Proton–electron-coupled MoS<sub>2</sub> synaptic transistors with a natural renewable biopolymer neurotransmitter for brain-inspired neuromorphic learning

Hu, Wennan; Jiang, Jie; Xie, Dingdong; Liu, Biao; Yang, Junliang; He, Jun

doi:10.1039/c8tc04740e

Cited by 73 publications

(72 citation statements)

References 51 publications

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“…With the advantages of low cost, flexibility, and compatibility with large‐scale roll‐to‐roll manufacturing, organic field‐effect transistors (OFETs) can be one of the promising candidates for synaptic electronics. A large number of organic synaptic transistors with various ionic electrolytes have been developed for neuromorphic computing 20–24. However, most synaptic transistors are the thin‐film and layer‐by‐layer architectures with high spike voltage and large power consumption, which limits the application in the brain‐inspired low‐power artificial neural networks.…”

Section: Comparison Of the Energy Consumption From Other Researchesmentioning

confidence: 99%

The Design of 3D‐Interface Architecture in an Ultralow‐Power, Electrospun Single‐Fiber Synaptic Transistor for Neuromorphic Computing

Liu

Shi

Dai

et al. 2020

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Synaptic electronics is a new technology for developing functional electronic devices that can mimic the structure and functions of biological counterparts. It has broad application prospects in wearable computing chips, human–machine interfaces, and neuron prostheses. These types of applications require synaptic devices with ultralow energy consumption as the effective energy supply for wearable electronics, which is still very difficult. Here, artificial synapse emulation is demonstrated by solid‐ion gated organic field‐effect transistors (OFETs) with a 3D‐interface conducting channel for ultralow‐power synaptic simulation. The basic features of the artificial synapse, excitatory postsynaptic current (EPSC), paired‐pulse facilitation (PPF), and high‐pass filtering, are successfully realized. Furthermore, the single‐fiber based artificial synapse can be operated by an ultralow presynaptic spike down to −0.5 mV with an ultralow reading voltage at −0.1 mV due to the large contact surface between the ionic electrolyte and fiber‐like semiconducting channel. Therefore, the ultralow energy consumption at one spike of the artificial synapse can be realized as low as ≈3.9 fJ, which provides great potential in a low‐power integrated synaptic circuit.

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Section: Comparison Of the Energy Consumption From Other Researchesmentioning

confidence: 99%

The Design of 3D‐Interface Architecture in an Ultralow‐Power, Electrospun Single‐Fiber Synaptic Transistor for Neuromorphic Computing

Liu

Shi

Dai

et al. 2020

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“…Compared with the traditional semiconductors, atomically layered 2D materials have attracted extensive attention for nanoscale devices and exhibited promising potentials for many state‐of‐the‐art electronic applications . Recently, 2D van der Waals layered crystals and quasi‐2D transition metal‐oxides have been used as semiconductor materials in biological neural inspired synaptic transistors . For example, Jiang et al reported multi‐gate 2D MoS 2 synaptic transistors, which were laterally gated by a solid state poly (vinyl alcohol) (PVA) electrolyte, for a brain‐like computational system ( Figure a) .…”

Section: Electrolyte‐gate Synaptic Transistorsmentioning

confidence: 53%

Recent Advances in Transistor‐Based Artificial Synapses

Dai

Zhao

Wang

et al. 2019

Adv Funct Materials

421

422

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Simulating biological synapses with electronic devices is a re-emerging field of research. It is widely recognized as the first step in hardware building brain-like computers and artificial intelligent systems. Thus far, different types of electronic devices have been proposed to mimic synaptic functions. Among them, transistor-based artificial synapses have the advantages of good stability, relatively controllable testing parameters, clear operation mechanism, and can be constructed from a variety of materials. In addition, they can perform concurrent learning, in which synaptic weight update can be performed without interrupting the signal transmission process. Synergistic control of one device can also be implemented in a transistor-based artificial synapse, which opens up the possibility of developing robust neuron networks with significantly fewer neural elements. These unique features of transistor-based artificial synapses make them more suitable for emulating synaptic functions than other types of devices. However, the development of transistor-based artificial synapses is still in its very early stages. Herein, this article presents a review of recent advances in transistor-based artificial synapses in order to give a guideline for future implementation of synaptic functions with transistors. The main challenges and research directions of transistor-based artificial synapses are also presented.In the nerve system, a synapse is a specialized structure that allows a neuron to pass chemical or electrical signals to another Figure 2. a) Schematic illustration (top) and microscopy image (bottom) of flexible synaptic transistors based on a random matrix of semiconducting CNTs. b) Case 1: the amplitudes of V LTP and V LTP are greater than other cases; thus, NL is the highest and ΔG is the largest. c) Case 2: the amplitudes of V LTP and V LTP are smaller than in case 1; thus, NL and ΔG are lower. d) Case 3: if the CNT transistor without the Au floating gate is used for the synaptic transistor, NL and ΔG are considerably smaller than in the other cases due to the limited charge storage space. Reproduced with permission. [87]

