Tailoring the synaptic properties of a-IGZO memristors for artificial deep neural networks-Reference-Cited by-同舟云学术

Tailoring the synaptic properties of a-IGZO memristors for artificial deep neural networks

Published:2022-01-01 Issue:1 Volume:10 Page:011113
ISSN:2166-532X
Container-title:APL Materials
language:en
Short-container-title:APL Materials

Author:

Pereira Maria Elias¹^ORCID,Deuermeier Jonas¹^ORCID,Freitas Pedro²^ORCID,Barquinha Pedro¹^ORCID,Zhang Weidong²^ORCID,Martins Rodrigo¹^ORCID,Fortunato Elvira¹^ORCID,Kiazadeh Asal¹^ORCID

Affiliation:

1. i3N/CENIMAT, Department of Materials Science, NOVA School of Science and Technology and CEMOP/UNINOVA, NOVA University Lisbon, Campus de Caparica, 2829-516 Caparica, Portugal

2. Liverpool John Moores University, Faculty of Engineering and Technology, School of Engineering, Liverpool, United Kingdom

Funder

Fundação para a Ciência e a Tecnologia

H2020 European Research Council

Engineering and Physical Sciences Research Council

Publisher

AIP Publishing

Subject

General Engineering,General Materials Science

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0073056

Reference53 articles.

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5. S. Yu, Z. Li, P. Y. Chen, H. Wu, B. Gao, D. Wang, W. Wu, and H. Qian, in IEEE International Electron Devices Meeting (IEEE, 2017), pp. 16.2.1–16.2.4.

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