Optical computing with supercontinuum generation in photonic crystal fibers

Fuente: arXiv
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Hauptverfasser: Muda, Azka Maula Iskandar, Teğin, Uğur
Format: Preprint
Veröffentlicht: 2024
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author Muda, Azka Maula Iskandar
Teğin, Uğur
author_facet Muda, Azka Maula Iskandar
Teğin, Uğur
contents We introduce a novel photonic neural network using photonic crystal fibers, leveraging femtosecond pulse supercontinuum generation for optical computing. Investigating its efficacy across machine learning tasks, we uncover the crucial impact of nonlinear pulse propagation dynamics on network performance. Our findings show that octave-spanning supercontinuum generation results in loss of dataset variety due to many-to-one mapping, and optimal performance requires balancing optical nonlinearity. This study offers guidance for designing energy-efficient and high-performance photonic neural network architectures by explaining the interplay between nonlinear dynamics and optical computing.
format Preprint
id arxiv_https___arxiv_org_abs_2403_07667
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Optical computing with supercontinuum generation in photonic crystal fibers
Muda, Azka Maula Iskandar
Teğin, Uğur
Optics
Emerging Technologies
We introduce a novel photonic neural network using photonic crystal fibers, leveraging femtosecond pulse supercontinuum generation for optical computing. Investigating its efficacy across machine learning tasks, we uncover the crucial impact of nonlinear pulse propagation dynamics on network performance. Our findings show that octave-spanning supercontinuum generation results in loss of dataset variety due to many-to-one mapping, and optimal performance requires balancing optical nonlinearity. This study offers guidance for designing energy-efficient and high-performance photonic neural network architectures by explaining the interplay between nonlinear dynamics and optical computing.
title Optical computing with supercontinuum generation in photonic crystal fibers
topic Optics
Emerging Technologies
url https://arxiv.org/abs/2403.07667