Memristive-synapse spiking neural networks based on single-electron transistors

Long, Keliu; Zhang, Xiaohong

doi:10.1007/s10825-019-01437-w

Cited by 9 publications

(2 citation statements)

References 47 publications

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“…For instance, white graphene, as a two-dimensional material, can be utilized in these structures [ 26 ]. The SET can be used in various electronic devices such as oscillators [ 27 ], sensors [ 28 ], detection of gas molecules [ 29 ] and single electron memory [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Schemes for Single Electron Transistor Based on Double Quantum Dot Islands Utilizing a Graphene Nanoscroll, Carbon Nanotube and Fullerene

et al. 2022

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The single electron transistor (SET) is a nanoscale switching device with a simple equivalent circuit. It can work very fast as it is based on the tunneling of single electrons. Its nanostructure contains a quantum dot island whose material impacts on the device operation. Carbon allotropes such as fullerene (C60), carbon nanotubes (CNTs) and graphene nanoscrolls (GNSs) can be utilized as the quantum dot island in SETs. In this study, multiple quantum dot islands such as GNS-CNT and GNS-C60 are utilized in SET devices. The currents of two counterpart devices are modeled and analyzed. The impacts of important parameters such as temperature and applied gate voltage on the current of two SETs are investigated using proposed mathematical models. Moreover, the impacts of CNT length, fullerene diameter, GNS length, and GNS spiral length and number of turns on the SET’s current are explored. Additionally, the Coulomb blockade ranges (CB) of the two SETs are compared. The results reveal that the GNS-CNT SET has a lower Coulomb blockade range and a higher current than the GNS-C60 SET. Their charge stability diagrams indicate that the GNS-CNT SET has smaller Coulomb diamond areas, zero-current regions, and zero-conductance regions than the GNS-C60 SET.

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Section: Introductionmentioning

confidence: 99%

Schemes for Single Electron Transistor Based on Double Quantum Dot Islands Utilizing a Graphene Nanoscroll, Carbon Nanotube and Fullerene

et al. 2022

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“…Long et al researched SNN circuits based on a single electron transistor (SET) combined with a memristor. The relevant bionic characteristics of the SNN circuit are realized by establishing the PSPICE model 14 . S. Kim et al proposed a memristor based on SiNx material to simulate the switching synaptic characteristic of resistance.…”

Section: Introductionmentioning

confidence: 99%

Characteristic analysis of volatile avalanche diode threshold switching for bionic nerve synapse applications

Wang

Zhong

Jin

et al. 2021

Sci Rep

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The combination of biological neurology and memristive theory has greatly promoted the development of neuromorphic computing. To build a large-scale artificial intelligence alert system, the exploration of bionic synapses compatible with standard processes has become an urgent problem to be solved in the next step. In response to the above application requirements, this paper proposes a volatile avalanche diode threshold switching (VADTS) that is fully compatible with standard semiconductor technology to simulate the various functions of the synapse. Technology computer-aided design device-level simulation can verify the bionic principle of VADTS. The function of VADTS's bionic synapse was verified by the experimental test platform. The results show that under the action of the excitation signal (11.25 V), the device can continuously change from a high-resistance state to a low-resistance state. When the excitation signal is lower than the threshold, VADTS presents a “no adaptation” state of nerve synapses. When the excitation signal is higher than the threshold and changes continuously, the current changes along with the amplitude of the excitation signal, similar to the “sensitization” state of the nerve synapse.

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Memristor-based synaptic plasticity and unsupervised learning of spiking neural networks

Hajiabadi

Shalchian

2021

J Comput Electron

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Memristive-synapse spiking neural networks based on single-electron transistors

Cited by 9 publications

References 47 publications

Schemes for Single Electron Transistor Based on Double Quantum Dot Islands Utilizing a Graphene Nanoscroll, Carbon Nanotube and Fullerene

Schemes for Single Electron Transistor Based on Double Quantum Dot Islands Utilizing a Graphene Nanoscroll, Carbon Nanotube and Fullerene

Characteristic analysis of volatile avalanche diode threshold switching for bionic nerve synapse applications

Memristor-based synaptic plasticity and unsupervised learning of spiking neural networks

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