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Sub-nanosecond memristor based on ferroelectric tunnel junction

Author

Listed:
  • Chao Ma

    (University of Science and Technology of China)

  • Zhen Luo

    (University of Science and Technology of China)

  • Weichuan Huang

    (University of Science and Technology of China)

  • Letian Zhao

    (University of Science and Technology of China)

  • Qiaoling Chen

    (University of Science and Technology of China)

  • Yue Lin

    (University of Science and Technology of China)

  • Xiang Liu

    (University of Science and Technology of China)

  • Zhiwei Chen

    (University of Science and Technology of China)

  • Chuanchuan Liu

    (University of Science and Technology of China)

  • Haoyang Sun

    (University of Science and Technology of China)

  • Xi Jin

    (University of Science and Technology of China)

  • Yuewei Yin

    (University of Science and Technology of China)

  • Xiaoguang Li

    (University of Science and Technology of China
    Institute of Solid State Physics, CAS
    Collaborative Innovation Center of Advanced Microstructures)

Abstract

Next-generation non-volatile memories with ultrafast speed, low power consumption, and high density are highly desired in the era of big data. Here, we report a high performance memristor based on a Ag/BaTiO3/Nb:SrTiO3 ferroelectric tunnel junction (FTJ) with the fastest operation speed (600 ps) and the highest number of states (32 states or 5 bits) per cell among the reported FTJs. The sub-nanosecond resistive switching maintains up to 358 K, and the write current density is as low as 4 × 103 A cm−2. The functionality of spike-timing-dependent plasticity served as a solid synaptic device is also obtained with ultrafast operation. Furthermore, it is demonstrated that a Nb:SrTiO3 electrode with a higher carrier concentration and a metal electrode with lower work function tend to improve the operation speed. These results may throw light on the way for overcoming the storage performance gap between different levels of the memory hierarchy and developing ultrafast neuromorphic computing systems.

Suggested Citation

  • Chao Ma & Zhen Luo & Weichuan Huang & Letian Zhao & Qiaoling Chen & Yue Lin & Xiang Liu & Zhiwei Chen & Chuanchuan Liu & Haoyang Sun & Xi Jin & Yuewei Yin & Xiaoguang Li, 2020. "Sub-nanosecond memristor based on ferroelectric tunnel junction," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15249-1
    DOI: 10.1038/s41467-020-15249-1
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    Cited by:

    1. Boyuan Cui & Zhen Fan & Wenjie Li & Yihong Chen & Shuai Dong & Zhengwei Tan & Shengliang Cheng & Bobo Tian & Ruiqiang Tao & Guo Tian & Deyang Chen & Zhipeng Hou & Minghui Qin & Min Zeng & Xubing Lu & , 2022. "Ferroelectric photosensor network: an advanced hardware solution to real-time machine vision," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. Zhen Luo & Zijian Wang & Zeyu Guan & Chao Ma & Letian Zhao & Chuanchuan Liu & Haoyang Sun & He Wang & Yue Lin & Xi Jin & Yuewei Yin & Xiaoguang Li, 2022. "High-precision and linear weight updates by subnanosecond pulses in ferroelectric tunnel junction for neuro-inspired computing," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    3. Jing Wang & Deshan Liang & Jing Ma & Yuanyuan Fan & Ji Ma & Hasnain Mehdi Jafri & Huayu Yang & Qinghua Zhang & Yue Wang & Changqing Guo & Shouzhe Dong & Di Liu & Xueyun Wang & Jiawang Hong & Nan Zhang, 2023. "Polar Solomon rings in ferroelectric nanocrystals," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    4. Yueyang Jia & Qianqian Yang & Yue-Wen Fang & Yue Lu & Maosong Xie & Jianyong Wei & Jianjun Tian & Linxing Zhang & Rui Yang, 2024. "Giant tunnelling electroresistance in atomic-scale ferroelectric tunnel junctions," Nature Communications, Nature, vol. 15(1), pages 1-9, December.

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