Optical computing with supercontinuum generation in photonic crystal fibers

2024/03/12 by Azka Maula Iskandar Muda, Muda, Azka Maula Iskandar, Uğur Teğin +1 · 5 citations
Engineering · #Advanced Photonic Communication Systems #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Physical sciences #Materials science #Optical Network Technologies #Optical fiber #Optics #Optics (physics.optics) #Optoelectronics #Photonic crystal #Photonic-crystal fiber #Physics #Semiconductor Lasers and Optical Devices #Supercontinuum

paper · pdf · doi:10.48550/arxiv.2403.07667

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2024/03/12 · openalex created_date 2024/03/14 · openalex updated_date 2026/07/28

Abstract

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.

Cited by

• Fiber-based diffractive deep neural network
• Trainable dynamical masking for readout-free optical computing
• Self-optimizing multichannel optical computing
• Recurrent neural networks implemented through spatiotemporal light propagation in optical fibers
• Multimode fiber laser cavities as nonlinear optical processors

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