An Interface‐Type Memristive Device for Artificial Synapse and Neuromorphic Computing

Author:

Kunwar Sundar1ORCID,Jernigan Zachary1,Hughes Zach1,Somodi Chase1,Saccone Michael D.2,Caravelli Francesco2,Roy Pinku13,Zhang Di1,Wang Haiyan4,Jia Quanxi3,MacManus-Driscoll Judith L.5,Kenyon Garrett6,Sornborger Andrew6,Nie Wanyi1,Chen Aiping1

Affiliation:

1. Center for Integrated Nanotechnologies (CINT) Los Alamos National Laboratory Los Alamos NM 87545 USA

2. T-4 Los Alamos National Laboratory Los Alamos NM 87545 USA

3. Department of Materials Design and Innovation University at Buffalo - The State University of New York Buffalo NY 14260 USA

4. School of Materials Engineering Purdue University West Lafayette IN 47907 USA

5. Department of Materials Science and Metallurgy University of Cambridge 27 Charles Babbage Road Cambridge CB3 0FS UK

6. CCS-3 Los Alamos National Laboratory Los Alamos NM 87545 USA

Abstract

Interface‐type (IT) metal/oxide Schottky memristive devices have attracted considerable attention over filament‐type (FT) devices for neuromorphic computing because of their uniform, filament‐free, and analog resistive switching (RS) characteristics. The most recent IT devices are based on oxygen ions and vacancies movement to alter interfacial Schottky barrier parameters and thereby control RS properties. However, the reliability and stability of these devices have been significantly affected by the undesired diffusion of ionic species. Herein, a reliable interface‐dominated memristive device is demonstrated using a simple Au/Nb‐doped SrTiO3 (Nb:STO) Schottky structure. The Au/Nb:STO Schottky barrier modulation by charge trapping and detrapping is responsible for the analog resistive switching characteristics. Because of its interface‐controlled RS, the proposed device shows low device‐to‐device, cell‐to‐cell, and cycle‐to‐cycle variability while maintaining high repeatability and stability during endurance and retention tests. Furthermore, the Au/Nb:STO IT memristive device exhibits versatile synaptic functions with an excellent uniformity, programmability, and reliability. A simulated artificial neural network with Au/Nb:STO synapses achieves a high recognition accuracy of 94.72% for large digit recognition from MNIST database. These results suggest that IT resistive switching can be potentially used for artificial synapses to build next‐generation neuromorphic computing.

Funder

National Science Foundation

Engineering and Physical Sciences Research Council

Royal Academy of Engineering

Publisher

Wiley

Subject

General Medicine

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