IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-34230-8.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-34230-8
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-34230-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Chao Du & Fuxi Cai & Mohammed A. Zidan & Wen Ma & Seung Hwan Lee & Wei D. Lu, 2017. "Reservoir computing using dynamic memristors for temporal information processing," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    2. Tao Chen & Jeroen van Gelder & Bram van de Ven & Sergey V. Amitonov & Bram de Wilde & Hans-Christian Ruiz Euler & Hajo Broersma & Peter A. Bobbert & Floris A. Zwanenburg & Wilfred G. van der Wiel, 2020. "Classification with a disordered dopant-atom network in silicon," Nature, Nature, vol. 577(7790), pages 341-345, January.
    3. Peng Yao & Huaqiang Wu & Bin Gao & Jianshi Tang & Qingtian Zhang & Wenqiang Zhang & J. Joshua Yang & He Qian, 2020. "Fully hardware-implemented memristor convolutional neural network," Nature, Nature, vol. 577(7792), pages 641-646, January.
    4. Hongwei Tan & Yifan Zhou & Quanzheng Tao & Johanna Rosen & Sebastiaan van Dijken, 2021. "Bioinspired multisensory neural network with crossmodal integration and recognition," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    5. Dmitri B. Strukov & Gregory S. Snider & Duncan R. Stewart & R. Stanley Williams, 2008. "The missing memristor found," Nature, Nature, vol. 453(7191), pages 80-83, May.
    6. Anil K. Jain, 2007. "Biometric recognition," Nature, Nature, vol. 449(7158), pages 38-40, September.
    7. Nikolai C. Passler & Xiang Ni & Guangwei Hu & Joseph R. Matson & Giulia Carini & Martin Wolf & Mathias Schubert & Andrea Alù & Joshua D. Caldwell & Thomas G. Folland & Alexander Paarmann, 2022. "Hyperbolic shear polaritons in low-symmetry crystals," Nature, Nature, vol. 602(7898), pages 595-600, February.
    8. L. Appeltant & M.C. Soriano & G. Van der Sande & J. Danckaert & S. Massar & J. Dambre & B. Schrauwen & C.R. Mirasso & I. Fischer, 2011. "Information processing using a single dynamical node as complex system," Nature Communications, Nature, vol. 2(1), pages 1-6, September.
    9. Yanan Zhong & Jianshi Tang & Xinyi Li & Bin Gao & He Qian & Huaqiang Wu, 2021. "Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    10. Hongwei Tan & Quanzheng Tao & Ishan Pande & Sayani Majumdar & Fu Liu & Yifan Zhou & Per O. Å. Persson & Johanna Rosen & Sebastiaan van Dijken, 2020. "Tactile sensory coding and learning with bio-inspired optoelectronic spiking afferent nerves," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiangpeng Liang & Yanan Zhong & Jianshi Tang & Zhengwu Liu & Peng Yao & Keyang Sun & Qingtian Zhang & Bin Gao & Hadi Heidari & He Qian & Huaqiang Wu, 2022. "Rotating neurons for all-analog implementation of cyclic reservoir computing," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. 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.
    3. Changsong Gao & Di Liu & Chenhui Xu & Weidong Xie & Xianghong Zhang & Junhua Bai & Zhixian Lin & Cheng Zhang & Yuanyuan Hu & Tailiang Guo & Huipeng Chen, 2024. "Toward grouped-reservoir computing: organic neuromorphic vertical transistor with distributed reservoir states for efficient recognition and prediction," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    4. 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.
    5. Rohit Abraham John & Yiğit Demirağ & Yevhen Shynkarenko & Yuliia Berezovska & Natacha Ohannessian & Melika Payvand & Peng Zeng & Maryna I. Bodnarchuk & Frank Krumeich & Gökhan Kara & Ivan Shorubalko &, 2022. "Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    6. Hongwei Tan & Sebastiaan van Dijken, 2023. "Dynamic machine vision with retinomorphic photomemristor-reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    7. Zhiyuan Li & Zhongshao Li & Wei Tang & Jiaping Yao & Zhipeng Dou & Junjie Gong & Yongfei Li & Beining Zhang & Yunxiao Dong & Jian Xia & Lin Sun & Peng Jiang & Xun Cao & Rui Yang & Xiangshui Miao & Ron, 2024. "Crossmodal sensory neurons based on high-performance flexible memristors for human-machine in-sensor computing system," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    8. Zhang, Shaohua & Zhang, Hongli & Wang, Cong & Lin, Hairong, 2024. "Bionic modeling and dynamics analysis of heterogeneous brain regions connected by memristive synaptic crosstalk," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    9. Yanming Liu & He Tian & Fan Wu & Anhan Liu & Yihao Li & Hao Sun & Mario Lanza & Tian-Ling Ren, 2023. "Cellular automata imbedded memristor-based recirculated logic in-memory computing," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    10. Min Yan & Can Huang & Peter Bienstman & Peter Tino & Wei Lin & Jie Sun, 2024. "Emerging opportunities and challenges for the future of reservoir computing," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    11. Min, Fuhong & Zhang, Wen & Ji, Ziyi & Zhang, Lei, 2021. "Switching dynamics of a non-autonomous FitzHugh-Nagumo circuit with piecewise-linear flux-controlled memristor," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    12. Liying Xu & Jiadi Zhu & Bing Chen & Zhen Yang & Keqin Liu & Bingjie Dang & Teng Zhang & Yuchao Yang & Ru Huang, 2022. "A distributed nanocluster based multi-agent evolutionary network," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    13. Zhuohui Liu & Qinghua Zhang & Donggang Xie & Mingzhen Zhang & Xinyan Li & Hai Zhong & Ge Li & Meng He & Dashan Shang & Can Wang & Lin Gu & Guozhen Yang & Kuijuan Jin & Chen Ge, 2023. "Interface-type tunable oxygen ion dynamics for physical reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    14. Ruomin Zhu & Sam Lilak & Alon Loeffler & Joseph Lizier & Adam Stieg & James Gimzewski & Zdenka Kuncic, 2023. "Online dynamical learning and sequence memory with neuromorphic nanowire networks," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    15. See-On Park & Hakcheon Jeong & Jongyong Park & Jongmin Bae & Shinhyun Choi, 2022. "Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    16. Zhong, Huiyan & Li, Guodong & Xu, Xiangliang, 2022. "A generic voltage-controlled discrete memristor model and its application in chaotic map," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    17. Ke Yang & Yanghao Wang & Pek Jun Tiw & Chaoming Wang & Xiaolong Zou & Rui Yuan & Chang Liu & Ge Li & Chen Ge & Si Wu & Teng Zhang & Ru Huang & Yuchao Yang, 2024. "High-order sensory processing nanocircuit based on coupled VO2 oscillators," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    18. Sanghyeon Choi & Jaeho Shin & Gwanyeong Park & Jung Sun Eo & Jingon Jang & J. Joshua Yang & Gunuk Wang, 2024. "3D-integrated multilayered physical reservoir array for learning and forecasting time-series information," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    19. Ren, Lujie & Mou, Jun & Banerjee, Santo & Zhang, Yushu, 2023. "A hyperchaotic map with a new discrete memristor model: Design, dynamical analysis, implementation and application," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    20. Yuchun Zhang & Lin Liu & Bin Tu & Bin Cui & Jiahui Guo & Xing Zhao & Jingyu Wang & Yong Yan, 2023. "An artificial synapse based on molecular junctions," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34230-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.