Compact wavemeter incorporating femtosecond laser-induced surface nanostructures enabled by deep learning.

Miniature spectrometers have the advantage of high portability and integration, making them quick and easy to use in various working environments. The speckle patterns produced by light scattering through a disordered medium are highly sensitive to wavelength changes and can be used to design high-precision wavemeters and spectrometers. In this study, we used a self-organized, femtosecond laser-prepared nanostructure with a characteristic size of approximately 30-50 nm on a sapphire surface as a scattering medium to effectively induce spectral dispersion. By leveraging this random scattering structure, we successfully designed a compact scattering wavelength meter with efficient scattering properties. The collected speckle patterns were identified and classified using a neural network, and the variation of speckle patterns with wavelength was accurately extracted, achieving a measurement accuracy of 10 pm in multiple wavelength ranges. The system can effectively suppress instrument and environmental noise with high robustness. This work paves the way for the development of compact high-precision wavemeters.