Optical deep learning with multimode signals

Yuval Tamir; Moti Fridman

doi:10.1117/12.3003044

13 March 2024 Optical deep learning with multimode signals

Yuval Tamir, Moti Fridman

Proceedings Volume PC12903, AI and Optical Data Sciences V; PC1290309 (2024) https://doi.org/10.1117/12.3003044
Event: SPIE OPTO, 2024, San Francisco, California, United States

Abstract

Deep learning has emerged as a powerful tool for solving complex problems in a wide range of domains. The success of deep learning can be attributed to several factors, including the availability of massive datasets, the increasing computing power of modern hardware, and the development of efficient algorithms. Still, In the modern era of information and communication technologies, the demand for faster and more efficient data transmission has driven researchers to explore novel approaches to enhance communication systems, among them is the optical approach for such a problem. In our lab, we develop a fully optical deep learning network that is based on high order spatial mode, and the ultrafast nonlinear four wave mixing interactions inside multimode fibers. We exploit the optical nonlinear interactions between waves for developing a deep learning network that is faster than any electronic based network. In this study, we present the algorithm we developed and the theoretical implementation of such network. In addition, we demonstrate our ability to decompose and classify ultrafast signals, such as temporal modes combinations, which are typically undetectable by standard devices,

Conference Presentation

Citation Download Citation

Yuval Tamir and Moti Fridman "Optical deep learning with multimode signals", Proc. SPIE PC12903, AI and Optical Data Sciences V, PC1290309 (13 March 2024); https://doi.org/10.1117/12.3003044

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