MANUFACTURING with short pulse lasers FOCUS under industrial conditions. In this context, the integration of artificial intelligence and machine learning represents a transformative development. Data-driven predictive modeling can link process parameters to resulting nanostructures and their functionalities, enabling accelerated optimization, adaptive process control, and the identification of previously unexplored parameter regimes. At the same time, such predictive approaches can save energy and material resources, further developing laser processing towards a green laser technology. Overall, scaling of LIN is less about overcoming a single physical limit than about intelligently combining self-organization, optical parallelization, fast beam delivery, and advanced laser sources.
CONCLUSION This review presented a brief summary of important milestones in the field of LIN of solids with surface- or volume-related features sizes below the diffraction limit. Features smaller than one-tenth of the laser wavelength can be realized. This is made possible by the minimal HAZ achieved by the ultrashort pulse durations and high intensities, which effectively drive nonlinear excitation processes and enable coherently excited collective near-field optical effects. Such tiny structures then require advanced characterization techniques as they are provided through XFELs, to investigate even in-situ the formation mechanisms at extreme scales in space( nm to µ m) and time( fs to ms). A special focus was on LIPSS that are often manifesting in laser processing. Recent developments and trends in
REFERENCES their generation, characterisation, and scaling were outlined, and the potential for further promoting their use in industrial applications was highlighted.
ACKNOWLEDGEMENTS This work was funded by the Deutsche Forschungsgemeinschaft( DFG, German Research Foundation) through Project IDs 530345255 and 278162697-SFB 1242.
[ 1 ] R. Stoian and J. Bonse( eds.), Ultrafast Laser Nanostructuring— The Pursuit of Extreme Scales( Springer, Cham, 2023)
[ 2 ] J. Bonse and A. F. Lasagni( eds.), Scaling of Laser Processing— Making Light Matter( Springer, Cham, 2026)
[ 3 ] A. P. Joglekar, H. Liu, E. Meyhöfer et al. Proc. Natl. Acad. Sci. U. S. A. 101, 5856( 2004) [ 4 ] V. V. Belloni, M. Hassan, L. Furfaro et al. Laser Photon. Rev. 18, 2300687( 2024) [ 5 ] S. Juodkazis, K. Nishimura, S. Tanaka et al. Phys. Rev. Lett. 96, 166101( 2006) [ 6 ] J. Cao, M. Lancry, F. Brisset et al. Crystal Growth & Design 19, 2189( 2019) [ 7 ] J. Bonse and S. Gräf, Laser Photon. Rev. 14, 2000215( 2020) [ 8 ] Z. Liu, J. Siegel, M. Garcia-Lechuga et al. ACS Nano 11, 5031( 2017) [ 9 ] N. Sharma, M. Vangheluwe, F. Vocanson et al. Mater. Horiz. 6, 978( 2019) [ 10 ] J. Bonse and K. Sokolowski-Tinten, Laser Photon. Rev. 18, 2300912( 2024) [ 11 ] Y. Sun, C. Chen, T. J. Albert et al., Commun. Mater. 6, 69( 2025) [ 12 ] E. Rahner, T. Thiele, H. Voss et al. Appl. Surf. Sci. 726, 165919( 2026)
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