Experimental study demonstrates machine learning optimization of an ultrafast fiber amplifier across L- and U-bands, enabling smart laser development with adaptive spectral shaping.
We demonstrate amplification of ultrashort pulses in the L (1565-1625 nm) and U (1625-1675 nm) bands using a highly nonlinear erbium-ytterbium co-doped, small-core (3.8 μm) double-clad fiber amplifier optimized with machine learning. The fiber operates in the normal-dispersion regime, enabling strong nonlinear broadening and a characteristic spectral redshift. A programmable optical filter controlled the seed pulse's spectral phase and shape, while a machine-learning algorithm iteratively optimized the input conditions. This work presents the first experimental demonstration of a machine-learning-optimized, gain-managed nonlinear amplifier designed to maximize emission within a user-defined spectral range while maintaining near-transform-limited compressibility. The results pave the way for the development of smart lasers with adaptive phase and spectral shaping across previously inaccessible spectral ranges.
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Krakowski et al. (2026) studied this question.
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