A low-power memristor based on 2H–MoTe2 nanosheets with synaptic plasticity and arithmetic functions-Reference-Cited by-同舟云学术

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A low-power memristor based on 2H–MoTe2 nanosheets with synaptic plasticity and arithmetic functions

Published:2022-08 Issue: Volume:19 Page:100233
ISSN:2588-8420
Container-title:Materials Today Nano
language:en
Short-container-title:Materials Today Nano

Author:

Yu T.,Zhao Z.,Jiang H.,Weng Z.,Fang Y.,Liu C.,Lei W.,Shafe S.B.,Mohtar M.N.

Publisher

Elsevier BV

Subject

Materials Chemistry,Condensed Matter Physics,Biomaterials,Electronic, Optical and Magnetic Materials

Reference35 articles.

1. Human-level control through deep reinforcement learning

2. Mastering the game of Go with deep neural networks and tree search

3. From biomaterial-based data storage to bio-inspired artificial synapse

4. A VLSI Array of Low-Power Spiking Neurons and Bistable Synapses With Spike-Timing Dependent Plasticity

5. Toward a generalized Bienenstock-Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices

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1. Low-voltage solution-processed Sn-doped CuI neuromorphic transistors with synaptic plasticity and pain mimicked;Applied Physics Letters;2024-07-29

2. Artificial synapses based on Ag-sericin memrister for bioinspired neuromorphic computing;Journal of Materials Science: Materials in Electronics;2024-06

3. 2D multifunctional devices: from material preparation to device fabrication and neuromorphic applications;International Journal of Extreme Manufacturing;2024-03-13

4. Advances in two-dimensional heterojunction for sophisticated memristors;Materials Today Physics;2024-02

5. Recent progress of organic artificial synapses in biomimetic sensory neural systems;Journal of Materials Chemistry C;2024

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