Organic Synaptic Transistor with Integration of Memory and Neuromorphic Computing

2021 ◽  
Author(s):  
Shaomin Chen ◽  
Enlong Li ◽  
Rengjian Yu ◽  
Huihuang Yang ◽  
Yujie Yan ◽  
...  
Keyword(s):  
Computing System ◽  
Neuromorphic Computing ◽  
Von Neumann ◽  
Neumann System ◽  
Artificial Synapse

Artificial synapse devices have caused great interest in recent years for attempting to emulate brain-like computing system and to conquer the bottleneck of Von Neumann system. However, integration of the...

High Photosensitivity Light-Controlled Planar ZnO Artificial Synapse for Neuromorphic Computing

Nanoscale ◽  
2021 ◽  
Author(s):  
Wei Xiao ◽  
Linbo Shan ◽  
Haitao Zhang ◽  
Yujun Fu ◽  
yan fei zhao ◽  
...  
Keyword(s):  
Human Brain ◽  
Computing System ◽  
Neuromorphic Computing ◽  
Artificial Synapse ◽  
Fundamental Physical ◽  
High Photosensitivity ◽  
The Ideal

Light-controlled artificial synapse which mimics the human brain has been considered to be one of the ideal candidates for the fundamental physical architecture of neuromorphic computing system owing to the...

Ferroelectric polymer-based artificial synapse for neuromorphic computing

2021 ◽  
Author(s):  
Sungjun Kim ◽  
Keun Heo ◽  
Sunghun Lee ◽  
Seunghwan Seo ◽  
Hyeongjun Kim ◽  
...  
Keyword(s):  
Field Effect ◽  
Field Effect Transistor ◽  
Neuromorphic Computing ◽  
Ferroelectric Polymer ◽  
Artificial Synapse ◽  
Effect Transistor

Recently, various efforts have been made to implement synaptic characteristics with a ferroelectric field-effect transistor (FeFET), but in-depth physical analyses have not been reported thus far.

Reliable analog resistive switching behaviors achieved using memristive devices in AlOx/HfOx bilayer structure for neuromorphic systems

2021 ◽  
Author(s):  
Meng Qi ◽  
Tianquan Fu ◽  
Huadong Yang ◽  
ye tao ◽  
Chunran Li ◽  
...  
Keyword(s):  
Resistive Switching ◽  
Learning Experience ◽  
Random Access ◽  
Resistive Random Access Memory ◽  
Spike Timing ◽  
Information Storage ◽  
Bilayer Structure ◽  
Neuromorphic Computing ◽  
Von Neumann ◽  
Simulation Based

Abstract Human brain synaptic memory simulation based on resistive random access memory (RRAM) has an enormous potential to replace traditional Von Neumann digital computer thanks to several advantages, including its simple structure, high-density integration, and the capability to information storage and neuromorphic computing. Herein, the reliable resistive switching (RS) behaviors of RRAM are demonstrated by engineering AlOx/HfOx bilayer structure. This allows for uniform multibit information storage. Further, the analog switching behaviors are capable of imitate several synaptic learning functions, including learning experience behaviors, short-term plasticity-long-term plasticity transition, and spike-timing-dependent-plasticity (STDP). In addition, the memristor based on STDP learning rules are implemented in image pattern recognition. These results may offer a promising potential of HfOx-based memristors for future information storage and neuromorphic computing applications.

Thwarting Replication Attack Against Memristor-Based Neuromorphic Computing System

2020 ◽  
Vol 39 (10) ◽  
pp. 2192-2205
Author(s):  
Chaofei Yang ◽  
Beiye Liu ◽  
Hai Li ◽  
Yiran Chen ◽  
Mark Barnell ◽  
...  
Keyword(s):  
Computing System ◽  
Neuromorphic Computing

scholarly journals Memristive Spiking Neural Networks Trained with Unsupervised STDP

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 396 ◽  
Author(s):  
Errui Zhou ◽  
Liang Fang ◽  
Binbin Yang
Keyword(s):  
Neural Networks ◽  
Feature Learning ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Learning Method ◽  
Hardware Complexity ◽  
Neuromorphic Computing ◽  
Computing Systems ◽  
Von Neumann ◽  
Complex Architectures

Neuromorphic computing systems are promising alternatives in the fields of pattern recognition, image processing, etc. especially when conventional von Neumann architectures face several bottlenecks. Memristors play vital roles in neuromorphic computing systems and are usually used as synaptic devices. Memristive spiking neural networks (MSNNs) are considered to be more efficient and biologically plausible than other systems due to their spike-based working mechanism. In contrast to previous SNNs with complex architectures, we propose a hardware-friendly architecture and an unsupervised spike-timing dependent plasticity (STDP) learning method for MSNNs in this paper. The architecture, which is friendly to hardware implementation, includes an input layer, a feature learning layer and a voting circuit. To reduce hardware complexity, some constraints are enforced: the proposed architecture has no lateral inhibition and is purely feedforward; it uses the voting circuit as a classifier and does not use additional classifiers; all neurons can generate at most one spike and do not need to consider firing rates and refractory periods; all neurons have the same fixed threshold voltage for classification. The presented unsupervised STDP learning method is time-dependent and uses no homeostatic mechanism. The MNIST dataset is used to demonstrate our proposed architecture and learning method. Simulation results show that our proposed architecture with the learning method achieves a classification accuracy of 94.6%, which outperforms other unsupervised SNNs that use time-based encoding schemes.

scholarly journals Current-induced crystallisation in Heusler alloy films for memory potentiation in neuromorphic computation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
William Frost ◽  
Kelvin Elphick ◽  
Marjan Samiepour ◽  
Atsufumi Hirohata
Keyword(s):  
Heusler Alloy ◽  
Layer Growth ◽  
Layer By Layer ◽  
Half Metallic ◽  
Alloy Films ◽  
Von Neumann ◽  
Alternative Technologies ◽  
Artificial Synapse ◽  
Neumann Type ◽  
Area Product

AbstractThe current information technology has been developed based on von Neumann type computation. In order to sustain the rate of development, it is essential to investigate alternative technologies. In a next-generation computation, an important feature is memory potentiation, which has been overlooked to date. In this study, potentiation functionality is demonstrated in a giant magnetoresistive (GMR) junction consisting of a half-metallic Heusler alloy which can be a candidate of an artificial synapse while still achieving a low resistance-area product for low power consumption. Here the Heusler alloy films are grown on a (110) surface to promote layer-by-layer growth to reduce their crystallisation energy, which is comparable with Joule heating induced by a controlled current introduction. The current-induced crystallisation leads to the reduction in the corresponding resistivity, which acts as memory potentiation for an artificial GMR synaptic junction.

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