Simulation of a spiking neuron circuit using carbon nanotube transistors

Najari, Montassar; El-Grour, Tarek; Jelliti, Sami; Hakami, Othman Mousa

doi:10.1063/1.4953134

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…The carbon nanotube is another material under consideration for various neuromorphic applications. A variety of neuromorphic components, including dendrites on neurons [143][144][145][146][147], synapses [148][149][150][151][152][153][154][155][156][157][158][159][160][161][162][163][164][165], and spiking neurons [166][167][168][169], have been proposed to use carbon nanotubes. Carbon nanotubes have been used because they can provide the scale (number of neurons and synapses) and density (in terms of synapses) of neuromorphic systems that may be necessary for replicating or imitating biological neural systems.…”

Section: Materials and Devicesmentioning

confidence: 99%

Neuromorphic Computing between Reality and Future Needs

Ahmed¹,

Shereif²

2023

Neuromorphic Computing

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Neuromorphic computing is a one of computer engineering methods that to model their elements as the human brain and nervous system. Many sciences as biology, mathematics, electronic engineering, computer science and physics have been integrated to construct artificial neural systems. In this chapter, the basics of Neuromorphic computing together with existing systems having the materials, devices, and circuits. The last part includes algorithms and applications in some fields.

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Section: Materials and Devicesmentioning

confidence: 99%

Neuromorphic Computing between Reality and Future Needs

Ahmed¹,

Shereif²

2023

Neuromorphic Computing

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“…Another material considered for some neuromorphic implementations is the carbon nanotube. Carbon nanotubes have been proposed for use in a variety of neuromorphic components, including dendrites on neurons [2286]- [2290], synapses [235], [2291]- [2307], and spiking neurons [139], [2308]- [2310]. The reasons that carbon nanotubes have been utilized are that they can produce both the scale of neuromorphic systems (number of neurons and synapses) and density (in terms of synapses) that may be required for emulating or simulating biological neural systems.…”

Section: Materials For Neuromorphic Systemsmentioning

confidence: 99%

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Schuman¹,

Potok²,

Patton³

et al. 2017

Preprint

171

180

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Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems. The promise of the technology is to create a brainlike ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities. In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history. We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications. We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled. The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed.

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The Artificial Neuron: Built From Nanosheet Transistors to Achieve Ultra Low Power Consumption

Sheriff,

Sakthivel

2024

IEEE Access

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The performance of semiconductors has greatly improved due to the miniaturization of the transistor. To shrink the size of a transistor, the channel length must be decreased. Short-channel effects become noticeable as the gate length is reduced. Short-channel effects are observable at dimensions below 7 nm as a consequence of the shorter gate length. However, nanosheet structures have been proposed to replace FinFET and nanowire transistors at the 7 nm and 3 nm technological nodes. The purpose of this research is to construct an artificial neuron based on nanosheet transistors and evaluate its spiking behaviour and power consumption. We are building a regular Axon Hillock and our proposed neuron model using GPDK 45 nm and nanosheet transistor 20 nm technology. The simulation results clearly show that the nanosheet transistor consumes less power than the GPDK 45 nm transistors. Furthermore, we verified the validity of our suggested neuron model by performing Monte Carlo simulation, PVT analysis, AC response, noise response and layout. Additionally, the proposed neuron was tested with a variety of input currents, including pulse current, ramp current, sinusoidal current, and arbitrary current, and their associated spike patterns were recorded. According to our research, nanosheet-based Axon Hillock consumes 156 fW, and our proposed neuron consumes 1.9 pW of power.

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Simulation of a spiking neuron circuit using carbon nanotube transistors

Cited by 3 publications

References 9 publications

Neuromorphic Computing between Reality and Future Needs

Neuromorphic Computing between Reality and Future Needs

A Survey of Neuromorphic Computing and Neural Networks in Hardware

The Artificial Neuron: Built From Nanosheet Transistors to Achieve Ultra Low Power Consumption

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