IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-025-59794-z.html
   My bibliography  Save this article

Large-scale photonic chip based pulse interleaver for low-noise microwave generation

Author

Listed:
  • Zheru Qiu

    (Swiss Federal Institute of Technology Lausanne (EPFL)
    Center for Quantum Science and Engineering, EPFL)

  • Neetesh Singh

    (Deutsches Elektronen-Synchrotron)

  • Yang Liu

    (Swiss Federal Institute of Technology Lausanne (EPFL)
    Center for Quantum Science and Engineering, EPFL)

  • Xinru Ji

    (Swiss Federal Institute of Technology Lausanne (EPFL)
    Center for Quantum Science and Engineering, EPFL)

  • Rui Ning Wang

    (Swiss Federal Institute of Technology Lausanne (EPFL)
    Center for Quantum Science and Engineering, EPFL
    Luxtelligence SA)

  • Franz X. Kärtner

    (Deutsches Elektronen-Synchrotron
    Universität Hamburg)

  • Tobias Kippenberg

    (Swiss Federal Institute of Technology Lausanne (EPFL)
    Center for Quantum Science and Engineering, EPFL)

Abstract

Optically generated microwaves exhibit unprecedented low noise, benefiting applications such as communications, radar, instrumentation, and metrology. To date, the purest microwave signals are produced using optical frequency division with femtosecond mode-locked lasers. However, their typical repetition rates of hundreds of MHz require multiplication methods to reach the microwave domain. Here, we introduce a miniaturized photonic integrated circuit-based interleaver, achieving a 64-fold multiplication of the repetition rate from 216 MHz to 14 GHz in Ku-Band. With the interleaver, the generated microwave power was improved by 35 dB, with a phase noise floor reduced by more than 10 folds by alleviating photodetector saturation. Based on a low-loss and high-density Si3N4 waveguides, six cascaded stages of Mach-Zehnder interferometers with optical delay lines up to 33 centimeters long are fully integrated into a compact chip. Our result can significantly reduce the cost and footprint of mode-locked-laser-based microwave generation, enabling field deployment in aerospace and communication applications.

