We show that the structural color variations due to a broad range of the illumination incident angles combined with both the controlled orientations of LSFLs and differences in features captured in the image make this system suitable for deep learning-based optical authentication. In this work, by fine-tuning one of the lightweight convolutional neural networks, MobileNetV1, we investigate the optical authentication capabilities of the structurally colorized images on metal surfaces fabricated by controlling the orientation of femtosecond LSFLs. Yet, no applications of deep learning algorithms, known to discover meaningful structures in data with far-reaching optimization capabilities, to such optical authentication applications involving low-spatial-frequency laser-induced periodic surface structures (LSFLs) have been demonstrated to date. Structurally colored materials present potential technological applications including anticounterfeiting tags for authentication due to the ability to controllably manipulate colors through nanostructuring. The validation of this compact characterization unit represents a step forward for its implementation as an in-line monitoring tool for industrial laser-based micropatterning. In addition, focus shifts of 70 µm from the optimum focus position can be detected, and missing patterned lines with a minimum width of 28 µm can be identified. As supported by topographical measurements, the system can accurately calculate spatial periods with a resolution of at least 100 nm. The detection limits of the system were determined by recording the intensities of the zero, first, and second diffraction order using a charge-coupled device (CCD) camera. Namely, fluctuations of the DLIP process parameters such as laser fluence, spatial period, and focus position are introduced, and also, two patterning strategies are implemented, whereby pulses are deliberately not fired at both deterministic and random positions. To this end, a stainless steel plate was structured by direct laser interference patterning (DLIP) following a set of conditions with artificial patterning errors. In this study, a scatterometry-based monitoring system designed for tracking the quality and reproducibility of laser-textured surfaces in industrial environments was validated in off-line and real-time modes. Without the need for specialized expertise and equipment, the method can serve as a simple and widely accessible optical characterization of materials useful in material science and photonics applications. The refractive index of an exemplary soft-moldable material is successfully estimated over a wide wavelength range by simply incorporating the measured topography and diffraction efficiency of the grating into a convenient scalar theory-based diffraction model. Here, we propose a simple diffractive method for the measurement of the refractive index of homogenous solid thin films, which requires only the structuring of the surface of the material to be measured with the profile of a diffraction grating. The measurement of the refractive index typically requires the use of optical ellipsometry which, although potentially very accurate, is extremely sensitive to the structural properties of the sample and its theoretical modeling, and typically requires specialized expertise to obtain reliable output data. Note also that the diffraction efficiency is substantially reduced for all wavelengths other than the blaze wavelength. The seven classical discrete colors: red (λ 1 ¼ 650 nm), orange (λ 2 ¼ 600 nm), yellow (λ 3 ¼ 550 nm), green (λ 4 ¼ 500 nm), blue (λ 5 ¼ 450 nm), indigo (λ 6 ¼ 400 nm), and violet (λ 7 ¼ 350 nm) are obtained by replacing the integral in the above equation by a discrete summation: E Q -T A R G E T t e m p : i n t r a l i n k - e 0 6 3 3 2 6 3 6 2 Figure 30 illustrates that the dispersion is indeed doubled if the grating is blazed for the second diffracted order. polychromatic light, we can represent the resulting diffracted orders with a summation over the discrete diffracted orders of an integral over some spectral band Δλ ¼ λ 2 − λ 1 : E Q -T A R G E T t e m p : i n t r a l i n k - e 0 6 2 3 2 6 5 1 5 Figure 29 schematically illustrates the dispersive behavior over the visible spectrum of a grating blazed for the first order at a wavelength 500 nm.
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