Results 31 to 40 of about 95,522 (318)
Halide perovskite for low‐power consumption neuromorphic devices
EcoMat, 2021 The rapid emergency of data science, information technology, and artificial intelligence (AI) relies on massive data processing with high computing efficiency and low power consumption.
Itaru Raifuku +9 more
doaj +1 more source
Brain-inspired ferroelectric Si nanowire synaptic device [PDF]
APL Materials, 2021 We herein demonstrate a brain-inspired synaptic device using a poly(vinylidene fluoride) and trifluoroethylene (PVDF-TrFE)/silicon nanowire (Si NW) based ferroelectric field effect transistor (FeFET). The PVDF-TrFE/Si NW FeFET structure achieves reliable synaptic plasticity such as symmetrical potentiation and depression, thanks to the reversible ...
M. Lee +8 more
openaire +2 more sources
Atomic Layer Deposited SiOX-Based Resistive Switching Memory for Multi-Level Cell Storage
Metals, 2022 Herein, stable resistive switching characteristics are demonstrated in an atomic-layer-deposited SiOX-based resistive memory device. The thickness and chemical properties of the Pt/SiOX/TaN stack are verified by transmission electron microscopy (TEM) and
Yewon Lee +6 more
doaj +1 more source
A differential memristive synapse circuit for on-line learning in neuromorphic computing systems [PDF]
, 2017 Spike-based learning with memristive devices in neuromorphic computing architectures typically uses learning circuits that require overlapping pulses from pre- and post-synaptic nodes.
Indiveri, Giacomo +2 more
core +1 more source
Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective [PDF]
, 2019 On metrics of density and power efficiency, neuromorphic technologies have the potential to surpass mainstream computing technologies in tasks where real-time functionality, adaptability, and autonomy are essential.
Abbott +56 more
core +2 more sources
Recent Progress in Synaptic Devices Based on 2D Materials
Advanced Intelligent Systems, 2020 Diverse synaptic plasticity with a wide range of timescales in biological synapses plays an important role in memory, learning, and various signal processing with exceptionally low power consumption.
Linfeng Sun, Wei Wang, Heejun Yang
doaj +1 more source
Advanced Electronic Materials, 2023 If the speed of machine learning is to be improved, devices and systems with strong resistances to various types of internal noise, mainly internal thermal noise, are urgently needed. The successful demonstration of a synaptic device is reported based on
Haoqun An +4 more
doaj +1 more source
Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition [PDF]
, 2015 A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density.
Saxena, Vishal, Wu, Xinyu, Zhu, Kehan
core +3 more sources
Vertical 3-Terminal Artificial Synaptic Devices
Journal of Flexible and Printed Electronics, 2023 Recently, synaptic devices have the advantages of being able to process information in parallel. However, nondestructive weight control is limited in 2-terminal synaptic devices because reading and writing are conducted in a common electrode. Hence, 3-terminal
Nahyun Jeong, Kyung-Geun Lim
openaire +1 more source
A geographically distributed bio-hybrid neural network with memristive plasticity [PDF]
, 2017 Throughout evolution the brain has mastered the art of processing real-world inputs through networks of interlinked spiking neurons. Synapses have emerged as key elements that, owing to their plasticity, are merging neuron-to-neuron signalling with ...
Corna, Andrea +10 more
core +1 more source