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

Kireev, Dmitry; Liu, Samuel; Jin, Harrison; Xiao, T. Patrick; Bennett, Christopher H.; Akinwande, Deji; Incorvia, Jean Anne C.

doi:10.1038/s41467-022-32078-6

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

Journal Article · Thu Jul 28 00:00:00 EDT 2022 · Nature Communications

DOI:https://doi.org/10.1038/s41467-022-32078-6· OSTI ID:2469903

^[1]; ^[1]; ^[1]; Xiao, T. Patrick ^[2]; Bennett, Christopher H. ^[2]; ^[1]; ^[1]

Univ. of Texas, Austin, TX (United States)
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

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/µm² 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.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: National Science Foundation (NSF); US Department of the Navy, Office of Naval Research (ONR); USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 2469903

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 13; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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