Mode splitting in optical microcavities for speckle-free wavelength reconstruction

Ivan Saetchnikov; Elina Tcherniavskaia; Andreas Ostendorf; Anton Saetchnikov

doi:10.1038/s41377-025-02073-9

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Mode splitting in optical microcavities for speckle-free wavelength reconstruction

Original Articles

- Mode splitting in optical microcavities for speckle-free wavelength reconstruction
- Light: Science & Applications Vol. 15, Issue 1, Pages: 176-184(2026)
- Affiliations：
  
  1.Radio Physics Department, Belarusian State University, Minsk, Belarus
  2.Physics Department, Belarusian State University, Minsk, Belarus
  3.Chair of Applied Laser Technologies, Ruhr University Bochum, Bochum, Germany
- Author bio：
  
  Anton Saetchnikov (anton.saetchnikov@rub.de)
- Funds：
- DOI：10.1038/s41377-025-02073-9
  CLC：
- Received：09 April 2025，
  
  Revised：2025-08-31，
  
  Accepted：24 September 2025，
  
  Published Online：01 January 2026，
  
  Published：31 January 2026
- Accepted：
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Saetchnikov, I. et al. Mode splitting in optical microcavities for speckle-free wavelength reconstruction. Light: Science & Applications, 15, 176-184 (2026).
DOI：

Saetchnikov, I. et al. Mode splitting in optical microcavities for speckle-free wavelength reconstruction. Light: Science & Applications, 15, 176-184 (2026). DOI： 10.1038/s41377-025-02073-9.

摘要

Abstract

Accurate wavelength measurement is critical for spectroscopy

optical communications

semiconductor manufacturing

and quantum research. Emerging reconstructive wavemeters are compact

cost-effective devices that utilize pseudo-random wavelength patterns and computational techniques to provide high-resolution

broadband alternatives to solutions based on frequency beating and interferometry. We propose a novel reconstructive wavemeter that synergizes the advantages of both approaches. Its physical model is based on the integration of thousands of high-quality-factor optical microcavities

which are deformed to stimulate whispering gallery mode splitting. For realizing a wavelength interpreter

we developed a hybrid machine learning approach utilizing boosting methods and variational autoencoders. This enabled the implementation of wavelength interpretation as a rigorous regression task for the first time. The introduced novel concept ensures the uniqueness of the wavelength patterns up to ultra-wide (~100 nm) spectral window while guarantees high (~100 fm) intrinsic sensitivity. The latter allocates the proposed solution right next to the ultimate reconstructive wavemeters based on integrating spheres

but with less calibration efforts

featuring superior miniaturization options and chip-scale integrability.

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references

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