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Bivariate-continuous-tunable interface memristor based on Bi2S3 nested nano-networks

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Abstract

A memristor that can emulate biological synapses is a promising basic-processing unit in neural-network computation. Here we propose a new-conceptual memristor based on a memoristive interface composed of two types of non-memristive materials, successfully realizing continuously tunable resistance controlled by both voltage (current) and applied time of a single pulse with a swift response comparable with synapses. The brain-like memorizing capability of the memristor is demonstrated. The memoristive mechanism in the interface is thought to be dominated by a Schottky barrier tuned by the capture/release of the carriers in interface traps with dispersive energy.

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Authors and Affiliations

  1. National Center for Nanoscience and Technology (NCNST), China. No.11, Beiyitiao, Zhongguancun, Beijing, 100190, China

    Ye Tian, Shengming Guo & Qian Liu

  2. Hunan City University, Yiyang, Hunan, 413000, China

    Ye Tian

  3. Department of Physics, Boston College, Chestnut Hill, MA, 02467, USA

    Chuangfei Guo

  4. Taida Applied Physics School, Nankai University, Tianjin, 300457, China

    Qian Liu

  5. Department of Physics, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China

    Taifung Yu

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  1. Ye Tian
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  2. Chuangfei Guo
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  3. Shengming Guo
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  4. Taifung Yu
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  5. Qian Liu
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Correspondence to Qian Liu.

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Tian, Y., Guo, C., Guo, S. et al. Bivariate-continuous-tunable interface memristor based on Bi2S3 nested nano-networks. Nano Res. 7, 953–962 (2014). https://doi.org/10.1007/s12274-014-0456-5

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  • DOI: https://doi.org/10.1007/s12274-014-0456-5

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