of Physics, Central South University. His research focuses on 2D neuromorphic devices
      • Jie Jiang received BE and ME in electronic science and technology and PhD in physics from Hunan University in 2007, 2009, and 2012, respectively. He was a postdoc in Nanyang Technological University (2012−2013) and Auburn University (2014−2015), respectively. He is currently a Professor in School of Physics, Central South University. His research focuses on neuromorphic materials and devices
      • Corresponding author: jiangjie@csu.edu.cn
      • Received Date: 2024-09-22
      • Revised Date: 2024-10-15
        • Available Online: 2024-11-04

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