IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-42116-6.html
   My bibliography  Save this article

Graphene/silicon heterojunction for reconfigurable phase-relevant activation function in coherent optical neural networks

Author

Listed:
  • Chuyu Zhong

    (Zhejiang University)

  • Kun Liao

    (Peking University)

  • Tianxiang Dai

    (Peking University)

  • Maoliang Wei

    (Zhejiang University)

  • Hui Ma

    (Zhejiang University)

  • Jianghong Wu

    (Westlake University
    Westlake Institute for Advanced Study)

  • Zhibin Zhang

    (Peking University)

  • Yuting Ye

    (Westlake University
    Westlake Institute for Advanced Study)

  • Ye Luo

    (Westlake University
    Westlake Institute for Advanced Study)

  • Zequn Chen

    (Westlake University
    Westlake Institute for Advanced Study)

  • Jialing Jian

    (Westlake University
    Westlake Institute for Advanced Study)

  • Chunlei Sun

    (Westlake University
    Westlake Institute for Advanced Study)

  • Bo Tang

    (Institute of Microelectronics of the Chinese Academy of Sciences)

  • Peng Zhang

    (Institute of Microelectronics of the Chinese Academy of Sciences)

  • Ruonan Liu

    (Institute of Microelectronics of the Chinese Academy of Sciences)

  • Junying Li

    (Zhejiang University)

  • Jianyi Yang

    (Zhejiang University)

  • Lan Li

    (Westlake University
    Westlake Institute for Advanced Study)

  • Kaihui Liu

    (Peking University)

  • Xiaoyong Hu

    (Peking University)

  • Hongtao Lin

    (Zhejiang University
    Zhejiang University)

Abstract

Optical neural networks (ONNs) herald a new era in information and communication technologies and have implemented various intelligent applications. In an ONN, the activation function (AF) is a crucial component determining the network performances and on-chip AF devices are still in development. Here, we first demonstrate on-chip reconfigurable AF devices with phase activation fulfilled by dual-functional graphene/silicon (Gra/Si) heterojunctions. With optical modulation and detection in one device, time delays are shorter, energy consumption is lower, reconfigurability is higher and the device footprint is smaller than other on-chip AF strategies. The experimental modulation voltage (power) of our Gra/Si heterojunction achieves as low as 1 V (0.5 mW), superior to many pure silicon counterparts. In the photodetection aspect, a high responsivity of over 200 mA/W is realized. Special nonlinear functions generated are fed into a complex-valued ONN to challenge handwritten letters and image recognition tasks, showing improved accuracy and potential of high-efficient, all-component-integration on-chip ONN. Our results offer new insights for on-chip ONN devices and pave the way to high-performance integrated optoelectronic computing circuits.

