Interface-engineered reliable HfO2-based RRAM for synaptic simulation-Reference-Cited by-同舟云学术

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Interface-engineered reliable HfO2-based RRAM for synaptic simulation

Published:2019 Issue:40 Volume:7 Page:12682-12687
ISSN:2050-7526
Container-title:Journal of Materials Chemistry C
language:en
Short-container-title:J. Mater. Chem. C

Author:

Wang Qiang¹²³⁴⁵^ORCID,Niu Gang¹²³⁴⁵,Roy Sourav¹²³⁴⁵,Wang Yankun¹²³⁴⁵,Zhang Yijun¹²³⁴⁵,Wu Heping¹²³⁴⁵,Zhai Shijie¹²³⁴⁵,Bai Wei¹²³⁴⁵,Shi Peng¹²³⁴⁵,Song Sannian⁶⁷⁸⁹¹⁰,Song Zhitang⁶⁷⁸⁹¹⁰,Xie Ya-Hong¹¹¹²¹²¹³,Ye Zuo-Guang¹⁴¹⁵¹⁶¹⁷^ORCID,Wenger Christian¹⁸¹⁹²⁰,Meng Xiangjian²¹²²⁸²³¹⁰,Ren Wei¹²³⁴⁵

Affiliation:

1. Electronic Materials Research Laboratory

2. Key Laboratory of the Ministry of Education & International Center for Dielectric Research

3. School of Electronic Science and Engineering

4. Xi’an Jiaotong University

5. Xi’an 710049

6. State Key Laboratory of Functional Materials for Informatics

7. Shanghai Institute of Microsystem and Information Technology

8. Chinese Academy of Sciences

9. Shanghai 200050

10. China

11. Department of Materials Science and Engineering, University of California

12. Los Angeles

13. USA

14. Department of Chemistry and 4D LABS

15. Simon Fraser University

16. Burnaby

17. Canada

18. IHP-Leibniz-Institut für innovative Mikroelektronik

19. 15236 Frankfurt (Oder)

20. Germany

21. National Laboratory for Infrared Physics

22. Shanghai Institute of Technical Physics

23. Shanghai

Abstract

O₃-based interface engineering dominates implementations of analog memory and synaptic simulation using reliable Pt/HfO₂/TiN RRAM.

Funder

Natural Sciences and Engineering Research Council of Canada

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Higher Education Discipline Innovation Project

Publisher

Royal Society of Chemistry (RSC)

Subject

Materials Chemistry,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2019/TC/C9TC04880D

Reference38 articles.

1. Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses

2. Electronic imitation of behavioral and psychological synaptic activities using TiOx/Al2O3-based memristor devices

3. Tutorial: Concepts for closely mimicking biological learning with memristive devices: Principles to emulate cellular forms of learning

4. Control of Synaptic Plasticity Learning of Ferroelectric Tunnel Memristor by Nanoscale Interface Engineering

5. Nanoelectronic Programmable Synapses Based on Phase Change Materials for Brain-Inspired Computing

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