Estimating the actual parameters of real holographic volume gratings from diffraction efficiency measurements is challenging. The natural formation of the grating provides different phenomena, such as shrinkage, bending of the fringes, or non-homogeneous modulation as a function of the thickness, amongst other issues. This work proposes a deep learning Convolutional Neural Networks (CNNs) and Feedforward Neural Networks (FNNs) hybrid architecture capable of predicting the grating parameters from theoretical and experimental diffraction efficiency patterns. For the training set of this regression problem, Kogelnik’s Coupled Wave Theory simulated data has been employed. Our best model has been trained with an 8000-sized dataset of 80 points of diffraction efficiency patterns simulated from a range of values for the normalized grating wavelengths, index modulations, and optical depths. It achieves test losses under one per cent (mean absolute error) for predicting the normalized grating wavelengths, index modulations and optical depths.
Photopolymers are designed and engineered with versatile applications including optics and photonics. Holography is one of the classical porpoises that use photopolymers as holographic recording materials. The success of these materials can be seen in the market with the photopolymer fabricated by Covestro. Some of these holographic applications require a long-time life of the holograms recorded in photopolymers. Nevertheless, initial tests of Covestro holograms show significant degradation after less than one year of exposure even after sealing and degradation occurs under solar light exposition. In this sense, it is important to perform deeper studies of the different possibilities for hologram conservation. Usually, the first step after recording is the material cure, with UV or visible light, to eliminate the residual dye and monomer. With this process high efficiency holograms can also be obtained. Afterwards, an index matching technique can be used to cover the material with a glass or it is possible the application of aerosol sealant. In this paper we analyze the introduction of holograms between two glasses linked by pressure, using Bayfol HX 200 from Covestro as the recording material. In order to characterize the process, four different spatial frequencies were tested, which were stored either by transmission or reflection schemes. The data of the reconstruction step has been measured before and after the encapsulation. In addition, multiple holograms have been superposed in the same glass, where we have found that shrinkage is more significant.
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