2D Material Based Synaptic Devices for Neuromorphic Computing

By Guiming Cao, Peng Meng, Jiangang Chen, Haishi Liu, Renji Bian, Chao Zhu, Fucai Liu, Zheng Liu. Advanced Functional Materials, 2020

328 citations recorded by the source corpus.

The demand for computing power has been increasing exponentially since the emergence of artificial intelligence (AI), internet of things (IoT), and machine learning (ML), where novel computing primitives are required. Brain inspired neuromorphic computing systems, capable of combining analog computing and data storage at the device level, have drawn great attention recently. In addition, the basic electronic devices mimicking the biological synapse have achieved significant progress. Owing to their atomic thickness and reduced screening effect, the physical properties of 2D materials could be easily modulated by various stimuli, which is quite beneficial for synaptic applications. In this article, aiming at high‐performance and functional neuromorphic computing applications, a comprehensive review of synaptic devices based on 2D materials is provided, including the advantages of 2D materials and heterostructures, various robust multifunctional 2D synaptic devices, and associated neuromorphic applications. Challenges and strategies for the future development of 2D synaptic devices are also discussed. This review will provide an insight into the design and preparation of 2D synaptic devices and their applications in neuromorphic computing.

Subfields: Electrical and Electronic Engineering, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/9611c82cb60c60cefe75a3decf3564a0f79bace6

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