Results 11 to 20 of about 25,251 (300)
Perceptrons from memristors [PDF]
Neural Networks, 2020 Memristors, resistors with memory whose outputs depend on the history of their inputs, have been used with success in neuromorphic architectures, particularly as synapses and non-volatile memories. However, to the best of our knowledge, no model for a network in which both the synapses and the neurons are implemented using memristors has been proposed ...
Silva, Francisco +4 more
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Entangled quantum memristors [PDF]
Physical Review A, 2021 We propose the interaction of two quantum memristors via capacitive and inductive coupling in feasible superconducting circuit architectures. In this composed system the input gets correlated in time, which changes the dynamic response of each quantum memristor in terms of its pinched hysteresis curve and their nontrivial entanglement.
Kumar, Shubham +6 more
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Chitosan-Based Flexible Memristors with Embedded Carbon Nanotubes for Neuromorphic Electronics
Micromachines, 2021 In this study, we propose high-performance chitosan-based flexible memristors with embedded single-walled carbon nanotubes (SWCNTs) for neuromorphic electronics.
Jin-Gi Min, Won-Ju Cho
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Metal–Organic Frameworks–Based Memristors: Materials, Devices, and Applications
Molecules, 2022 Facing the explosive growth of data, a number of new micro-nano devices with simple structure, low power consumption, and size scalability have emerged in recent years, such as neuromorphic computing based on memristor.
Fan Shu +4 more
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Electrolyte-Dependent Modification of Resistive Switching in Anodic Hafnia
Nanomaterials, 2021 Anodic HfO2 memristors grown in phosphate, borate, or citrate electrolytes and formed on sputtered Hf with Pt top electrodes are characterized at fundamental and device levels.
Ivana Zrinski +7 more
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Advanced Electronic Materials, 2023 For Schottky barrier‐modulated memristors based on 2D semiconductors, it has, to date, not been possible to achieve control over defect type and concentration as the measured switching characteristics vary considerably even under similar fabrication ...
Hangbo Zhou +5 more
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Learning to Approximate Functions Using Nb-Doped SrTiO3 Memristors
Frontiers in Neuroscience, 2021 Memristors have attracted interest as neuromorphic computation elements because they show promise in enabling efficient hardware implementations of artificial neurons and synapses.
Thomas F. Tiotto +9 more
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Scientific Reports, 2016 AbstractTechnology based on memristors, resistors with memory whose resistance depends on the history of the crossing charges, has lately enhanced the classical paradigm of computation with neuromorphic architectures. However, in contrast to the known quantized models of passive circuit elements, such as inductors, capacitors or resistors, the design ...
Pfeiffer, P +4 more
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Proceedings of the great lakes symposium on VLSI, 2012 The memristor (M) is considered to be the fourth two-terminal passive element in electronics, alongside the resistor (R), the capacitor (C), and the inductor (L). Its existence was postulated in 1971 but its first implementation was reported in 2008. Where was it hiding all that time and what can we do with it?
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Bio‐Voltage Memristors: From Physical Mechanisms to Neuromorphic Interfaces
Advanced Electronic Materials, 2023 With the rapid development of emerging artificial intelligence technology, brain–computer interfaces are gradually moving from science fiction to reality, which has broad application prospects in the field of intelligent robots.
Saisai Wang +5 more
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