High‐Performance Neuromorphic Computing Based on Ferroelectric Synapses with Excellent Conductance Linearity and Symmetry-Reference-Cited by-同舟云学术

High‐Performance Neuromorphic Computing Based on Ferroelectric Synapses with Excellent Conductance Linearity and Symmetry

Published:2022-06-22 Issue:35 Volume:32 Page:2202366
ISSN:1616-301X
Container-title:Advanced Functional Materials
language:en
Short-container-title:Adv Funct Materials

Author:

Yang Shu‐Ting¹²,Li Xing‐Yu¹,Yu Tong‐Liang¹,Wang Jie³⁴,Fang Hong³⁴,Nie Fang¹,He Bin³,Zhao Le²,Lü Wei‐Ming³⁴,Yan Shi‐Shen¹,Nogaret Alain⁵,Liu Gang⁶,Zheng Li‐Mei¹^ORCID

Affiliation:

1. School of Physics and State Key Laboratory of Crystal Materials Shandong University Jinan 250100 P. R. China

2. School of Information and Automation Engineering Qilu University of Technology (Shandong Academy of Science) Jinan 250353 P. R. China

3. Spintronics Institute University of Jinan Jinan 250022 P. R. China

4. School of Instrumentation Science and Engineering Condensed Matter Science and Technology Institute Harbin Institute of Technology Harbin 150080 P. R. China

5. Department of Physics University of Bath Bath BA2 7AY UK

6. Center for High Pressure Science and Technology Advanced Research Shanghai 201203 P. R. China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

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

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adfm.202202366

Reference51 articles.

1. Memristive devices for computing