The University of Southampton
University of Southampton Institutional Repository

An ultra high-endurance memristor using back-end-of-line amorphous SiC

An ultra high-endurance memristor using back-end-of-line amorphous SiC
An ultra high-endurance memristor using back-end-of-line amorphous SiC

Integrating resistive memory or neuromorphic memristors into mainstream silicon technology can be substantially facilitated if the memories are built in the back-end-of-line (BEOL) and stacked directly above the logic circuitries. Here we report a promising memristor employing a plasma-enhanced chemical vapour deposition (PECVD) bilayer of amorphous SiC/Si as device layer and Cu as an active electrode. Its endurance exceeds one billion cycles with an ON/OFF ratio of ca. two orders of magnitude. Resistance drift is observed in the first 200 million cycles, after which the devices settle with a coefficient of variation of ca. 10% for both the low and high resistance states. Ohmic conduction in the low resistance state is attributed to the formation of Cu conductive filaments inside the bilayer structure, where the nanoscale grain boundaries in the Si layer provide the pre-defined pathway for Cu ion migration. Rupture of the conductive filament leads to current conduction dominated by reverse bias Schottky emission. Multistate switching is achieved by precisely controlling the pulse conditions for potential neuromorphic computing applications. The PECVD deposition method employed here has been frequently used to deposit typical BEOL SiOC low-k interlayer dielectrics. This makes it a unique memristor system with great potential for integration.

Back-end-of-line, Endurance, Memristor, PECVD, SiC
2045-2322
Kapur, Omesh
2be52575-505f-472f-ad9c-ce6fe84c20fd
Guo, Dongkai
cc5dd5b1-9e1b-4a86-8f41-7161de1e2e8f
Reynolds, Jamie
96faa744-02ee-458c-8e48-953ea9e54afe
Newbrook, Daniel William
8eb26553-e1e2-492d-ad78-ce51a487f31f
Han, Yisong
9307e57c-85b5-461d-93c5-9c3081224c02
Beanland, Richard
77643be6-0c38-4542-b05a-4ccc14efdc25
Jiang, Liudi
374f2414-51f0-418f-a316-e7db0d6dc4d1
De Groot, C.H. Kees
92cd2e02-fcc4-43da-8816-c86f966be90c
Huang, Ruomeng
c6187811-ef2f-4437-8333-595c0d6ac978
Kapur, Omesh
2be52575-505f-472f-ad9c-ce6fe84c20fd
Guo, Dongkai
cc5dd5b1-9e1b-4a86-8f41-7161de1e2e8f
Reynolds, Jamie
96faa744-02ee-458c-8e48-953ea9e54afe
Newbrook, Daniel William
8eb26553-e1e2-492d-ad78-ce51a487f31f
Han, Yisong
9307e57c-85b5-461d-93c5-9c3081224c02
Beanland, Richard
77643be6-0c38-4542-b05a-4ccc14efdc25
Jiang, Liudi
374f2414-51f0-418f-a316-e7db0d6dc4d1
De Groot, C.H. Kees
92cd2e02-fcc4-43da-8816-c86f966be90c
Huang, Ruomeng
c6187811-ef2f-4437-8333-595c0d6ac978

Kapur, Omesh, Guo, Dongkai, Reynolds, Jamie, Newbrook, Daniel William, Han, Yisong, Beanland, Richard, Jiang, Liudi, De Groot, C.H. Kees and Huang, Ruomeng (2024) An ultra high-endurance memristor using back-end-of-line amorphous SiC. Scientific Reports, 14 (1), [14008]. (doi:10.1038/s41598-024-64499-2).

Record type: Article

Abstract

Integrating resistive memory or neuromorphic memristors into mainstream silicon technology can be substantially facilitated if the memories are built in the back-end-of-line (BEOL) and stacked directly above the logic circuitries. Here we report a promising memristor employing a plasma-enhanced chemical vapour deposition (PECVD) bilayer of amorphous SiC/Si as device layer and Cu as an active electrode. Its endurance exceeds one billion cycles with an ON/OFF ratio of ca. two orders of magnitude. Resistance drift is observed in the first 200 million cycles, after which the devices settle with a coefficient of variation of ca. 10% for both the low and high resistance states. Ohmic conduction in the low resistance state is attributed to the formation of Cu conductive filaments inside the bilayer structure, where the nanoscale grain boundaries in the Si layer provide the pre-defined pathway for Cu ion migration. Rupture of the conductive filament leads to current conduction dominated by reverse bias Schottky emission. Multistate switching is achieved by precisely controlling the pulse conditions for potential neuromorphic computing applications. The PECVD deposition method employed here has been frequently used to deposit typical BEOL SiOC low-k interlayer dielectrics. This makes it a unique memristor system with great potential for integration.

Text
s41598-024-64499-2 - Version of Record
Available under License Creative Commons Attribution.
Download (5MB)

More information

Submitted date: 13 April 2024
Accepted/In Press date: 10 June 2024
Published date: 18 June 2024
Additional Information: Publisher Copyright: © The Author(s) 2024.
Keywords: Back-end-of-line, Endurance, Memristor, PECVD, SiC

Identifiers

Local EPrints ID: 490089
URI: http://eprints.soton.ac.uk/id/eprint/490089
ISSN: 2045-2322
PURE UUID: 429e070e-587b-4509-8650-4aad344e0568
ORCID for Jamie Reynolds: ORCID iD orcid.org/0000-0002-0072-0134
ORCID for Daniel William Newbrook: ORCID iD orcid.org/0000-0002-5047-6168
ORCID for Liudi Jiang: ORCID iD orcid.org/0000-0002-3400-825X
ORCID for C.H. Kees De Groot: ORCID iD orcid.org/0000-0002-3850-7101
ORCID for Ruomeng Huang: ORCID iD orcid.org/0000-0003-1185-635X

Catalogue record

Date deposited: 14 May 2024 16:47
Last modified: 12 Nov 2024 03:08

Export record

Altmetrics

Contributors

Author: Omesh Kapur
Author: Dongkai Guo
Author: Jamie Reynolds ORCID iD
Author: Daniel William Newbrook ORCID iD
Author: Yisong Han
Author: Richard Beanland
Author: Liudi Jiang ORCID iD
Author: Ruomeng Huang ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×