Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing.

Autor: Kireev D; Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.; Microelectronics Research Center, The University of Texas at Austin, Austin, TX, 78758, USA., Liu S; Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA., Jin H; Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA., Patrick Xiao T; Sandia National Laboratories, Albuquerque, NM, 87123, USA., Bennett CH; Sandia National Laboratories, Albuquerque, NM, 87123, USA., Akinwande D; Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.; Microelectronics Research Center, The University of Texas at Austin, Austin, TX, 78758, USA., Incorvia JAC; Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA. incorvia@austin.utexas.edu.; Microelectronics Research Center, The University of Texas at Austin, Austin, TX, 78758, USA. incorvia@austin.utexas.edu.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2022 Jul 28; Vol. 13 (1), pp. 4386. Date of Electronic Publication: 2022 Jul 28.
DOI: 10.1038/s41467-022-32078-6
Abstrakt: CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm 2 switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.
(© 2022. The Author(s).)
Databáze: MEDLINE
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