Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum. The finite-difference time-domain (FDTD) method is used to study the inhomogeneous absorption of linearly polarized laser radiation below a rough surface. The results are first analyzed in the frequency domain and compared to the efficacy factor theory of Sipe and coworkers. Both approaches show that the absorbed energy shows a periodic nature, not only in the direction orthogonal to the laser polarization, but also in the direction parallel to it. It is shown that the periodicity is not always close to the laser wavelength for the perpendicular direction. In the parallel direction, the periodicity is about λ/Re(ñ), withñ being the complex refractive index of the medium. The space-domain FDTD results show a periodicity in the inhomogeneous energy absorption similar to the periodicity of the low-and high-spatial-frequency laser-induced periodic surface structures depending on the material's excitation.
A model predicting the formation of laser-induced periodic surface structures (LIPSSs) is presented. That is, the finite-difference time domain method is used to study the interaction of electromagnetic fields with rough surfaces. In this approach, the rough surface is modified by “ablation after each laser pulse,” according to the absorbed energy profile, in order to account for inter-pulse feedback mechanisms. LIPSSs with a periodicity significantly smaller than the laser wavelength are found to “grow” either parallel or orthogonal to the laser polarization. The change in orientation and periodicity follow from the model. LIPSSs with a periodicity larger than the wavelength of the laser radiation and complex superimposed LIPSS patterns are also predicted by the model.
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