Article Title

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

Authors

Ye Tian, National Center for Nanoscience and Technology (NCNST), China. No.11, Beiyitiao, Zhongguancun, Beijing 100190, China Hunan City University, Yiyang, Hunan, 413000, China
Chuangfei Guo, Department of Physics, Boston College, Chestnut Hill, MA 02467, USA
Shengming Guo, National Center for Nanoscience and Technology (NCNST), China. No.11, Beiyitiao, Zhongguancun, Beijing 100190, China
Taifung Yu, Department of Physics, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
Qian Liu, National Center for Nanoscience and Technology (NCNST), China. No.11, Beiyitiao, Zhongguancun, Beijing 100190, China Taida Applied Physics School, Nankai University, Tianjin 300457, China

Keywords

interface memristor, resistive switching, synaptic electronics, nanostructure

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.

Graphical Abstract

DOI

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

Publisher

Tsinghua University Press

Recommended Citation

Ye Tian,Chuangfei Guo,Shengming Guo,Taifung Yu,Qian Liu, Bivariate-continuous-tunable interface memristor based on Bi2S3 nested nano-networks. NanoRes.2014, 7(7): 953–962