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Transparent materials do not absorb light but have profound influence on the phase evolution of transmitted radiation. One consequence is chromatic dispersion, i.e., light of different frequencies travels at different velocities, causing ultrashort laser pulses to elongate in time while propagating. Here we experimentally demonstrate ultrathin nanostructured coatings that resolve this challenge: we tailor the dispersion of silicon nanopillar arrays such that they temporally reshape pulses upon transmission using slow light effects and act as ultrashort laser pulse compressors. The coatings induce anomalous group delay dispersion in the visible to near-infrared spectral region around 800 nm wavelength over an 80 nm bandwidth. We characterize the arrays performance in the spectral domain via white light interferometry and directly demonstrate the temporal compression of femtosecond laser pulses. Applying these coatings to conventional optics renders them ultrashort pulse compatible and suitable for a wide range of applications.
We present a comprehensive theoretical description for an irradiation of an ultrashort light pulse normally on thin materials based on first-principles time-dependent density functional theory. As the most elaborate scheme, we develop a microscopic d
Recent advances in deep learning have been providing non-intuitive solutions to various inverse problems in optics. At the intersection of machine learning and optics, diffractive networks merge wave-optics with deep learning to design task-specific
Direct generation of ultrashort, transform-limited pulses in a laser resonator is observed theoretically and experimentally. This constitutes a new type of ultrashort pulse generation in mode-locked lasers: in contrast to the well-known solitons (hyp
We propose and numerically validate an all-optical scheme to generate a train of optical pulses. Modulation of a continuous wave with a periodic binary temporal phase pattern followed by a spectral phase shaping enables us to obtain ultrashort pulse
We use a supervised machine-learning model based on a neural network to predict the temporal and spectral intensity profiles of the pulses that form upon nonlinear propagation in optical fibers with both normal and anomalous second-order dispersion.