Suggested Citation

  • Zheru Qiu & Neetesh Singh & Yang Liu & Xinru Ji & Rui Ning Wang & Franz X. Kärtner & Tobias Kippenberg, 2025. "Large-scale photonic chip based pulse interleaver for low-noise microwave generation," Nature Communications, Nature, vol. 16(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59794-z
    DOI: 10.1038/s41467-025-59794-z
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-025-59794-z
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-025-59794-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. Junqiu Liu & Guanhao Huang & Rui Ning Wang & Jijun He & Arslan S. Raja & Tianyi Liu & Nils J. Engelsen & Tobias J. Kippenberg, 2021. "High-yield, wafer-scale fabrication of ultralow-loss, dispersion-engineered silicon nitride photonic circuits," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    2. Fuchang Gao & Lixing Han, 2012. "Implementing the Nelder-Mead simplex algorithm with adaptive parameters," Computational Optimization and Applications, Springer, vol. 51(1), pages 259-277, January.
    3. Minji Hyun & Changmin Ahn & Yongjin Na & Hayun Chung & Jungwon Kim, 2020. "Attosecond electronic timing with rising edges of photocurrent pulses," 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. Hsuan-Hao Lu & Karthik V. Myilswamy & Ryan S. Bennink & Suparna Seshadri & Mohammed S. Alshaykh & Junqiu Liu & Tobias J. Kippenberg & Daniel E. Leaird & Andrew M. Weiner & Joseph M. Lukens, 2022. "Bayesian tomography of high-dimensional on-chip biphoton frequency combs with randomized measurements," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. Riva-Palacio, Alan & Leisen, Fabrizio, 2021. "Compound vectors of subordinators and their associated positive Lévy copulas," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    3. Jaromír Antoch & Michal Černý & Ryozo Miura, 2025. "R-estimation in linear models: algorithms, complexity, challenges," Computational Statistics, Springer, vol. 40(1), pages 405-439, January.
    4. Ivan Jericevich & Patrick Chang & Tim Gebbie, 2021. "Simulation and estimation of an agent-based market-model with a matching engine," Papers 2108.07806, arXiv.org, revised Aug 2021.
    5. Grigory Lihachev & Johann Riemensberger & Wenle Weng & Junqiu Liu & Hao Tian & Anat Siddharth & Viacheslav Snigirev & Vladimir Shadymov & Andrey Voloshin & Rui Ning Wang & Jijun He & Sunil A. Bhave & , 2022. "Low-noise frequency-agile photonic integrated lasers for coherent ranging," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    6. Rocha Filho, T.M. & Moret, M.A. & Chow, C.C. & Phillips, J.C. & Cordeiro, A.J.A. & Scorza, F.A. & Almeida, A.-C.G. & Mendes, J.F.F., 2021. "A data-driven model for COVID-19 pandemic – Evolution of the attack rate and prognosis for Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    7. Kota Matsui & Wataru Kumagai & Takafumi Kanamori, 2017. "Parallel distributed block coordinate descent methods based on pairwise comparison oracle," Journal of Global Optimization, Springer, vol. 69(1), pages 1-21, September.
    8. Chang, Kuo-Hao, 2015. "A direct search method for unconstrained quantile-based simulation optimization," European Journal of Operational Research, Elsevier, vol. 246(2), pages 487-495.
    9. Ronan Keane & H. Oliver Gao, 2021. "Fast Calibration of Car-Following Models to Trajectory Data Using the Adjoint Method," Transportation Science, INFORMS, vol. 55(3), pages 592-615, May.
    10. Michele Mininni & Giuseppe Orlando & Giovanni Taglialatela, 2021. "Challenges in approximating the Black and Scholes call formula with hyperbolic tangents," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 73-100, June.
    11. Ralf Biehl, 2019. "Jscatter, a program for evaluation and analysis of experimental data," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-18, June.
    12. Mejía-de-Dios, Jesús-Adolfo & Mezura-Montes, Efrén & Toledo-Hernández, Porfirio, 2022. "Pseudo-feasible solutions in evolutionary bilevel optimization: Test problems and performance assessment," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    13. Papo, David & Righetti, Marco & Fadiga, Luciano & Biscarini, Fabio & Zanin, Massimiliano, 2020. "A minimal model of hospital patients’ dynamics in COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    14. Pinto, Roberto, 2016. "Stock rationing under a profit satisficing objective," Omega, Elsevier, vol. 65(C), pages 55-68.
    15. Ingrida Steponavičė & Rob J. Hyndman & Kate Smith-Miles & Laura Villanova, 2017. "Dynamic algorithm selection for pareto optimal set approximation," Journal of Global Optimization, Springer, vol. 67(1), pages 263-282, January.
    16. Ferreiro, Ana M. & García-Rodríguez, José Antonio & Vázquez, Carlos & e Silva, E. Costa & Correia, A., 2019. "Parallel two-phase methods for global optimization on GPU," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 156(C), pages 67-90.
    17. Minji Hyun & Hayun Chung & Woongdae Na & Jungwon Kim, 2023. "Femtosecond-precision electronic clock distribution in CMOS chips by injecting frequency comb-extracted photocurrent pulses," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    18. He, Jie-Cao & Hsieh, Chang-Chieh & Huang, Zi-Wei & Lin, Shih-Kuei, 2023. "Valuation of callable range accrual linked to CMS Spread under generalized swap market model," International Review of Financial Analysis, Elsevier, vol. 90(C).
    19. Carvajal-Rodríguez, A., 2020. "Multi-model inference of non-random mating from an information theoretic approach," Theoretical Population Biology, Elsevier, vol. 131(C), pages 38-53.
    20. 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.

    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:16:y:2025:i:1:d:10.1038_s41467-025-59794-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.