Memristors Based on 2D Materials as an Artificial Synapse for Neuromorphic Electronics
- PMID: 32985042
- DOI: 10.1002/adma.202002092
Memristors Based on 2D Materials as an Artificial Synapse for Neuromorphic Electronics
Abstract
The memristor, a composite word of memory and resistor, has become one of the most important electronic components for brain-inspired neuromorphic computing in recent years. This device has the ability to control resistance with multiple states by memorizing the history of previous electrical inputs, enabling it to mimic a biological synapse in the neural network of the human brain. Among many candidates for memristive materials, including metal oxides, organic materials, and low-dimensional nanomaterials, 2D layered materials have been widely investigated owing to their outstanding physical properties and electrical tunability, low-power-switching capability, and hetero-integration compatibility. Hence, a large number of experimental demonstrations on 2D material-based memristors have been reported showing their unique memristive characteristics and novel synaptic functionalities, distinct from traditional bulk-material-based systems. Herein, an overview of the latest advances in the structures, mechanisms, and memristive characteristics of 2D material-based memristors is presented. Additionally, novel strategies to modulate and enhance the synaptic functionalities of 2D-memristor-based artificial synapses are summarized. Finally, as a foreseeing perspective, the potentials and challenges of these emerging materials for future neuromorphic electronics are also discussed.
Keywords: 2D materials; artificial synapses; memristors; neuromorphic electronics; transition metal dichalcogenides.
© 2020 Wiley-VCH GmbH.
References
-
- J. v. Neumann, IEEE Ann. Hist. Comput. 1993, 15, 27.
-
- E. Bullmore, O. Sporns, Nat. Rev. Neurosci. 2012, 13, 336.
-
- O. Sporns, Front. Comput. Neurosci. 2011, 5, 5.
-
- E. R. Kandel, J. H. Schwartz, T. M. Jessell, D. o. Biochemistry, M. B. T. Jessell, S. Siegelbaum, A. Hudspeth, Principles of Neural Science, Vol. 4, McGraw-Hill, New York, 2000.
-
- P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, Y. Nakamura, B. Brezzo, I. Vo, S. K. Esser, R. Appuswamy, B. Taba, A. Amir, M. D. Flickner, W. P. Risk, R. Manohar, D. S. Modha, Science 2014, 345, 668.
Publication types
MeSH terms
Substances
Grants and funding
LinkOut - more resources
Full Text Sources