IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-33132-z.html
   My bibliography  Save this article

Enabling scalable optical computing in synthetic frequency dimension using integrated cavity acousto-optics

Author

Listed:
  • Han Zhao

    (University of Washington)

  • Bingzhao Li

    (University of Washington)

  • Huan Li

    (University of Washington)

  • Mo Li

    (University of Washington
    University of Washington)

Abstract

Optical computing with integrated photonics brings a pivotal paradigm shift to data-intensive computing technologies. However, the scaling of on-chip photonic architectures using spatially distributed schemes faces the challenge imposed by the fundamental limit of integration density. Synthetic dimensions of light offer the opportunity to extend the length of operand vectors within a single photonic component. Here, we show that large-scale, complex-valued matrix-vector multiplications on synthetic frequency lattices can be performed using an ultra-efficient, silicon-based nanophotonic cavity acousto-optic modulator. By harnessing the resonantly enhanced strong electro-optomechanical coupling, we achieve, in a single such modulator, the full-range phase-coherent frequency conversions across the entire synthetic lattice, which constitute a fully connected linear computing layer. Our demonstrations open up the route toward the experimental realizations of frequency-domain integrated optical computing systems simultaneously featuring very large-scale data processing and small device footprints.

Suggested Citation

  • Han Zhao & Bingzhao Li & Huan Li & Mo Li, 2022. "Enabling scalable optical computing in synthetic frequency dimension using integrated cavity acousto-optics," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33132-z
    DOI: 10.1038/s41467-022-33132-z
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-33132-z
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-022-33132-z?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. Siddharth Buddhiraju & Avik Dutt & Momchil Minkov & Ian A. D. Williamson & Shanhui Fan, 2021. "Arbitrary linear transformations for photons in the frequency synthetic dimension," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    2. J. Feldmann & N. Youngblood & M. Karpov & H. Gehring & X. Li & M. Stappers & M. Gallo & X. Fu & A. Lukashchuk & A. S. Raja & J. Liu & C. D. Wright & A. Sebastian & T. J. Kippenberg & W. H. P. Pernice , 2021. "Publisher Correction: Parallel convolutional processing using an integrated photonic tensor core," Nature, Nature, vol. 591(7849), pages 13-13, March.
    3. Michael Kues & Christian Reimer & Piotr Roztocki & Luis Romero Cortés & Stefania Sciara & Benjamin Wetzel & Yanbing Zhang & Alfonso Cino & Sai T. Chu & Brent E. Little & David J. Moss & Lucia Caspani , 2017. "On-chip generation of high-dimensional entangled quantum states and their coherent control," Nature, Nature, vol. 546(7660), pages 622-626, June.
    4. Wim Bogaerts & Daniel Pérez & José Capmany & David A. B. Miller & Joyce Poon & Dirk Englund & Francesco Morichetti & Andrea Melloni, 2020. "Programmable photonic circuits," Nature, Nature, vol. 586(7828), pages 207-216, October.
    5. H. Zhang & M. Gu & X. D. Jiang & J. Thompson & H. Cai & S. Paesani & R. Santagati & A. Laing & Y. Zhang & M. H. Yung & Y. Z. Shi & F. K. Muhammad & G. Q. Lo & X. S. Luo & B. Dong & D. L. Kwong & L. C., 2021. "An optical neural chip for implementing complex-valued neural network," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    6. Xingyuan Xu & Mengxi Tan & Bill Corcoran & Jiayang Wu & Andreas Boes & Thach G. Nguyen & Sai T. Chu & Brent E. Little & Damien G. Hicks & Roberto Morandotti & Arnan Mitchell & David J. Moss, 2021. "11 TOPS photonic convolutional accelerator for optical neural networks," Nature, Nature, vol. 589(7840), pages 44-51, January.
    7. Hao Tian & Junqiu Liu & Bin Dong & J. Connor Skehan & Michael Zervas & Tobias J. Kippenberg & Sunil A. Bhave, 2020. "Hybrid integrated photonics using bulk acoustic resonators," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    8. J. Feldmann & N. Youngblood & M. Karpov & H. Gehring & X. Li & M. Stappers & M. Gallo & X. Fu & A. Lukashchuk & A. S. Raja & J. Liu & C. D. Wright & A. Sebastian & T. J. Kippenberg & W. H. P. Pernice , 2021. "Parallel convolutional processing using an integrated photonic tensor core," Nature, Nature, vol. 589(7840), pages 52-58, January.
    9. Mohammad Mirhosseini & Alp Sipahigil & Mahmoud Kalaee & Oskar Painter, 2020. "Superconducting qubit to optical photon transduction," Nature, Nature, vol. 588(7839), pages 599-603, December.
    10. Cheng Wang & Mian Zhang & Xi Chen & Maxime Bertrand & Amirhassan Shams-Ansari & Sethumadhavan Chandrasekhar & Peter Winzer & Marko Lončar, 2018. "Integrated lithium niobate electro-optic modulators operating at CMOS-compatible voltages," Nature, Nature, vol. 562(7725), pages 101-104, October.
