IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-37623-5.html
   My bibliography  Save this article

A two-dimensional mid-infrared optoelectronic retina enabling simultaneous perception and encoding

Author

Listed:
  • Fakun Wang

    (Nanyang Technological University)

  • Fangchen Hu

    (Nanyang Technological University
    Fudan University)

  • Mingjin Dai

    (Nanyang Technological University)

  • Song Zhu

    (Nanyang Technological University)

  • Fangyuan Sun

    (Nanyang Technological University)

  • Ruihuan Duan

    (Nanyang Technological University)

  • Chongwu Wang

    (Nanyang Technological University)

  • Jiayue Han

    (Nanyang Technological University)

  • Wenjie Deng

    (Nanyang Technological University)

  • Wenduo Chen

    (Nanyang Technological University)

  • Ming Ye

    (Nanyang Technological University)

  • Song Han

    (Nanyang Technological University)

  • Bo Qiang

    (Nanyang Technological University)

  • Yuhao Jin

    (Nanyang Technological University)

  • Yunda Chua

    (Nanyang Technological University)

  • Nan Chi

    (Fudan University)

  • Shaohua Yu

    (Peng Cheng Laboratory)

  • Donguk Nam

    (Nanyang Technological University)

  • Sang Hoon Chae

    (Nanyang Technological University)

  • Zheng Liu

    (Nanyang Technological University)

  • Qi Jie Wang

    (Nanyang Technological University
    Nanyang Technological University)

Abstract

Infrared machine vision system for object perception and recognition is becoming increasingly important in the Internet of Things era. However, the current system suffers from bulkiness and inefficiency as compared to the human retina with the intelligent and compact neural architecture. Here, we present a retina-inspired mid-infrared (MIR) optoelectronic device based on a two-dimensional (2D) heterostructure for simultaneous data perception and encoding. A single device can perceive the illumination intensity of a MIR stimulus signal, while encoding the intensity into a spike train based on a rate encoding algorithm for subsequent neuromorphic computing with the assistance of an all-optical excitation mechanism, a stochastic near-infrared (NIR) sampling terminal. The device features wide dynamic working range, high encoding precision, and flexible adaption ability to the MIR intensity. Moreover, an inference accuracy more than 96% to MIR MNIST data set encoded by the device is achieved using a trained spiking neural network (SNN).

Suggested Citation

  • Fakun Wang & Fangchen Hu & Mingjin Dai & Song Zhu & Fangyuan Sun & Ruihuan Duan & Chongwu Wang & Jiayue Han & Wenjie Deng & Wenduo Chen & Ming Ye & Song Han & Bo Qiang & Yuhao Jin & Yunda Chua & Nan C, 2023. "A two-dimensional mid-infrared optoelectronic retina enabling simultaneous perception and encoding," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37623-5
    DOI: 10.1038/s41467-023-37623-5
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-37623-5
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-37623-5?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. Akhil Dodda & Nicholas Trainor & Joan. M. Redwing & Saptarshi Das, 2022. "All-in-one, bio-inspired, and low-power crypto engines for near-sensor security based on two-dimensional memtransistors," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. Shiva Subbulakshmi Radhakrishnan & Amritanand Sebastian & Aaryan Oberoi & Sarbashis Das & Saptarshi Das, 2021. "A biomimetic neural encoder for spiking neural network," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    3. 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)

    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. Subir Ghosh & Andrew Pannone & Dipanjan Sen & Akshay Wali & Harikrishnan Ravichandran & Saptarshi Das, 2023. "An all 2D bio-inspired gustatory circuit for mimicking physiology and psychology of feeding behavior," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    2. Fanfan Li & Dingwei Li & Chuanqing Wang & Guolei Liu & Rui Wang & Huihui Ren & Yingjie Tang & Yan Wang & Yitong Chen & Kun Liang & Qi Huang & Mohamad Sawan & Min Qiu & Hong Wang & Bowen Zhu, 2024. "An artificial visual neuron with multiplexed rate and time-to-first-spike coding," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    3. Giovanni Maria Matrone & Eveline R. W. Doremaele & Abhijith Surendran & Zachary Laswick & Sophie Griggs & Gang Ye & Iain McCulloch & Francesca Santoro & Jonathan Rivnay & Yoeri Burgt, 2024. "A modular organic neuromorphic spiking circuit for retina-inspired sensory coding and neurotransmitter-mediated neural pathways," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    4. Chong Li & Xinxin Liao & Zhi-Ke Peng & Guang Meng & Qingbo He, 2023. "Highly sensitive and broadband meta-mechanoreceptor via mechanical frequency-division multiplexing," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    5. 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.
    6. Helin Yang & Kwok-Yan Lam & Liang Xiao & Zehui Xiong & Hao Hu & Dusit Niyato & H. Vincent Poor, 2022. "Lead federated neuromorphic learning for wireless edge artificial intelligence," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    7. Tianyu Wang & Jialin Meng & Xufeng Zhou & Yue Liu & Zhenyu He & Qi Han & Qingxuan Li & Jiajie Yu & Zhenhai Li & Yongkai Liu & Hao Zhu & Qingqing Sun & David Wei Zhang & Peining Chen & Huisheng Peng & , 2022. "Reconfigurable neuromorphic memristor network for ultralow-power smart textile electronics," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    8. Hongwei Tan & Sebastiaan van Dijken, 2023. "Dynamic machine vision with retinomorphic photomemristor-reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    9. Muhtasim Ul Karim Sadaf & Najam U Sakib & Andrew Pannone & Harikrishnan Ravichandran & Saptarshi Das, 2023. "A bio-inspired visuotactile neuron for multisensory integration," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    10. 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.
    11. Rui Yuan & Qingxi Duan & Pek Jun Tiw & Ge Li & Zhuojian Xiao & Zhaokun Jing & Ke Yang & Chang Liu & Chen Ge & Ru Huang & Yuchao Yang, 2022. "A calibratable sensory neuron based on epitaxial VO2 for spike-based neuromorphic multisensory system," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    12. Yikai Zheng & Harikrishnan Ravichandran & Thomas F. Schranghamer & Nicholas Trainor & Joan M. Redwing & Saptarshi Das, 2022. "Hardware implementation of Bayesian network based on two-dimensional memtransistors," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    13. Akhil Dodda & Nicholas Trainor & Joan. M. Redwing & Saptarshi Das, 2022. "All-in-one, bio-inspired, and low-power crypto engines for near-sensor security based on two-dimensional memtransistors," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    14. Amritanand Sebastian & Rahul Pendurthi & Azimkhan Kozhakhmetov & Nicholas Trainor & Joshua A. Robinson & Joan M. Redwing & Saptarshi Das, 2022. "Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks," Nature Communications, Nature, vol. 13(1), pages 1-10, 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:14:y:2023:i:1:d:10.1038_s41467-023-37623-5. 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.