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“…To better evaluate the performance of HZO/WS 2 ‐based synaptic transistor, we compared our device with other reported synaptic transistors. [ 13,15,20,21,30,46,53–62 ] It is clearly seen that our synaptic transistor shows a relatively high current on/off ratio at V G = 0 V and a high PSC change that outperforms other works (Figure 4e). This large memory window indicates a more reliable data reading and writing process.…”

Section: Resultsmentioning

confidence: 85%

A van der Waals Synaptic Transistor Based on Ferroelectric Hf_0.5Zr_0.5O₂ and 2D Tungsten Disulfide

Chen

Wang

Peng

et al. 2020

Adv Elect Materials

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Neuromorphic computing on the hardware level is promising for performing ever‐increasing data‐centric tasks owing to its superiority to conventional von Neumann architecture in terms of energy efficiency and learning ability. One key aspect to its implementation is the development of artificial synapses that can effectively emulate the multiple functionalities exhibited by their biological counterparts. Here, building on an inorganic ferroelectric gate stack integrated with a 2D layered semiconductor (WS2), a new type of ferroelectricity‐based synaptic transistor that differs from those relying on interface traps or floating gate configuration is reported. By virtue of a 6 nm thick ferroelectric hafnium zirconium oxide by atomic layer deposition and postannealing treatment, the device shows a channel resistance change ratio above 105 corresponding to opposite ferroelectric polarization direction. Furthermore, by applying electrical stimulus to the gate, it demonstrates good capability to mimic various synaptic behaviors including long‐term potentiation, long‐term depression, spike‐amplitude‐dependent plasticity, and spike‐rate‐dependent plasticity. Given the inherent compatibility of the ferroelectric gate stack with existing fabrication technology, and the reliability of ferroelectricity engineering, this work paves the way toward practical implementation of synaptic devices in neuromorphic circuits.

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Proton–electron-coupled MoS₂ synaptic transistors with a natural renewable biopolymer neurotransmitter for brain-inspired neuromorphic learning

Abstract: A new-type of artificial synapse based on proton–electron-coupled MoS2 transistors is firstly proposed gated by the chitosan-based natural renewable biopolymer.

Cited by 73 publications

References 51 publications

The Design of 3D‐Interface Architecture in an Ultralow‐Power, Electrospun Single‐Fiber Synaptic Transistor for Neuromorphic Computing

The Design of 3D‐Interface Architecture in an Ultralow‐Power, Electrospun Single‐Fiber Synaptic Transistor for Neuromorphic Computing

Recent Advances in Transistor‐Based Artificial Synapses

A van der Waals Synaptic Transistor Based on Ferroelectric Hf_0.5Zr_0.5O₂ and 2D Tungsten Disulfide

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Proton–electron-coupled MoS2 synaptic transistors with a natural renewable biopolymer neurotransmitter for brain-inspired neuromorphic learning

Abstract: A new-type of artificial synapse based on proton–electron-coupled MoS2 transistors is firstly proposed gated by the chitosan-based natural renewable biopolymer.

Cited by 73 publications

References 51 publications

The Design of 3D‐Interface Architecture in an Ultralow‐Power, Electrospun Single‐Fiber Synaptic Transistor for Neuromorphic Computing

The Design of 3D‐Interface Architecture in an Ultralow‐Power, Electrospun Single‐Fiber Synaptic Transistor for Neuromorphic Computing

Recent Advances in Transistor‐Based Artificial Synapses

A van der Waals Synaptic Transistor Based on Ferroelectric Hf0.5Zr0.5O2 and 2D Tungsten Disulfide

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Proton–electron-coupled MoS₂ synaptic transistors with a natural renewable biopolymer neurotransmitter for brain-inspired neuromorphic learning

A van der Waals Synaptic Transistor Based on Ferroelectric Hf_0.5Zr_0.5O₂ and 2D Tungsten Disulfide