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The fabrication of three-dimensional (3D) nanostructures within optical materials is currently a highly sought-after capability. Achieving nanoscale structuring of media within its inner volume in 3D and with free design flexibility, high accuracy and precision is a development yet to be demonstrated. In this work, a 3D laser nanolithography technique is developed which allows producing mm-long hollow nanopores inside solid-state laser crystals and with a high degree of control of pore cross-sectional aspect ratio and size. We report an in-depth study on the formation of pores both within the non-thermal regime at which temperature is fast dissipated after each laser pulse, and for a thermally controlled regime using pulse-bursts which facilitate the formation of pores with highly circular shapes down to 1.1. We demonstrate this process for a wide range of speeds, pulse repetition rates and pulse energies, thus opening the door to a much more useful nanofabrication technique for nanophotonics. Finally, we also report the change in index of refraction that is produced at the nanoscale obtaining a positive index contrast of ∼3%. The work therefore provides a promising path towards reliable 3D nanostructuring of solid-state laser media for the flexible fabrication of large and complex structures with features sizes from the nanoscale up to the mm-scale. Moreover, due to the embedded, seamless, and monolithic nature of this technology, and since YAG crystals can sustain temperatures of up to 1900 °C and are highly chemically inert and erosion resistant, we anticipate its direct application in harsh environments.

Featured Application: Piston alignment of segmented optical mirror telescopes through an algorithm that learns by itself how to maximize a physical quantity of the system. Unlike supervised machine learning methods, reinforcement learning allows an entity to learn how to deploy a task from experience rather than labeled data. This approach has been used in this paper to correct piston misalignment between segments in a segmented mirror telescope. It was proven in simulations that the algorithm converges to a point where it learns how to move the piston actuators in order to maximize the Strehl ratio of the wavefront at the intersection. [ABSTRACT FROM AUTHOR]

The performance of the "weighted Fourier phase slope" centroiding algorithm at the subpupil image of a Shack--Hartmann wavefront sensor for point-like astronomical guiding sources is explored. This algorithm estimates the image's displacement in the Fourier domain by directly computing the phase slope at several spatial frequencies, without the intermediate step of computing the phase; it then applies optimized weights to the phase slopes at each spatial frequency obtained by a Bayesian estimation method. The idea was inspired by cepstrum deconvolution techniques, and this relationship is illustrated. The algorithm's tilt estimation performance is characterized and contrasted with other known centroiding algorithms, such as thresholded centre of gravity (TCoG) and cross correlation (CC), first through numerical simulations at the subpupil level, then at the pupil level, and finally at the laboratory test bench. Results show a similar sensitivity to that of the CC algorithm, which is superior to that of the TCoG algorithm when large fields of view are necessary, i.e., in an open-loop configured adaptive optics system, thereby increasing the guide star limiting magnitude by 0.6 to 0.7 mag. On the other side, its advantage over the CC algorithm is its lower computational cost by approximately an order of magnitude. [ABSTRACT FROM AUTHOR]

We present a geometric sensor to restore the local tip-tilt in a segmented surface using the Van Dam and Lane algorithm [M. A. van Dam and R. G. Lane, Appl. Opt. 41(26), 5497-5502 (2002)]. The paper also presents an implementation of this algorithm using graphical processing units as specialized hardware. This compute unified device architecture implementation achieves real-time results inside the stability time of the atmosphere for resolutions of up to 1024 x 1024 pixels [ABSTRACT FROM AUTHOR]

Most of the methods used today for the alignment of segmented mirrors are based on Shack-Hartman wavefront sensors. Other proposed methods are based on curvature sensors. These can be used to cross-check the measurements given by the primary method. We investigate a different approach which employs convolutional neural networks. This technique allows the piston step values between segments to be measured with high accuracy, as well as a large capture range at visible wavelengths. The technique does not require special hardware, and is fast to be used at any time during the observation.