Ferroelectricity-Defects Synergistic Artificial Synapses for High Recognition Accuracy Neuromorphic Computing-Reference-Cited by-同舟云学术

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Ferroelectricity-Defects Synergistic Artificial Synapses for High Recognition Accuracy Neuromorphic Computing

Published:2024-04-08 Issue:15 Volume:16 Page:19235-19246
ISSN:1944-8244
Container-title:ACS Applied Materials & Interfaces
language:en
Short-container-title:ACS Appl. Mater. Interfaces

Author:

Dong Shijie¹^ORCID,Liu Hao²,Wang Yan²,Bian Jing²^ORCID,Su Jie¹²³^ORCID

Affiliation:

1. College of Physics Science, Qingdao University, Qingdao 266071, People’s Republic of China

2. College of Electronic and Information Engineering, Qingdao University, Qingdao 266071, People’s Republic of China

3. National Laboratory of Solid State Microstructures, Physics Department, Nanjing University, Nanjing 210093, People’s Republic of China

Funder

National Laboratory of Solid State Microstructures, Nanjing University

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsami.4c01489

Reference64 articles.

1. Artificial Visual Perception Nervous System Based on Low-Dimensional Material Photoelectric Memristors

2. Oxygen vacancy migration/diffusion induced synaptic plasticity in a single titanate nanobelt

3. Brain-inspired computing with memristors: Challenges in devices, circuits, and systems

4. An Artificial Neuromuscular System for Bimodal Human–Machine Interaction

5. Artificial Synapses Emulated by an Electrolyte-Gated Tungsten-Oxide Transistor

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1. Synaptic memristors based on BaTiO₃ thin films irradiated by swift heavy ions for neuromorphic computing;Materials Horizons;2024

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