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In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array

Author

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  • Zhongfang Zhang

    (University of Science and Technology of China)

  • Xiaolong Zhao

    (University of Science and Technology of China)

  • Xumeng Zhang

    (Fudan University)

  • Xiaohu Hou

    (University of Science and Technology of China)

  • Xiaolan Ma

    (University of Science and Technology of China)

  • Shuangzhu Tang

    (Fudan University)

  • Ying Zhang

    (University of Science and Technology of China)

  • Guangwei Xu

    (University of Science and Technology of China)

  • Qi Liu

    (Fudan University)

  • Shibing Long

    (University of Science and Technology of China)

Abstract

Detection and recognition of latent fingerprints play crucial roles in identification and security. However, the separation of sensor, memory, and processor in conventional ex-situ fingerprint recognition system seriously deteriorates the latency of decision-making and inevitably increases the overall computing power. In this work, a photoelectronic reservoir computing (RC) system, consisting of DUV photo-synapses and nonvolatile memristor array, is developed to detect and recognize the latent fingerprint with in-sensor and parallel in-memory computing. Through the Ga-rich design, we achieve amorphous GaOx (a-GaOx) photo-synapses with an enhanced persistent photoconductivity (PPC) effect. The PPC effect, which induces nonlinearly tunable conductivity, renders the a-GaOx photo-synapses an ideal deep ultraviolet (DUV) photoelectronic reservoir, thus mapping the complex input vector into a dimensionality-reduced output vector. Connecting the reservoirs and a memristor array, we further construct an in-sensor RC system for latent fingerprint identification. The system maintains over 90% recognition accuracy for latent fingerprint within 15% stochastic noise level via the proposed dual-feature strategy. This work provides a subversive prototype system of DUV in-sensor RC for highly efficient recognition of latent fingerprints.

Suggested Citation

  • Zhongfang Zhang & Xiaolong Zhao & Xumeng Zhang & Xiaohu Hou & Xiaolan Ma & Shuangzhu Tang & Ying Zhang & Guangwei Xu & Qi Liu & Shibing Long, 2022. "In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34230-8
    DOI: 10.1038/s41467-022-34230-8
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    References listed on IDEAS

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    1. Pengzhan Li & Mingzhen Zhang & Qingli Zhou & Qinghua Zhang & Donggang Xie & Ge Li & Zhuohui Liu & Zheng Wang & Erjia Guo & Meng He & Can Wang & Lin Gu & Guozhen Yang & Kuijuan Jin & Chen Ge, 2024. "Reconfigurable optoelectronic transistors for multimodal recognition," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    2. Pei-Yu Huang & Bi-Yi Jiang & Hong-Ji Chen & Jia-Yi Xu & Kang Wang & Cheng-Yi Zhu & Xin-Yan Hu & Dong Li & Liang Zhen & Fei-Chi Zhou & Jing-Kai Qin & Cheng-Yan Xu, 2023. "Neuro-inspired optical sensor array for high-accuracy static image recognition and dynamic trace extraction," Nature Communications, Nature, vol. 14(1), pages 1-9, December.

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