IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-44773-7.html
   My bibliography  Save this article

High-speed and energy-efficient non-volatile silicon photonic memory based on heterogeneously integrated memresonator

Author

Listed:
  • Bassem Tossoun

    (Hewlett Packard Enterprise)

  • Di Liang

    (Hewlett Packard Enterprise
    University of Michigan, Department of Electrical and Computer Engineering)

  • Stanley Cheung

    (Hewlett Packard Enterprise)

  • Zhuoran Fang

    (Hewlett Packard Enterprise)

  • Xia Sheng

    (Hewlett Packard Enterprise)

  • John Paul Strachan

    (Hewlett Packard Enterprise
    PGI-14, Forschungszentrum Jülich GmbH)

  • Raymond G. Beausoleil

    (Hewlett Packard Enterprise)

Abstract

Recently, interest in programmable photonics integrated circuits has grown as a potential hardware framework for deep neural networks, quantum computing, and field programmable arrays (FPGAs). However, these circuits are constrained by the limited tuning speed and large power consumption of the phase shifters used. In this paper, we introduce the memresonator, a metal-oxide memristor heterogeneously integrated with a microring resonator, as a non-volatile silicon photonic phase shifter. These devices are capable of retention times of 12 hours, switching voltages lower than 5 V, and an endurance of 1000 switching cycles. Also, these memresonators have been switched using 300 ps long voltage pulses with a record low switching energy of 0.15 pJ. Furthermore, these memresonators are fabricated on a heterogeneous III-V-on-Si platform capable of integrating a rich family of active and passive optoelectronic devices directly on-chip to enable in-memory photonic computing and further advance the scalability of integrated photonic processors.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-44773-7
    DOI: 10.1038/s41467-024-44773-7
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-44773-7
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-44773-7?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. Ying Zhang & Ge-Qi Mao & Xiaolong Zhao & Yu Li & Meiyun Zhang & Zuheng Wu & Wei Wu & Huajun Sun & Yizhong Guo & Lihua Wang & Xumeng Zhang & Qi Liu & Hangbing Lv & Kan-Hao Xue & Guangwei Xu & Xiangshui, 2021. "Evolution of the conductive filament system in HfO2-based memristors observed by direct atomic-scale imaging," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    2. Farshid Ashtiani & Alexander J. Geers & Firooz Aflatouni, 2022. "An on-chip photonic deep neural network for image classification," Nature, Nature, vol. 606(7914), pages 501-506, June.
    3. 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.
    4. Can Li & Lili Han & Hao Jiang & Moon-Hyung Jang & Peng Lin & Qing Wu & Mark Barnell & J. Joshua Yang & Huolin L. Xin & Qiangfei Xia, 2017. "Three-dimensional crossbar arrays of self-rectifying Si/SiO2/Si memristors," Nature Communications, Nature, vol. 8(1), pages 1-9, August.
    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. Wen Sun & Bin Gao & Miaofang Chi & Qiangfei Xia & J. Joshua Yang & He Qian & Huaqiang Wu, 2019. "Understanding memristive switching via in situ characterization and device modeling," Nature Communications, Nature, vol. 10(1), pages 1-13, 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. 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.
    2. Konlechner, Roland & Allagui, Anis & Antonov, Vladimir N. & Yudin, Dmitry, 2023. "A superstatistics approach to the modelling of memristor current–voltage responses," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 614(C).
    3. Koryazhkina, M.N. & Filatov, D.O. & Shishmakova, V.A. & Shenina, M.E. & Belov, A.I. & Antonov, I.N. & Kotomina, V.E. & Mikhaylov, A.N. & Gorshkov, O.N. & Agudov, N.V. & Guarcello, C. & Carollo, A. & S, 2022. "Resistive state relaxation time in ZrO2(Y)-based memristive devices under the influence of external noise," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    4. Mahata, Chandreswar & Kim, Sungjun, 2021. "Electrical and optical artificial synapses properties of TiN-nanoparticles incorporated HfAlO-alloy based memristor," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    5. 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.
    6. Feng, Liang & Hu, Cheng & Yu, Juan & Jiang, Haijun & Wen, Shiping, 2021. "Fixed-time Synchronization of Coupled Memristive Complex-valued Neural Networks," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    7. Hu, Yongbing & Li, Qian & Ding, Dawei & Jiang, Li & Yang, Zongli & Zhang, Hongwei & Zhang, Zhixin, 2021. "Multiple coexisting analysis of a fractional-order coupled memristive system and its application in image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    8. Zhang, Ge & Ma, Jun & Alsaedi, Ahmed & Ahmad, Bashir & Alzahrani, Faris, 2018. "Dynamical behavior and application in Josephson Junction coupled by memristor," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 290-299.
    9. 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.
    10. Qin, Xiaoli & Wang, Cong & Li, Lixiang & Peng, Haipeng & Yang, Yixian & Ye, Lu, 2018. "Finite-time modified projective synchronization of memristor-based neural network with multi-links and leakage delay," Chaos, Solitons & Fractals, Elsevier, vol. 116(C), pages 302-315.
    11. Liu, Yunfeng & Song, Zhiqiang & Tan, Manchun, 2019. "Multiple μ-stability and multiperiodicity of delayed memristor-based fuzzy cellular neural networks with nonmonotonic activation functions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 159(C), pages 1-17.
    12. Kwon, Osung & Kim, Sungjun & Agudov, Nikolay & Krichigin, Alexey & Mikhaylov, Alexey & Grimaudo, Roberto & Valenti, Davide & Spagnolo, Bernardo, 2022. "Non-volatile memory characteristics of a Ti/HfO2/Pt synaptic device with a crossbar array structure," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    13. Deng, Yue & Li, Yuxia, 2021. "Bifurcation and bursting oscillations in 2D non-autonomous discrete memristor-based hyperchaotic map," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    14. Ma, Xujiong & Mou, Jun & Xiong, Li & Banerjee, Santo & Cao, Yinghong & Wang, Jieyang, 2021. "A novel chaotic circuit with coexistence of multiple attractors and state transition based on two memristors," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    15. Aguilera-Morillo, M. Carmen & Aguilera, Ana M. & Jiménez-Molinos, Francisco & Roldán, Juan B., 2019. "Stochastic modeling of Random Access Memories reset transitions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 159(C), pages 197-209.
    16. Jahanshahi, Hadi & Yousefpour, Amin & Munoz-Pacheco, Jesus M. & Kacar, Sezgin & Pham, Viet-Thanh & Alsaadi, Fawaz E., 2020. "A new fractional-order hyperchaotic memristor oscillator: Dynamic analysis, robust adaptive synchronization, and its application to voice encryption," Applied Mathematics and Computation, Elsevier, vol. 383(C).
    17. Filatov, D.O. & Koryazhkina, M.N. & Novikov, A.S. & Shishmakova, V.A. & Shenina, M.E. & Antonov, I.N. & Gorshkov, O.N. & Agudov, N.V. & Carollo, A. & Valenti, D. & Spagnolo, B., 2022. "Effect of internal noise on the relaxation time of an yttria stabilized zirconia-based memristor," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    18. Dong, Yujiao & Yang, Shuting & Liang, Yan & Wang, Guangyi, 2022. "Neuromorphic dynamics near the edge of chaos in memristive neurons," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    19. Korneev, I.A. & Semenov, V.V. & Slepnev, A.V. & Vadivasova, T.E., 2021. "Complete synchronization of chaos in systems with nonlinear inertial coupling," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    20. 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.

    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:15:y:2024:i:1:d:10.1038_s41467-024-44773-7. 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.