Literature DB >> 29067743

Atomically Thin Femtojoule Memristive Device.

Huan Zhao1, Zhipeng Dong2, He Tian1, Don DiMarzi3, Myung-Geun Han4, Lihua Zhang5, Xiaodong Yan1, Fanxin Liu6, Lang Shen1, Shu-Jen Han7, Steve Cronin1, Wei Wu1, Jesse Tice3, Jing Guo2, Han Wang1.   

Abstract

The morphology and dimension of the conductive filament formed in a memristive device are strongly influenced by the thickness of its switching medium layer. Aggressive scaling of this active layer thickness is critical toward reducing the operating current, voltage, and energy consumption in filamentary-type memristors. Previously, the thickness of this filament layer has been limited to above a few nanometers due to processing constraints, making it challenging to further suppress the on-state current and the switching voltage. Here, the formation of conductive filaments in a material medium with sub-nanometer thickness formed through the oxidation of atomically thin two-dimensional boron nitride is studied. The resulting memristive device exhibits sub-nanometer filamentary switching with sub-pA operation current and femtojoule per bit energy consumption. Furthermore, by confining the filament to the atomic scale, current switching characteristics are observed that are distinct from that in thicker medium due to the profoundly different atomic kinetics. The filament morphology in such an aggressively scaled memristive device is also theoretically explored. These ultralow energy devices are promising for realizing femtojoule and sub-femtojoule electronic computation, which can be attractive for applications in a wide range of electronics systems that desire ultralow power operation.
© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  2D materials; femtojoules; hexagonal boron nitride (h-BN); memory; memristors; ultra-low power

Year:  2017        PMID: 29067743     DOI: 10.1002/adma.201703232

Source DB:  PubMed          Journal:  Adv Mater        ISSN: 0935-9648            Impact factor:   30.849


  6 in total

1.  Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing.

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2.  Wafer-scale solution-processed 2D material analog resistive memory array for memory-based computing.

Authors:  Baoshan Tang; Hasita Veluri; Yida Li; Zhi Gen Yu; Moaz Waqar; Jin Feng Leong; Maheswari Sivan; Evgeny Zamburg; Yong-Wei Zhang; John Wang; Aaron V-Y Thean
Journal:  Nat Commun       Date:  2022-06-01       Impact factor: 17.694

3.  In-sensor reservoir computing for language learning via two-dimensional memristors.

Authors:  Linfeng Sun; Zhongrui Wang; Jinbao Jiang; Yeji Kim; Bomin Joo; Shoujun Zheng; Seungyeon Lee; Woo Jong Yu; Bai-Sun Kong; Heejun Yang
Journal:  Sci Adv       Date:  2021-05-14       Impact factor: 14.136

4.  Graphene-oxide interface for optoelectronic synapse application.

Authors:  Ricardo Martinez-Martinez; Molla Manjurul Islam; Adithi Krishnaprasad; Tania Roy
Journal:  Sci Rep       Date:  2022-04-07       Impact factor: 4.379

5.  Flexible synaptic floating gate devices with dual electrical modulation based on ambipolar black phosphorus.

Authors:  Xiong Xiong; Xin Wang; Qianlan Hu; Xuefei Li; Yanqing Wu
Journal:  iScience       Date:  2022-02-18

6.  A hardware Markov chain algorithm realized in a single device for machine learning.

Authors:  He Tian; Xue-Feng Wang; Mohammad Ali Mohammad; Guang-Yang Gou; Fan Wu; Yi Yang; Tian-Ling Ren
Journal:  Nat Commun       Date:  2018-10-17       Impact factor: 14.919

  6 in total

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