Suggested Citation

  • Chuyu Zhong & Kun Liao & Tianxiang Dai & Maoliang Wei & Hui Ma & Jianghong Wu & Zhibin Zhang & Yuting Ye & Ye Luo & Zequn Chen & Jialing Jian & Chunlei Sun & Bo Tang & Peng Zhang & Ruonan Liu & Junyin, 2023. "Graphene/silicon heterojunction for reconfigurable phase-relevant activation function in coherent optical neural networks," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42116-6
    DOI: 10.1038/s41467-023-42116-6
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-42116-6
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-42116-6?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. J. Feldmann & N. Youngblood & C. D. Wright & H. Bhaskaran & W. H. P. Pernice, 2019. "All-optical spiking neurosynaptic networks with self-learning capabilities," Nature, Nature, vol. 569(7755), pages 208-214, May.
    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. Sajjad Abdollahramezani & Omid Hemmatyar & Mohammad Taghinejad & Hossein Taghinejad & Alex Krasnok & Ali A. Eftekhar & Christian Teichrib & Sanchit Deshmukh & Mostafa A. El-Sayed & Eric Pop & Matthias, 2022. "Electrically driven reprogrammable phase-change metasurface reaching 80% efficiency," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Ui Yeon Won & Quoc An Vu & Sung Bum Park & Mi Hyang Park & Van Dam Do & Hyun Jun Park & Heejun Yang & Young Hee Lee & Woo Jong Yu, 2023. "Multi-neuron connection using multi-terminal floating–gate memristor for unsupervised learning," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    3. Xiaoyun Yuan & Yong Wang & Zhihao Xu & Tiankuang Zhou & Lu Fang, 2023. "Training large-scale optoelectronic neural networks with dual-neuron optical-artificial learning," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    4. Xiangyan Meng & Guojie Zhang & Nuannuan Shi & Guangyi Li & José Azaña & José Capmany & Jianping Yao & Yichen Shen & Wei Li & Ninghua Zhu & Ming Li, 2023. "Compact optical convolution processing unit based on multimode interference," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    5. Elena Goi & Steffen Schoenhardt & Min Gu, 2022. "Direct retrieval of Zernike-based pupil functions using integrated diffractive deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    6. Steven Becker & Dirk Englund & Birgit Stiller, 2024. "An optoacoustic field-programmable perceptron for recurrent neural networks," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    7. Jian Yao & Qinan Wang & Yong Zhang & Yu Teng & Jing Li & Pin Zhao & Chun Zhao & Ziyi Hu & Zongjie Shen & Liwei Liu & Dan Tian & Song Qiu & Zhongrui Wang & Lixing Kang & Qingwen Li, 2024. "Ultra-low power carbon nanotube/porphyrin synaptic arrays for persistent photoconductivity and neuromorphic computing," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    8. Bassem Tossoun & Di Liang & Stanley Cheung & Zhuoran Fang & Xia Sheng & John Paul Strachan & Raymond G. Beausoleil, 2024. "High-speed and energy-efficient non-volatile silicon photonic memory based on heterogeneously integrated memresonator," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    9. 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.
    10. Yang Shi & Junyu Ren & Guanyu Chen & Wei Liu & Chuqi Jin & Xiangyu Guo & Yu Yu & Xinliang Zhang, 2022. "Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    11. Minati, Ludovico & Mancinelli, Mattia & Frasca, Mattia & Bettotti, Paolo & Pavesi, Lorenzo, 2021. "An analog electronic emulator of non-linear dynamics in optical microring resonators," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    12. Junwei Cheng & Chaoran Huang & Jialong Zhang & Bo Wu & Wenkai Zhang & Xinyu Liu & Jiahui Zhang & Yiyi Tang & Hailong Zhou & Qiming Zhang & Min Gu & Jianji Dong & Xinliang Zhang, 2024. "Multimodal deep learning using on-chip diffractive optics with in situ training capability," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    13. Jin Zhao & Wen-Xiong Song & Tianjiao Xin & Zhitang Song, 2021. "Rules of hierarchical melt and coordinate bond to design crystallization in doped phase change materials," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    14. 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.
    15. He-Shan Zhang & Xue-Mei Dong & Zi-Cheng Zhang & Ze-Pu Zhang & Chao-Yi Ban & Zhe Zhou & Cheng Song & Shi-Qi Yan & Qian Xin & Ju-Qing Liu & Yin-Xiang Li & Wei Huang, 2022. "Co-assembled perylene/graphene oxide photosensitive heterobilayer for efficient neuromorphics," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    16. 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.
    17. Wen Zhou & Bowei Dong & Nikolaos Farmakidis & Xuan Li & Nathan Youngblood & Kairan Huang & Yuhan He & C. David Wright & Wolfram H. P. Pernice & Harish Bhaskaran, 2023. "In-memory photonic dot-product engine with electrically programmable weight banks," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    18. Ali Momeni & Romain Fleury, 2022. "Electromagnetic wave-based extreme deep learning with nonlinear time-Floquet entanglement," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    19. Michele Cotrufo & Akshaj Arora & Sahitya Singh & Andrea Alù, 2023. "Dispersion engineered metasurfaces for broadband, high-NA, high-efficiency, dual-polarization analog image processing," Nature Communications, Nature, vol. 14(1), pages 1-9, 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_s41467-023-42116-6. 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.