Poster + Paper
18 June 2024 Estimation of characteristic parameters of holographic volume gratings based on machine learning
Jaume Colomina Martínez, Joan Josep Sirvent-Verdú, Juan Carlos Bravo, Andrés Pérez-Bernabeu, Mariela L. Álvarez, Jorge Francés, Cristian Neipp
Author Affiliations +
Conference Poster
Abstract
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.
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jaume Colomina Martínez, Joan Josep Sirvent-Verdú, Juan Carlos Bravo, Andrés Pérez-Bernabeu, Mariela L. Álvarez, Jorge Francés, and Cristian Neipp "Estimation of characteristic parameters of holographic volume gratings based on machine learning", Proc. SPIE 13015, Photosensitive Materials and their Applications III, 1301516 (18 June 2024); https://doi.org/10.1117/12.3016976
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KEYWORDS
Diffraction gratings

Holography

Optical gratings

Diffraction

Modulation

Volume holography

Machine learning

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