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On-chip real-time detection of optical frequency variations with ultrahigh resolution using the sine-cosine encoder approach

Author

Listed:
  • X. Steve Yao

    (Hebei University
    Inc)

  • Yulong Yang

    (Hebei University)

  • Xiaosong Ma

    (Hebei University)

  • Zhongjin Lin

    (Sun Yat-sen University)

  • Yuntao Zhu

    (Sun Yat-sen University)

  • Wei Ke

    (Liobate Technologies)

  • Heyun Tan

    (Sun Yat-sen University)

  • Xichen Wang

    (NeoPIC Technologies)

  • Xinlun Cai

    (Sun Yat-sen University)

Abstract

Real-time measurement of optical frequency variations (OFVs) is crucial for various applications including laser frequency control, optical computing, and optical sensing. Traditional devices, though accurate, are often too large, slow, and costly. Here we present a photonic integrated circuit (PIC) chip, utilizing the sine-cosine encoder principle, for high-speed and high-resolution real-time OFV measurement. Fabricated on a thin film lithium niobate (TFLN) platform, this chip-sized optical frequency detector (OFD) (5.5 mm × 2.7 mm) achieves a speed of up to 2500 THz/s and a resolution as fine as 2 MHz over a range exceeding 160 nm. Our robust algorithm overcomes the device imperfections and ensures precise quantification of OFV parameters. As a practical demonstration, the PIC OFD surpasses existing fiber Bragg grating (FBG) interrogators in sensitivity and speed for strain and vibration measurements. This work opens new avenues for on-chip OFV detection and offers significant potential for diverse applications involving OFV measurement.

Suggested Citation

  • X. Steve Yao & Yulong Yang & Xiaosong Ma & Zhongjin Lin & Yuntao Zhu & Wei Ke & Heyun Tan & Xichen Wang & Xinlun Cai, 2025. "On-chip real-time detection of optical frequency variations with ultrahigh resolution using the sine-cosine encoder approach," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58251-1
    DOI: 10.1038/s41467-025-58251-1
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    References listed on IDEAS

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    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.
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