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High-Speed Ionic Synaptic Memory Based on 2D Titanium Carbide MXene
Department of Materials Science and Engineering, Stanford University, Stanford, 94305, CA, United States; Exponent, 149 Commonwealth Dr, Menlo Park, 94025, CA, United States.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Fibre- and Polymer Technology.
A. J. Drexel Nanomaterials Institute and Department of Materials Science and Engineering, Drexel University, Philadelphia, 19104, PA, United States.
Department of Materials Science and Engineering, Stanford University, Stanford, 94305, CA, United States.
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2022 (English)In: Advanced Functional Materials, ISSN 1616-301X, E-ISSN 1616-3028, Vol. 32, no 12, p. 2109970-, article id 2109970Article in journal (Refereed) Published
Abstract [en]

Synaptic devices with linear high-speed switching can accelerate learning in artificial neural networks (ANNs) embodied in hardware. Conventional resistive memories however suffer from high write noise and asymmetric conductance tuning, preventing parallel programming of ANN arrays. Electrochemical random-access memories (ECRAMs), where resistive switching occurs by ion insertion into a redox-active channel, aim to address these challenges due to their linear switching and low noise. ECRAMs using 2D materials and metal oxides however suffer from slow ion kinetics, whereas organic ECRAMs enable high-speed operation but face challenges toward on-chip integration due to poor temperature stability of polymers. Here, ECRAMs using 2D titanium carbide (Ti3C2Tx) MXene that combine the high speed of organics and the integration compatibility of inorganic materials in a single high-performance device are demonstrated. These ECRAMs combine the speed, linearity, write noise, switching energy, and endurance metrics essential for parallel acceleration of ANNs, and importantly, they are stable after heat treatment needed for back-end-of-line integration with Si electronics. The high speed and performance of these ECRAMs introduces MXenes, a large family of 2D carbides and nitrides with more than 30 stoichiometric compositions synthesized to date, as promising candidates for devices operating at the nexus of electrochemistry and electronics.

Place, publisher, year, edition, pages
Wiley , 2022. Vol. 32, no 12, p. 2109970-, article id 2109970
Keywords [en]
2D materials, analog resistive memories, electrochemical random-access memories, linear switching, mixed ionic–electronic conductors, molecular self-assembly, MXenes, neuromorphic computing, Functional materials, Neural networks, Self assembly, 2d material, Analog resistive memory, Electrochemical random-access memory, Electrochemicals, Mixed ionic-electronic conductors, Molecular self assembly, Random access memory, Resistive memory, Switching
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URN: urn:nbn:se:kth:diva-313255DOI: 10.1002/adfm.202109970ISI: 000720741200001Scopus ID: 2-s2.0-85119507500OAI: oai:DiVA.org:kth-313255DiVA, id: diva2:1669830
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QC 20220615

Available from: 2022-06-15 Created: 2022-06-15 Last updated: 2022-12-12Bibliographically approved

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Kang, MinaTian, WeiqianHamedi, Mahiar

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