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

Kunwar, Sundar; Jernigan, Zachary; Hughes, Zach; Somodi, Chase; Saccone, Michael Douglas; Caravelli, Francesco; Roy, Pinku; Zhang, Di; Wang, Haiyan; Jia, Quanxi; MacManus-Driscoll, Judith L.; Kenyon, Garrett; Sornborger, Andrew; Nie, Wanyi; Chen, Aiping

doi:10.1002/aisy.202300035

Title: An Interface–Type Memristive Device for Artificial Synapse and Neuromorphic Computing

Journal Article · 26 April 2023 · Advanced Intelligent Systems

DOI: https://doi.org/10.1002/aisy.202300035 · OSTI ID:1975058

^[1]; Jernigan, Zachary ^[1]; Hughes, Zach ^[1]; Somodi, Chase ^[1];

^[1];

^[2];

^[1]; Wang, Haiyan ^[3]; Jia, Quanxi ^[4]; MacManus-Driscoll, Judith L. ^[5];

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^[1]

Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); State Univ. of New York at Buffalo, NY (United States)
Purdue Univ., West Lafayette, IN (United States)
State Univ. of New York at Buffalo, NY (United States)
Univ. of Cambridge (United Kingdom)

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.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); National Science Foundation (NSF); Engineering and Physical Sciences Research Council (EPSRC); Royal Academy of Engineering

Grant/Contract Number:: 89233218CNA000001; ECCS-1902623; ECCS-1902644; DMR-1809520; EP/T012218/1; CIET 1819 24

OSTI ID:: 1975058

Report Number(s):: LA-UR-23-24739

Journal Information:: Advanced Intelligent Systems, Vol. 5, Issue 8; ISSN 2640-4567

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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36 MATERIALS SCIENCE
analog resistive switching
artificial synapse
interface-controlled memristive devices
neuromorphic computing

Title: An Interface–Type Memristive Device for Artificial Synapse and Neuromorphic Computing

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