Simulation Study on Silicon-Based Floating Body Synaptic Transistor with Short- and Long-Term Memory Functions and Its Spike Timing-Dependent Plasticity

Kim, Hyungjin; Cho, Seongjae; Sun, Min-Chul; Park, Jungjin; Hwang, Sungmin; Park, Byung‐Gook

doi:10.5573/jsts.2016.16.5.657

Cited by 16 publications

(7 citation statements)

References 26 publications

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“…Synaptic transistors with different architectures, such as silicon-based ones [170,171], and oxide-based ones [172][173][174][175][176][177][178][179][180][181][182][183][184], have also been produced for neuromorphic applications. Synapses and other neuromorphic components have been built using organic electrochemical transistors [185][186][187][188][189][190] and organic nanoparticle transistors [191][192][193][194].…”

Section: Materials and Devicesmentioning

confidence: 99%

Neuromorphic Computing between Reality and Future Needs

Ahmed¹,

Shereif²

2023

Artificial Intelligence

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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

Artificial Intelligence

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“…A variety of synaptic transistors have also been fabricated for neuromorphic implementations, including silicon-based synaptic transistors [2311], [2312] and oxide-based synaptic transistors [2313]- [2325]. Organic electrochemical transistors [2326]- [2331] and organic nanoparticle transistors [2332]- [2335] have also been utilized to build neuromorphic components such as synapses.…”

Section: Materials For Neuromorphic Systemsmentioning

confidence: 99%

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Schuman¹,

Potok²,

Patton³

et al. 2017

Preprint

176

218

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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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“…In other words, efficiency should be supported not only by algorithms but also by the hardware. In this respect, studies on neuromorphic chips that integrate software and hardware are attracting particular interest [3,4,5,6].…”

Section: Introductionmentioning

confidence: 99%

Design and Characterization of Semi-Floating-Gate Synaptic Transistor

Cho

Lee

et al. 2019

Micromachines

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In this work, a study on a semi-floating-gate synaptic transistor (SFGST) is performed to verify its feasibility in the more energy-efficient hardware-driven neuromorphic system. To realize short- and long-term potentiation (STP/LTP) in the SFGST, a poly-Si semi-floating gate (SFG) and a SiN charge-trap layer are utilized, respectively. When an adequate number of holes are accumulated in the SFG, they are injected into the nitride charge-trap layer by the Fowler–Nordheim tunneling mechanism. Moreover, since the SFG is charged by an embedded tunneling field-effect transistor existing between the channel and the drain junction when the post-synaptic spike occurs after the pre-synaptic spike, and vice versa, the SFG is discharged by the diode when the post-synaptic spike takes place before the pre-synaptic spike. This indicates that the SFGST can attain STP/LTP and spike-timing-dependent plasticity behaviors. These characteristics of the SFGST in the highly miniaturized transistor structure can contribute to the neuromorphic chip such that the total system may operate as fast as the human brain with low power consumption and high integration density.

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Simulation Study on Silicon-Based Floating Body Synaptic Transistor with Short- and Long-Term Memory Functions and Its Spike Timing-Dependent Plasticity

Cited by 16 publications

References 26 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

Design and Characterization of Semi-Floating-Gate Synaptic Transistor

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