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Vertical‐organic‐nanocrystal‐arrays for crossbar memristors with tuning switching dynamics toward neuromorphic computing

Memristors proposed by Leon Chua provide a new type of memory device for novel neuromorphic computing applications. However, the approaching of distinct multi‐intermediate states for tunable switching dynamics, the controlling of conducting filaments (CFs) toward high device repeatability and reprod...

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Bibliographic Details
Published in:SmartMat (Beijing, China) China), 2021-03, Vol.2 (1), p.99-108
Main Authors: Yang, Fangxu, Sun, Lingjie, Duan, Qingxi, Dong, Huanli, Jing, Zhaokun, Yang, Yuchao, Li, Rongjin, Zhang, Xiaotao, Hu, Wenping, Chua, Leon
Format: Article
Language:English
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Summary:Memristors proposed by Leon Chua provide a new type of memory device for novel neuromorphic computing applications. However, the approaching of distinct multi‐intermediate states for tunable switching dynamics, the controlling of conducting filaments (CFs) toward high device repeatability and reproducibility, and the ability for large‐scale preparation devices, remain full of challenges. Here, we show that vertical‐organic‐nanocrystal‐arrays (VONAs) could make a way toward the challenges. The perfect one‐dimensional structure of the VONAs could confine the CFs accurately with fine‐tune resistance states in a broad range of 103 ratios. The availability of large‐area VONAs makes the fabrication of large‐area crossbar memristor arrays facilely, and the analog switching characteristic of the memristors is to effectively imitate different kinds of synaptic plasticity, indicating their great potential in future applications. In this study, vertical‐organic‐nanocrystal‐arrays (VONAs) was developed to construct high‐performance memristors. The unique nanostructure of VONAs could confine conducting filaments accurately, showing fine resistance tuning in a broad ratio. The availability of large‐area VONAs makes the fabrication of large‐area crossbar memristor arrays facilely, and their analog switching characteristic is effective to imitate different kinds of synaptic plasticity, indicating their great potential in future applications.
ISSN:2688-819X
2688-819X
DOI:10.1002/smm2.1022