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Electrolyte-gated transistors for neuromorphic applications

Heyi Huang1, 2, Chen Ge1, 2, , Zhuohui Liu1, 2, Hai Zhong1, Erjia Guo1, 2, Meng He1, Can Wang1, 2, 3, Guozhen Yang1 and Kuijuan Jin1, 2, 3,

+ Author Affiliations

 Corresponding author: Chen Ge, gechen@iphy.ac.cn; Kuijuan Jin, kjjin@iphy.ac.cn

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Abstract: Von Neumann computers are currently failing to follow Moore’s law and are limited by the von Neumann bottleneck. To enhance computing performance, neuromorphic computing systems that can simulate the function of the human brain are being developed. Artificial synapses are essential electronic devices for neuromorphic architectures, which have the ability to perform signal processing and storage between neighboring artificial neurons. In recent years, electrolyte-gated transistors (EGTs) have been seen as promising devices in imitating synaptic dynamic plasticity and neuromorphic applications. Among the various electronic devices, EGT-based artificial synapses offer the benefits of good stability, ultra-high linearity and repeated cyclic symmetry, and can be constructed from a variety of materials. They also spatially separate “read” and “write” operations. In this article, we provide a review of the recent progress and major trends in the field of electrolyte-gated transistors for neuromorphic applications. We introduce the operation mechanisms of electric-double-layer and the structure of EGT-based artificial synapses. Then, we review different types of channels and electrolyte materials for EGT-based artificial synapses. Finally, we review the potential applications in biological functions.

Key words: electrolyte-gated transistorsneuromorphic comuptingartificial synapses



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Fig. 1.  (Color online) Schematic of the electrolyte-gated transistor operation mechanisms. (a, c) Quasi-static FET operation and (b, d) electrochemical transistors operation. (a) When a positive VG < VT is applied, an EDL is formed at the electrolyte/semiconductor interface (electron doping). (b) When a positive VG > VT is applied, an EDL is formed at the electrolyte/semiconductor interface, but some cations can intercalate into the semiconductor (electrochemical doping). (c) When a negative VG < VT is applied, an EDL is formed at the electrolyte/semiconductor interface (hole doping). (d) When a negative VG > VT is applied, an EDL is formed at the electrolyte/semiconductor interface but some anions can intercalate into the semiconductor (electrochemical doping).

Fig. 2.  (Color online) (a) Schematic of a biological synapse[34] and (b) an EGT-based artificial synapse. The synaptic weight (channel conductance) can be modulated in this device using electrochemical intercalation to adjust the small ion concentration in the semiconductor.

Fig. 3.  (Color online) (a) Evolution of the structural phase via electrolyte gating. (b) Schematic of the SrFeOx transistor design. (c) Sheet conductance versus gate bias. (d) VG-controlled LTP and depression behavior[31]. (e) EPSCs stimulated by presynaptic spikes with different amplitudes. (f) Nonvolatile multilevel conductance modulation for SCO devices[32].

Fig. 4.  (Color online) (a) Schematic of ion dynamics in EGTs. (b) PPF and PTP curves. (c) Three cycles of gating-induced LTP and LTD processes[29]. (d) Schematic diagram of hydrogen ion movement during electrolyte gating. (e) Electrolyte-gated VO2 transistor. (f) Synaptic potentiation and depression[33]. (g) Schematic structure of a multi-terminal IGZO neuro-transistor[75].

Fig. 5.  (Color online) (a) A multi-gated architecture of analogous artificial MoS2 synapses[76]. (b) Transfer curve. (c) Output characteristics at different top gate biases. (d) EPSC triggered by a series of presynaptic spikes. (e) Energy consumption (left-hand) and the synaptic weight change (right-hand) as a function of pulse amplitudes for one spike[77]. (f) Electrochemical graphene synapse[30].

Fig. 6.  (Color online) (a) Schematic of an organic synaptic transistor using wood-derived cellulose nanopaper (WCN). (b) EPSC values obtained during simultaneous triggering by Gate 1 and Gate 2[39]. (c) EPSC signals in response to 10 presynaptic spike trains at different frequencies. (d) Schematic of biological neuronal network and an ONW (organic nanowire) based synaptic transistor. (e) Schematic of EPSC triggered by a pair of spatiotemporally correlated spikes applied to an ONW synaptic transistor via two laterally coupled gates. (f) Postsynaptic current triggered by 60 negative and 60 positive pulses. (g) Array of 144 ONW synaptic transistors fabricated on a 4-inch silicon wafer. Inset: Scanning electron microscopy (SEM) image of a typical ONW with a diameter of 200 nm. (h) Ultraviolet-visible light spectroscopy as a measure of the transparency of bare and ONW array-loaded PET (polyethylene terephthalate) sheets. Inset: Photograph of a bare PET sheet and a 50-mm-pitched ONW array-loaded PET sheet[40].

