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Programmable ferroelectric bionic vision hardware with selective attention for high-precision image classification

Author

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  • Rengjian Yu

    (National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University
    Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China)

  • Lihua He

    (National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University)

  • Changsong Gao

    (National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University)

  • Xianghong Zhang

    (National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University)

  • Enlong Li

    (Department of Materials Science, Fudan University
    Zhangjiang Fudan International Innovation Center, Fudan University)

  • Tailiang Guo

    (National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University
    Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China)

  • Wenwu Li

    (Department of Materials Science, Fudan University
    Zhangjiang Fudan International Innovation Center, Fudan University)

  • Huipeng Chen

    (National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University
    Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China)

Abstract

Selective attention is an efficient processing strategy to allocate computational resources for pivotal optical information. However, the hardware implementation of selective visual attention in conventional intelligent system is usually bulky and complex along with high computational cost. Here, programmable ferroelectric bionic vision hardware to emulate the selective attention is proposed. The tunneling effect of photogenerated carriers are controlled by dynamic variation of energy barrier, enabling the modulation of memory strength from 9.1% to 47.1% without peripheral storage unit. The molecular polarization of ferroelectric P(VDF-TrFE) layer enables a single device not only multiple nonvolatile states but also the implementation of selective attention. With these ferroelectric devices are arrayed together, UV light information can be selectively recorded and suppressed the with high current decibel level. Furthermore, the device with positive polarization exhibits high wavelength dependence in the image attention processing, and the fabricated ferroelectric sensory network exhibits high accuracy of 95.7% in the pattern classification for multi-wavelength images. This study can enrich the neuromorphic functions of bioinspired sensing devices and pave the way for profound implications of future bioinspired optoelectronics.

Suggested Citation

  • Rengjian Yu & Lihua He & Changsong Gao & Xianghong Zhang & Enlong Li & Tailiang Guo & Wenwu Li & Huipeng Chen, 2022. "Programmable ferroelectric bionic vision hardware with selective attention for high-precision image classification," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34565-2
    DOI: 10.1038/s41467-022-34565-2
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    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. Sören Boyn & Julie Grollier & Gwendal Lecerf & Bin Xu & Nicolas Locatelli & Stéphane Fusil & Stéphanie Girod & Cécile Carrétéro & Karin Garcia & Stéphane Xavier & Jean Tomas & Laurent Bellaiche & Manu, 2017. "Learning through ferroelectric domain dynamics in solid-state synapses," Nature Communications, Nature, vol. 8(1), pages 1-7, April.
    3. C. Shawn Green & Daphne Bavelier, 2003. "Action video game modifies visual selective attention," Nature, Nature, vol. 423(6939), pages 534-537, May.
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    Cited by:

    1. Liuting Shan & Qizhen Chen & Rengjian Yu & Changsong Gao & Lujian Liu & Tailiang Guo & Huipeng Chen, 2023. "A sensory memory processing system with multi-wavelength synaptic-polychromatic light emission for multi-modal information recognition," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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