Back-end-of-line SiC based memristor for resistive memory and artificial synapse
Back-end-of-line SiC based memristor for resistive memory and artificial synapse
Two-terminal memristor has emerged as one of the most promising neuromorphic artificial electronic devices for their structural resemblance to biological synapses and ability to emulate many synaptic functions. In this work, a memristor based on the back-end-of-line (BEOL) material silicon carbide (SiC) is developed. The thin film memristors demonstrate excellent binary resistive switching with compliance-free and self-rectifying characteristics which are advantageous for the implementation of high-density 3D crossbar memory architectures. The conductance of this SiC-based memristor can be modulated gradually through the application of both DC and AC signals. This behavior is demonstrated to further emulate several vital synaptic functions including paired-pulse facilitation (PPF), post-tetanic potentiation (PTP), short-term potentiation (STP), and spike-rate-dependent plasticity (SRDP). The synaptic function of learning-forgetting-relearning processes is successfully emulated and demonstrated using a 3 × 3 artificial synapse array. This work presents an important advance in SiC-based memristor and its application in both memory and neuromorphic computing.
artificial synapses, memristors, neuromorphic computing, silicon carbide
Kapur, Omesh, Radhev
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Guo, Dongkai
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Reynolds, Jamie
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Han, Yisong
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Beanland, Richard
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Jiang, Liudi
374f2414-51f0-418f-a316-e7db0d6dc4d1
De Groot, Kees
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Huang, Ruomeng
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September 2022
Kapur, Omesh, Radhev
008af9d2-92eb-4749-a3be-210010f63449
Guo, Dongkai
cc5dd5b1-9e1b-4a86-8f41-7161de1e2e8f
Reynolds, Jamie
505b2309-b2fa-434a-9a20-b61e866b3d57
Han, Yisong
9307e57c-85b5-461d-93c5-9c3081224c02
Beanland, Richard
f5ff7f86-c400-4a2a-8e38-421ed4d3a420
Jiang, Liudi
374f2414-51f0-418f-a316-e7db0d6dc4d1
De Groot, Kees
92cd2e02-fcc4-43da-8816-c86f966be90c
Huang, Ruomeng
c6187811-ef2f-4437-8333-595c0d6ac978
Kapur, Omesh, Radhev, Guo, Dongkai, Reynolds, Jamie, Han, Yisong, Beanland, Richard, Jiang, Liudi, De Groot, Kees and Huang, Ruomeng
(2022)
Back-end-of-line SiC based memristor for resistive memory and artificial synapse.
Advanced Electronic Materials, 8 (9), [2200312].
(doi:10.1002/aelm.202200312).
Abstract
Two-terminal memristor has emerged as one of the most promising neuromorphic artificial electronic devices for their structural resemblance to biological synapses and ability to emulate many synaptic functions. In this work, a memristor based on the back-end-of-line (BEOL) material silicon carbide (SiC) is developed. The thin film memristors demonstrate excellent binary resistive switching with compliance-free and self-rectifying characteristics which are advantageous for the implementation of high-density 3D crossbar memory architectures. The conductance of this SiC-based memristor can be modulated gradually through the application of both DC and AC signals. This behavior is demonstrated to further emulate several vital synaptic functions including paired-pulse facilitation (PPF), post-tetanic potentiation (PTP), short-term potentiation (STP), and spike-rate-dependent plasticity (SRDP). The synaptic function of learning-forgetting-relearning processes is successfully emulated and demonstrated using a 3 × 3 artificial synapse array. This work presents an important advance in SiC-based memristor and its application in both memory and neuromorphic computing.
Text
Adv Elect Materials - 2022 - Kapur - Back‐End‐of‐Line SiC‐Based Memristor for Resistive Memory and Artificial Synapse
- Version of Record
More information
Accepted/In Press date: 3 May 2022
e-pub ahead of print date: 1 June 2022
Published date: September 2022
Additional Information:
Funding Information:
The authors would like to thank EPSRC and AWE Ltd. for the ICASE studentship No. 16000087 for O.K. All data supporting this study are openly available from the University of Southampton repository at DOI: https://doi.org/10.5258/SOTON/D2165.
Publisher Copyright:
© 2022 The Authors. Advanced Electronic Materials published by Wiley-VCH GmbH.
Keywords:
artificial synapses, memristors, neuromorphic computing, silicon carbide
Identifiers
Local EPrints ID: 457906
URI: http://eprints.soton.ac.uk/id/eprint/457906
PURE UUID: e3929e8b-be0b-4c28-bc3f-c5b3203d0b8a
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Date deposited: 21 Jun 2022 18:20
Last modified: 06 Dec 2024 03:01
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Contributors
Author:
Omesh, Radhev Kapur
Author:
Dongkai Guo
Author:
Jamie Reynolds
Author:
Yisong Han
Author:
Richard Beanland
Author:
Ruomeng Huang
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