A Review of Graphene-Based Memristive Neuromorphic Devices and Circuits
Walters, Ben, Jacob, Mohan V., Amirsoleimani, Amirali, and Rahimi Azghadi, Mostafa (2023) A Review of Graphene-Based Memristive Neuromorphic Devices and Circuits. Advanced Intelligent Systems, 5 (10). 2300136.
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Abstract
As data processing volume increases, the limitations of traditional computers and the need for more efficient computing methods become evident. Neuromorphic computing mimics the brain's low-power and high-speed computations, making it crucial in the era of big data and artificial intelligence. One significant development in this field is the memristor, a device that exhibits neuromorphic tendencies. The performance of memristive devices and circuits relies on the materials used, with graphene being a promising candidate due to its unique properties. Researchers are investigating graphene-based memristors for large-scale, sustainable fabrication. Herein, progress in the development of graphene-based memristive neuromorphic devices and circuits is highlighted. Graphene and its common fabrication methods are discussed. The fabrication and production of graphene-based memristive devices are reviewed and comparisons are provided among graphene- and nongraphene-based memristive devices. Next, a detailed synthesis of the devices utilizing graphene-based memristors is provided to implement the basic building blocks of neuromorphic architectures, that is, synapses, and neurons. This is followed by reviewing studies building graphene memristive spiking neural networks (SNNs). Finally, insights on the prospects of graphene-based neuromorphic memristive systems including their device- and network-level challenges and opportunities are given.
Item ID: | 80398 |
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Item Type: | Article (Research - C1) |
ISSN: | 2640-4567 |
Keywords: | graphene, memristors, neuromorphic, neurons, spiking neural networks, synapses |
Copyright Information: | © 2023 The Authors. Advanced Intelligent Systems published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
Date Deposited: | 23 Jan 2024 23:58 |
FoR Codes: | 40 ENGINEERING > 4018 Nanotechnology > 401804 Nanoelectronics @ 50% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks @ 50% |
SEO Codes: | 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 100% |
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