Thin atomic-vapor cells for all-optical neural network nonlinear activation

We present thin Rb-vapor cells with nonlinear transmission for compact integration with all-optical neural networks. The efficacy of the cells as nonlinear activation layers is demonstrated experimentally in optical neural networks for image classification.

Authors

Uriel Levy (ORCID: https://orcid.org/0000-0002-5918-1876)
Jacob Engelberg (ORCID: https://orcid.org/0000-0002-7421-914X)
David Ohana (ORCID: https://orcid.org/0000-0001-9300-5879)
Noa Mazurski
Roy Maman

Publication Details

Published: 2025-01-01
DOI: https://doi.org/10.1364/cleo_at.2025.jps200_43
Primary Topic: Neural Networks and Reservoir Computing
Type: article
Field-Weighted Citation Impact: 0.00

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article

Thin atomic-vapor cells for all-optical neural network nonlinear activation

Uriel Levy, Jacob Engelberg, David Ohana, Noa Mazurski et al.

Jan 1, 2025

Neural Networks and Reservoir Computing

article

Thin atomic-vapor cells for all-optical neural network nonlinear activation

Uriel Levy, Jacob Engelberg, David Ohana, Noa Mazurski, Roy Maman

January 1, 2025article en

Abstract

Openalex Percentile: Top 87%

Neural Networks and Reservoir Computing

DOI Source OpenAlex

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Thin atomic-vapor cells for all-optical neural network nonlinear activation — Uriel Levy, Jacob Engelberg, et al. · (2025) | TGRS Research Map | TGRS