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High-precision and linear weight updates by subnanosecond pulses in ferroelectric tunnel junction for neuro-inspired computing

Author

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  • Zhen Luo

    (University of Science and Technology of China)

  • Zijian Wang

    (University of Science and Technology of China)

  • Zeyu Guan

    (University of Science and Technology of China)

  • Chao Ma

    (University of Science and Technology of China)

  • Letian Zhao

    (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)

  • He Wang

    (University of Science and Technology of China)

  • Yue Lin

    (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
    Nanjing University)

Abstract

The rapid development of neuro-inspired computing demands synaptic devices with ultrafast speed, low power consumption, and multiple non-volatile states, among other features. Here, a high-performance synaptic device is designed and established based on a Ag/PbZr0.52Ti0.48O3 (PZT, (111)-oriented)/Nb:SrTiO3 ferroelectric tunnel junction (FTJ). The advantages of (111)-oriented PZT (~1.2 nm) include its multiple ferroelectric switching dynamics, ultrafine ferroelectric domains, and small coercive voltage. The FTJ shows high-precision (256 states, 8 bits), reproducible (cycle-to-cycle variation, ~2.06%), linear (nonlinearity 109 cycles and an ultralow write energy consumption. In particular, manipulations among 150 states are realized under subnanosecond (~630 ps) pulse voltages ≤5 V, and the fastest resistance switching at 300 ps for the FTJs is achieved by voltages

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28303-x
    DOI: 10.1038/s41467-022-28303-x
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    Cited by:

    1. Jangsaeng Kim & Eun Chan Park & Wonjun Shin & Ryun-Han Koo & Chang-Hyeon Han & He Young Kang & Tae Gyu Yang & Youngin Goh & Kilho Lee & Daewon Ha & Suraj S. Cheema & Jae Kyeong Jeong & Daewoong Kwon, 2024. "Analog reservoir computing via ferroelectric mixed phase boundary transistors," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Tao Li & Yongyi Wu & Guoliang Yu & Shengxian Li & Yifeng Ren & Yadong Liu & Jiarui Liu & Hao Feng & Yu Deng & Mingxing Chen & Zhenyu Zhang & Tai Min, 2024. "Realization of sextuple polarization states and interstate switching in antiferroelectric CuInP2S6," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    3. Zhiwei Chen & Wenjie Li & Zhen Fan & Shuai Dong & Yihong Chen & Minghui Qin & Min Zeng & Xubing Lu & Guofu Zhou & Xingsen Gao & Jun-Ming Liu, 2023. "All-ferroelectric implementation of reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    4. Haipeng Lin & Jiali Ou & Zhen Fan & Xiaobing Yan & Wenjie Hu & Boyuan Cui & Jikang Xu & Wenjie Li & Zhiwei Chen & Biao Yang & Kun Liu & Linyuan Mo & Meixia Li & Xubing Lu & Guofu Zhou & Xingsen Gao & , 2025. "In situ training of an in-sensor artificial neural network based on ferroelectric photosensors," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    5. 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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