We experimentally demonstrate a pump-pulse-induced conversion of noise into solitons in multimode optical fibers. The process is based on the recently discovered phenomenon of soliton self-mode conversion, where a pump soliton in a higher-order spatial mode crafts another well-defined soliton, originating purely from noise, in a lower-order mode at a longer wavelength through intermodal Raman scattering. The lack of the need for any seed or cavity feedback demonstrates that soliton self-mode conversion is a fundamentally unavoidable, but nevertheless tailorable and hence useful, self-organizing nonlinear optical effect capable of turning noise into transform limited solitons.
Emerging applications of wavelength conversion
We discuss three emerging applications of wavelength conversion: 1) hybrid amplification outside of EDFA band, based on a combination of two wavelength converters and an EDFA, 2) spatial-mode-selective wavelength conversion, and 3) generation of spatial-mode-entangled photon pairs.
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- NSF-PAR ID:
- 10309489
- Date Published:
- Journal Name:
- 2021 IEEE Photonics Conference (IPC)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Optical networks satisfy high bandwidth and low latency requirements for telecommunication networks and data center interconnection. To improve network resource utilization, machine learning (ML) is used to accurately model optical amplifiers such as erbium-doped fiber amplifiers (EDFAs), which impact end-to-end system performance such as quality of transmission. However, a comprehensive measurement dataset is required for ML to accurately predict an EDFA’s wavelength-dependent gain. We present an open dataset consisting of 202,752 gain spectrum measurements collected from 16 commercial-grade reconfigurable optical add–drop multiplexer (ROADM) booster and pre-amplifier EDFAs under varying gain settings and diverse channel-loading configurations over 2,785 hours in total, with a total dataset size of 3.1 GB. With this EDFA dataset, we implemented component-level deep-neural-network-based EDFA models and use transfer learning (TL) to transfer the EDFA model among 16 ROADM EDFAs, which achieve less than 0.18/0.24 dB mean absolute error for booster/pre-amplifier gain prediction using only 0.5% of the full target training set. We also showed that TL reduces the EDFA data collection requirements on a new gain setting or a different type of EDFA on the same ROADM.
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Abstract In this work, mode conversion and wavefront shaping by integrating a metallic metasurface on top of a planar waveguide are proposed and demonstrated. The metasurface consists of C‐shaped nanoantennas. By controlling the orientation of each nanoantenna, mode conversion and focusing effect for the cross‐polarized electric fields inside the waveguide are achieved. The design and simulation results of 16 scenarios of wideband transverse‐magnetic to transverse‐electric mode converters with the mode purity up to 98%, and on‐chip lenses at the wavelength of 1550 nm are reported. It is worth noting that the dimension of the devices along the propagation direction is only 9.6 µm. This work manifests the potential application of mode division multiplexing systems and on‐chip optical interconnections based on metasurfaces.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IPC48725.2021.9592892
