A technical paper titled “A Review of Graphene-Based Memristive Neuromorphic Devices and Circuits” was published by researchers at James Cook University (Australia) and York University (Canada).


“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.”

Find the technical paper here. Published August 2023.

Walters, B., Jacob, M.V., Amirsoleimani, A. and Rahimi Azghadi, M. (2023), A Review of Graphene-Based Memristive Neuromorphic Devices and Circuits. Adv. Intell. Syst. 2300136. https://doi.org/10.1002/aisy.202300136

Related Reading
Neuromorphic Computing Knowledge Center
Spiking Neural Network (SNN) Knowledge Center

Source: https://semiengineering.com/neuromorphic-computing-graphene-based-memristors-for-future-ai-hardware-from-fabrication-to-snns/