Fig. 7.  (Color online) (a) Asymmetric and symmetric STDP functions implemented in ferrite synaptic transistors. (b) Neuromorphic computing simulation[31]. (c) Configurable logic operations with dual gating[77].

Fig. 8.  (Color online) (a) An artificial afferent nerve made of pressure sensors, an organic ring oscillator, and a synaptic transistor[95]. (b) Schematic illustration of sound azimuth detection simulation based on the neuro-transistor[64]. (c) Classical Pavlov’s dog conditioning experiment[77].

Table 1.   Summary of EGTs on ion species, channel materials, electrolytes, and energy consumption.

IonspeciesChannel materialsElectrolyteEnergy consumptionRef.
O2−SrFeOxIonic liquids4.8 pJ[31]
SrCoOxIonic liquidsN/A[32]
SmNiOxIonic liquidsN/A[21]
H+WO3Ionic liquids36 pJ[29]
VO2Ionic liquids2.2 pJ[33]
IGZOSolid electrolyte1 nJ[64]
MoS2Ionic liquids12.7 fJ[76, 77]
Li+GrapheneSolid electrolyte500 fJ[30]
Na+/H+Organic material(C8-BTBT)Organic electrolyte(WCNS)0.19 nJ[39]
Anions/HolesOrganic NanowiresIon gel1.23 fJ[40]
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    Received: 20 August 2020 Revised: 13 October 2020 Online: Accepted Manuscript: 27 November 2020Uncorrected proof: 27 November 2020Published: 09 January 2021

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      Heyi Huang, Chen Ge, Zhuohui Liu, Hai Zhong, Erjia Guo, Meng He, Can Wang, Guozhen Yang, Kuijuan Jin. Electrolyte-gated transistors for neuromorphic applications[J]. Journal of Semiconductors, 2021, 42(1): 013103. doi: 10.1088/1674-4926/42/1/013103 H Y Huang, C Ge, Z H Liu, H Zhong, E J Guo, M He, C Wang, G Z Yang, K J Jin, Electrolyte-gated transistors for neuromorphic applications[J]. J. Semicond., 2021, 42(1): 013103. doi: 10.1088/1674-4926/42/1/013103.Export: BibTex EndNote
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      Heyi Huang, Chen Ge, Zhuohui Liu, Hai Zhong, Erjia Guo, Meng He, Can Wang, Guozhen Yang, Kuijuan Jin. Electrolyte-gated transistors for neuromorphic applications[J]. Journal of Semiconductors, 2021, 42(1): 013103. doi: 10.1088/1674-4926/42/1/013103

      H Y Huang, C Ge, Z H Liu, H Zhong, E J Guo, M He, C Wang, G Z Yang, K J Jin, Electrolyte-gated transistors for neuromorphic applications[J]. J. Semicond., 2021, 42(1): 013103. doi: 10.1088/1674-4926/42/1/013103.
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      Electrolyte-gated transistors for neuromorphic applications

      doi: 10.1088/1674-4926/42/1/013103
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      • Heyi Huang:got her PhD degree in 2020 at Institute of Physics, Chinese Academy of Sciences under the supervision of Prof. Kuijuan Jin and Prof. Chen Ge. Her research focuses on thin film transistors and neuromorphic computing devices
      • Chen Ge:is currently an associate professor at the Institute of Physics, Chinese Academy of Sciences. He received PhD degree from Institute of Physics, Chinese Academy of Sciences in 2012. His research interests include electrolyte-gated synaptic transistors and ferroelectric synapses
      • Kuijuan Jin:received her PhD degree and became a professor at the Institute of Physics, Chinese Academy of Sciences in 1995 and 2004, respectively. She is a Fellow of the Institute of Physics of UK and a Fellow of the American Physical Society. Her main research is in the crossing area of optics and low dimensional perovskite oxides
      • Corresponding author: gechen@iphy.ac.cnkjjin@iphy.ac.cn
      • Received Date: 2020-08-20
      • Revised Date: 2020-10-13
      • Published Date: 2021-01-10

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