    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. I-Tung Chen & Bingzhao Li & Seokhyeong Lee & Srivatsa Chakravarthi & Kai-Mei Fu & Mo Li, 2023. "Optomechanical ring resonator for efficient microwave-optical frequency conversion," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    2. Seong Won Lee & Jong Seok Lee & Woo Hun Choi & Daegwang Choi & Su-Hyun Gong, 2024. "Ultra-compact exciton polariton modulator based on van der Waals semiconductors," Nature Communications, Nature, vol. 15(1), pages 1-7, 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. H. H. Zhu & J. Zou & H. Zhang & Y. Z. Shi & S. B. Luo & N. Wang & H. Cai & L. X. Wan & B. Wang & X. D. Jiang & J. Thompson & X. S. Luo & X. H. Zhou & L. M. Xiao & W. Huang & L. Patrick & M. Gu & L. C., 2022. "Space-efficient optical computing with an integrated chip diffractive neural network," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    2. 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.
    3. 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.
    4. 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.
    5. Bowen Bai & Qipeng Yang & Haowen Shu & Lin Chang & Fenghe Yang & Bitao Shen & Zihan Tao & Jing Wang & Shaofu Xu & Weiqiang Xie & Weiwen Zou & Weiwei Hu & John E. Bowers & Xingjun Wang, 2023. "Microcomb-based integrated photonic processing unit," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    6. Xuan-Kun Li & Jian-Xu Ma & Xiang-Yu Li & Jun-Jie Hu & Chuan-Yang Ding & Feng-Kai Han & Xiao-Min Guo & Xi Tan & Xian-Min Jin, 2024. "High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    7. 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.
    8. Jingwei Ling & Zhengdong Gao & Shixin Xue & Qili Hu & Mingxiao Li & Kaibo Zhang & Usman A. Javid & Raymond Lopez-Rios & Jeremy Staffa & Qiang Lin, 2024. "Electrically empowered microcomb laser," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    9. Miltiadis Moralis-Pegios & George Giamougiannis & Apostolos Tsakyridis & David Lazovsky & Nikos Pleros, 2024. "Perfect linear optics using silicon photonics," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    10. Guangwei Cong & Noritsugu Yamamoto & Takashi Inoue & Yuriko Maegami & Morifumi Ohno & Shota Kita & Shu Namiki & Koji Yamada, 2022. "On-chip bacterial foraging training in silicon photonic circuits for projection-enabled nonlinear classification," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    11. Shaofu Xu & Jing Wang & Sicheng Yi & Weiwen Zou, 2022. "High-order tensor flow processing using integrated photonic circuits," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    12. G. Mourgias-Alexandris & M. Moralis-Pegios & A. Tsakyridis & S. Simos & G. Dabos & A. Totovic & N. Passalis & M. Kirtas & T. Rutirawut & F. Y. Gardes & A. Tefas & N. Pleros, 2022. "Noise-resilient and high-speed deep learning with coherent silicon photonics," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    13. Ali Najjar Amiri & Aycan Deniz Vit & Kazim Gorgulu & Emir Salih Magden, 2024. "Deep photonic network platform enabling arbitrary and broadband optical functionality," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    14. Chen-Guang Wang & Wuyue Xu & Chong Li & Lili Shi & Junliang Jiang & Tingting Guo & Wen-Cheng Yue & Tianyu Li & Ping Zhang & Yang-Yang Lyu & Jiazheng Pan & Xiuhao Deng & Ying Dong & Xuecou Tu & Sining , 2024. "Integrated and DC-powered superconducting microcomb," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    15. Wenting Wang & Ping-Keng Lu & Abhinav Kumar Vinod & Deniz Turan & James F. McMillan & Hao Liu & Mingbin Yu & Dim-Lee Kwong & Mona Jarrahi & Chee Wei Wong, 2022. "Coherent terahertz radiation with 2.8-octave tunability through chip-scale photomixed microresonator optical parametric oscillation," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    16. Bitao Shen & Haowen Shu & Weiqiang Xie & Ruixuan Chen & Zhi Liu & Zhangfeng Ge & Xuguang Zhang & Yimeng Wang & Yunhao Zhang & Buwen Cheng & Shaohua Yu & Lin Chang & Xingjun Wang, 2023. "Harnessing microcomb-based parallel chaos for random number generation and optical decision making," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    17. Xuguang Zhang & Zixuan Zhou & Yijun Guo & Minxue Zhuang & Warren Jin & Bitao Shen & Yujun Chen & Jiahui Huang & Zihan Tao & Ming Jin & Ruixuan Chen & Zhangfeng Ge & Zhou Fang & Ning Zhang & Yadong Liu, 2024. "High-coherence parallelization in integrated photonics," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    18. Yiwei Li & Ning An & Zheyi Lu & Yuchen Wang & Bing Chang & Teng Tan & Xuhan Guo & Xizhen Xu & Jun He & Handing Xia & Zhaohui Wu & Yikai Su & Yuan Liu & Yunjiang Rao & Giancarlo Soavi & Baicheng Yao, 2022. "Nonlinear co-generation of graphene plasmons for optoelectronic logic operations," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    19. Mitsumasa Nakajima & Katsuma Inoue & Kenji Tanaka & Yasuo Kuniyoshi & Toshikazu Hashimoto & Kohei Nakajima, 2022. "Physical deep learning with biologically inspired training method: gradient-free approach for physical hardware," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    20. Ming Deng & Michele Cotrufo & Jian Wang & Jianji Dong & Zhichao Ruan & Andrea Alù & Lin Chen, 2024. "Broadband angular spectrum differentiation using dielectric metasurfaces," Nature Communications, Nature, vol. 15(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:13:y:2022:i:1:d:10.1038_s41467-022-33132-z. 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.