Results 51 to 60 of about 9,687 (293)

Comprehensive Study of SDC Memristors for Resistive RAM Applications

open access: yesEnergies
Memristors have garnered considerable attention within the scientific community as devices for emerging construction of Very Large Scale Integration (VLSI) systems.
Bartłomiej Garda, Karol Bednarz
doaj   +1 more source

LiNbO3 dynamic memristors for reservoir computing

open access: yesFrontiers in Neuroscience, 2023
Information in conventional digital computing platforms is encoded in the steady states of transistors and processed in a quasi-static way. Memristors are a class of emerging devices that naturally embody dynamics through their internal electrophyiscal ...
Yuanxi Zhao   +9 more
doaj   +1 more source

Intermediate Resistive State in Wafer‐Scale Vertical MoS2 Memristors Through Lateral Silver Filament Growth for Artificial Synapse Applications

open access: yesAdvanced Functional Materials, EarlyView.
In MOCVD MoS2 memristors, a current compliance‐regulated Ag filament mechanism is revealed. The filament ruptures spontaneously during volatile switching, while subsequent growth proceeds vertically through the MoS2 layers and then laterally along the van der Waals gaps during nonvolatile switching.
Yuan Fa   +19 more
wiley   +1 more source

Integration of Low‐Voltage Nanoscale MoS2 Memristors on CMOS Microchips

open access: yesAdvanced Functional Materials, EarlyView.
This article presents the first monolithic integration of nanoscale MoS2‐based memristors into the back‐end‐of‐line of foundry‐fabricated CMOS microchips in a one‐transistor‐one‐resistor (1T1R) architecture. The MoS2‐based 1T1R cells exhibit forming‐free, nonvolatile resistive switching with ultra‐low operating voltages, low cycle‐to‐cycle variability ...
Jimin Lee   +16 more
wiley   +1 more source

Synaptic and Fast Switching Memristance in Porous Silicon-Based Structures

open access: yesNanomaterials, 2019
Memristors are two terminal electronic components whose conductance depends on the amount of charge that has flown across them over time. This dependence can be gradual, such as in synaptic memristors, or abrupt, as in resistive switching memristors ...
Vicente Torres-Costa   +4 more
doaj   +1 more source

The Ouroboros of Memristors: Neural Networks Facilitating Memristor Programming

open access: yesProceedings of the Neuronics Conference, 2023
arXiv (2024).
Yu, Zhenming   +5 more
openaire   +5 more sources

Optoelectronic Synaptic Devices Using Molecular Telluride Phase‐Change Inks for Three‐Factor Learning

open access: yesAdvanced Functional Materials, EarlyView.
Optoelectronic synaptic devices based on solution‐processed molecular telluride GST‐225 phase‐change inks are demonstrated for three‐factor learning. A global optical signal broadcast through a silicon waveguide induces non‐volatile conductance updates exclusively in locally electrically flagged memristors.
Kevin Portner   +14 more
wiley   +1 more source

Special Memristor and Memristor-Based Compact Neuron Circuit

open access: yesJournal of Circuits, Systems and Computers, 2023
Implementing neuron circuit using memristor can be more effective thanks to some properties of memristor such as nonlinearity. For this reason, many memristor-based neuron circuits have been found in the literature. This study presents a memristor-based floating neuron circuit that exhibits different types of spikes.
Altan, Muhammet Alper   +4 more
openaire   +3 more sources

Advances in memristors, memristive devices and systems

open access: yes, 2017
This book reports on the latest advances in and applications of memristors, memristive devices and systems. It gathers 20 contributed chapters by subject experts, including pioneers in the field such as Leon Chua (UC Berkeley, USA) and R.S.
Vaidyanathan, Sundarapandian   +1 more
core   +1 more source

Implantable Ionic Memristors Based on Natural Polymer Heterojunctions

open access: yesAdvanced Functional Materials, EarlyView.
We report an implantable natural polymer‐based ionic memristor composed of hyaluronic acid, chitosan, and PDMS. The device achieved 98.94% accuracy in MNIST classification while reducing training time by 36.8% compared with a conventional artificial neural network (ANN).
Dong‐yup Lee   +6 more
wiley   +1 more source

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