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Register a new userSlow Light Nanocoatings for Ultrashort Pulse Shaping
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Authors
M. Ossiander
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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.
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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 description solving Maxwell equations for light electromagnetic fields and the time-dependent Kohn-Sham equation for electron dynamics simultaneously in the time domain using a common spatial grid. We call it the microscopic Maxwell-TDDFT scheme. We test this scheme for silicon thin films of various thickness, from a few atomic layers to a few tens of nm. We show that the microscopic Maxwell-TDDFT scheme provides a satisfactory description incorporating the electronic structure of thin films in the first-principles level, multiple reflections of the electromagnetic fields at the surfaces, and nonlinear light-matter interaction when the incident light pulse is strong. However, the calculation becomes expensive as the thickness increases. We then consider two limiting cases of extremely thin and sufficiently thick films and develop approximate schemes. For the extremely thin case including two-dimensional atomic-layered materials, a two-dimensional macroscopic electromagnetism is developed: a two-dimensional susceptibility is introduced for a weak field, while time evolution equation is derived for an intense field. For sufficiently thick films, the microscopic Maxwell-TDDFT scheme is expected to coincide with a description utilizing ordinary macroscopic electromagnetism. We numerically confirm it comparing the calculated results: For a weak field, a comparison is made with a description using the bulk dielectric susceptibility. For a strong field, a comparison is made with a multiscale Maxwell-TDDFT scheme which the authors group developed previously.
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 elements to all-optically perform various tasks such as object classification and machine vision. Here, we present a diffractive network, which is used to shape an arbitrary broadband pulse into a desired optical waveform, forming a compact pulse engineering system. We experimentally demonstrate the synthesis of square pulses with different temporal-widths by manufacturing passive diffractive layers that collectively control both the spectral amplitude and the phase of an input terahertz pulse. Our results constitute the first demonstration of direct pulse shaping in terahertz spectrum, where a complex-valued spectral modulation function directly acts on terahertz frequencies. Furthermore, a Lego-like physical transfer learning approach is presented to illustrate pulse-width tunability by replacing part of an existing network with newly trained diffractive layers, demonstrating its modularity. This learning-based diffractive pulse engineering framework can find broad applications in e.g., communications, ultra-fast imaging and spectroscopy.
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 (hyperbolic secant like), dispersion-managed solitons (Gaussian-like), and parabolic pulses plus external compression, ultrashort pulse solutions to the nonlinear wave equations that describe pulse evolution in the laser cavity are observed. Stable ultrashort, transform-limited pulses exist with optical spectrum broader than the gain bandwidth of the amplifier, and this has practical application for other lasers.
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 trains. An ideal step phase profile as well as a profile arisen from a bandwidth-limited device are investigated. Analytical guidelines describing pulse trains formation and their characteristics are provided.
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. We also show that the model is able to retrieve the parameters of the nonlinear propagation from the pulses observed at the output of the fiber. Various initial pulse shapes as well as initially chirped pulses are investigated.
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