Emerging synaptic devices: from two-terminal memristors to multiterminal neuromorphic transistors

Jiang, Shanshan; Nie, Sha; He, Yongli; Li, Rui; Chen, Chunsheng; Wan, Qing

doi:10.1016/j.mtnano.2019.100059

Cited by 72 publications

(71 citation statements)

References 112 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, Jiang et al. [ 239–241 ] also reported a photoelectronic heterosynaptic device based on the EDL gated MoS 2 transistor (Figure 12d–f). The photoelectric hybrid stimulus exhibited both potentiation and depression filtering effect.…”

Section: D Material‐based Synaptic Devicesmentioning

confidence: 99%

2D Material Based Synaptic Devices for Neuromorphic Computing

Cao

Peng

Chen

et al. 2020

Adv Funct Materials

207

168

View full text Add to dashboard Cite

The demand for computing power has been increasing exponentially since the emergence of artificial intelligence (AI), internet of things (IoT), and machine learning (ML), where novel computing primitives are required. Brain inspired neuromorphic computing systems, capable of combining analog computing and data storage at the device level, have drawn great attention recently. In addition, the basic electronic devices mimicking the biological synapse have achieved significant progress. Owing to their atomic thickness and reduced screening effect, the physical properties of 2D materials could be easily modulated by various stimuli, which is quite beneficial for synaptic applications. In this article, aiming at high‐performance and functional neuromorphic computing applications, a comprehensive review of synaptic devices based on 2D materials is provided, including the advantages of 2D materials and heterostructures, various robust multifunctional 2D synaptic devices, and associated neuromorphic applications. Challenges and strategies for the future development of 2D synaptic devices are also discussed. This review will provide an insight into the design and preparation of 2D synaptic devices and their applications in neuromorphic computing.

show abstract

Section: D Material‐based Synaptic Devicesmentioning

confidence: 99%

2D Material Based Synaptic Devices for Neuromorphic Computing

Cao

Peng

Chen

et al. 2020

Adv Funct Materials

207

168

View full text Add to dashboard Cite

show abstract

“…[1][2][3] Human brain-inspired neuromorphic computing, which can parallelly process unstructured information in an energyefficient manner, has attracted great attention in recent years. [3][4][5][6][7][8][9][10][11][12] Various artificial synapses, such as two-terminal memristors [13][14][15][16][17] and multiterminal neuromorphic transistors, [18][19][20][21][22][23][24] have been proposed to construct hardware artificial neural networks (ANNs) for neuromorphic computing. Especially, electrolytegated transistors (EGTs) using electrolyte to modulate the conductance state of the channels have been demonstrated as a promising candidate for neuromorphic applications due to their prominent analog switching performance.…”

Section: Introductionmentioning

confidence: 99%

Non‐Volatile Electrolyte‐Gated Transistors Based on Graphdiyne/MoS₂ with Robust Stability for Low‐Power Neuromorphic Computing and Logic‐In‐Memory

Yao

Chen

et al. 2021

Adv Funct Materials

View full text Add to dashboard Cite

Artificial synapses are the key building blocks for low-power neuromorphic computing that can go beyond the constraints of von Neumann architecture. In comparison with two-terminal memristors and three-terminal transistors with filament-formation and charge-trapping mechanisms, emerging electrolyte-gated transistors (EGTs) have been demonstrated as a promising candidate for neuromorphic applications due to their prominent analog switching performance. Here, a novel graphdiyne (GDY)/MoS 2 -based EGT is proposed, where an ion-storage layer (GDY) is adopted to EGTs for the first time. Benefitting from this Li-ion-storage layer, the GDY/MoS 2 -based EGT features a robust stability (variation < 1% for over 2000 cycles), an ultralow energy consumption (50 aJ µm −2 ), and long retention characteristics (>10 4 s). In addition, a quasi-linear conductance update with low noise (1.3%), an ultrahigh G max /G min ratio (10 3 ), and an ultralow readout conductance (<10 nS) have been demonstrated by this device, enabling the implementation of the neuromorphic computing with near-ideal accuracies. Moreover, the nonvolatile characteristics of the GDY/MoS 2 -based EGT enable it to demonstrate logic-in-memory functions, which can execute logic processing and store logic results in a single device. These results highlight the potential of the GDY/MoS 2 -based EGT for next-generation low-power electronics beyond von Neumann architecture.

show abstract

“…[ 13 ] Two‐terminal synaptic devices, based on the phase‐change materials, [ 14 ] memristor, [ 15 ] ferroelectric, [ 16 ] or ion‐migration mechanism, [ 17 ] as well as the three‐terminal and multi‐terminal devices in transistor architecture, have been developed and operated at a relatively low level of energy consumption. [ 18 ] These devices are working in the electrical mode, where the synaptic behaviors are triggered by either frequency or amplitude of voltage pulses.…”

Section: Introductionmentioning

confidence: 99%

Recent Progress in Optoelectronic Synapses for Artificial Visual‐Perception System

Han

et al. 2020

Small Structures

109

View full text Add to dashboard Cite

The human visual system undertakes most of the information perceiving tasks, and nearly 80% of the perceived information is obtained via the visual system. The basic functions of the human visual system can be emulated by neuromorphic visual‐perception systems in the light region from UV to near‐IR. An optoelectronic synapse served as the basic unit of the neuromorphic visual‐perception system is required to combine photosensing function and synaptic element. In addition, optoelectronic synapses demonstrate the prospective advantage of large bandwidth, ultrafast signal transmission, and low electrical energy loss, which is expected in the photonic signal‐triggered computing. This work reviews recent progress in the optoelectrical synapses. Device architectures and working mechanisms are discussed. The applications in the artificial visual‐perception system for image memorization and pattern recognition are reviewed. The main challenges and opportunities of optoelectrical synapses are also presented.

show abstract

Emerging synaptic devices: from two-terminal memristors to multiterminal neuromorphic transistors

Cited by 72 publications

References 112 publications

2D Material Based Synaptic Devices for Neuromorphic Computing

2D Material Based Synaptic Devices for Neuromorphic Computing

Non‐Volatile Electrolyte‐Gated Transistors Based on Graphdiyne/MoS₂ with Robust Stability for Low‐Power Neuromorphic Computing and Logic‐In‐Memory

Recent Progress in Optoelectronic Synapses for Artificial Visual‐Perception System

Contact Info

Product

Resources

About

Emerging synaptic devices: from two-terminal memristors to multiterminal neuromorphic transistors

Cited by 72 publications

References 112 publications

2D Material Based Synaptic Devices for Neuromorphic Computing

2D Material Based Synaptic Devices for Neuromorphic Computing

Non‐Volatile Electrolyte‐Gated Transistors Based on Graphdiyne/MoS2 with Robust Stability for Low‐Power Neuromorphic Computing and Logic‐In‐Memory

Recent Progress in Optoelectronic Synapses for Artificial Visual‐Perception System

Contact Info

Product

Resources

About

Non‐Volatile Electrolyte‐Gated Transistors Based on Graphdiyne/MoS₂ with Robust Stability for Low‐Power Neuromorphic Computing and Logic‐In‐Memory