Artificial sensory system based on memristive devices
In the biological nervous system, the integration and cooperation of parallel system of
receptors, neurons, and synapses allow efficient detection and processing of intricate and …
receptors, neurons, and synapses allow efficient detection and processing of intricate and …
A comprehensive review of advanced trends: from artificial synapses to neuromorphic systems with consideration of non-ideal effects
A neuromorphic system is composed of hardware-based artificial neurons and synaptic
devices, designed to improve the efficiency of neural computations inspired by energy …
devices, designed to improve the efficiency of neural computations inspired by energy …
Implementation of convolutional neural networks in memristor crossbar arrays with binary activation and weight quantization
We propose a hardware-friendly architecture of a convolutional neural network using a 32×
32 memristor crossbar array having an overshoot suppression layer. The gradual switching …
32 memristor crossbar array having an overshoot suppression layer. The gradual switching …
Programmable threshold logic implementations in a memristor crossbar array
In this study, we demonstrate the implementation of programmable threshold logics using a
32× 32 memristor crossbar array. Thanks to forming-free characteristics obtained by the …
32× 32 memristor crossbar array. Thanks to forming-free characteristics obtained by the …
Unveiling Resistance Switching Mechanisms in Undoped HfOx Ferroelectric Tunnel Junction Using Low-Frequency Noise Spectroscopy
We demonstrate that the resistance switching (RS) of an undoped hafnium oxide (HfOx)-
based ferroelectric tunnel junction (FTJ) is affected not only by ferroelectric domain switching …
based ferroelectric tunnel junction (FTJ) is affected not only by ferroelectric domain switching …
Low-fluctuation nonlinear model using incremental step pulse programming with memristive devices
On-chip learning in neuromorphic systems, wherein both training and inference are
performed on memristive synaptic devices, has been actively studied recently. However, on …
performed on memristive synaptic devices, has been actively studied recently. However, on …
Neuromorphic one-shot learning utilizing a phase-transition material
Design of hardware based on biological principles of neuronal computation and plasticity in
the brain is a leading approach to realizing energy-and sample-efficient AI and learning …
the brain is a leading approach to realizing energy-and sample-efficient AI and learning …
Fuse devices for pruning in memristive neural network
In this study, we developed a fuse device for pruning implementation in a hardware neural
network. A line-shaped fuse device was fabricated with aluminum metal and characterized …
network. A line-shaped fuse device was fabricated with aluminum metal and characterized …
Threshold learning algorithm for memristive neural network with binary switching behavior
On-chip learning is an effective method for adjusting artificial neural networks in
neuromorphic computing systems by considering hardware intrinsic properties. However, it …
neuromorphic computing systems by considering hardware intrinsic properties. However, it …
Self-rectifying NiOX/WOX heterojunction synaptic memristor for crossbar architectured reservoir computing system
In this study, we examine an ITO/NiO X/WO X/Pt pn heterojunction memristor for
neuromorphic applications as a synaptic crossbar array. The transition in the depletion …
neuromorphic applications as a synaptic crossbar array. The transition in the depletion …