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

doi:10.1117/12.3016976

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 +

Proceedings Volume 13015, Photosensitive Materials and their Applications III; 1301516 (2024) https://doi.org/10.1117/12.3016976
Event: SPIE Photonics Europe, 2024, Strasbourg, France

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available