All-optical phase regeneration aims at restoring the phase information of coherently encoded data signals directly in the optical domain so as to compensate for phase distortions caused by transceiver imperfections and nonlinear impairments along the transmission link. Although it was proposed two decades ago, all-optical phase regeneration has not been seen in realistic networks to date, mainly because this technique entails complex bulk modules and relies on high-precision phase sensitive nonlinear dynamics, both of which are adverse to field deployment. Here, we demonstrate a new, to the best of our knowledge, architecture to implement all-optical phase regeneration using integrated photonic devices. In particular, we realize quadrature phase quantization by exploring the phase-sensitive parametric wave mixing within on-chip silicon waveguides, while multiple coherent pump laser tones are provided by a chip-scale micro-cavity Kerr frequency comb. Multi-channel all-optical phase regeneration is experimentally demonstrated for 40 Gbps QPSK data, achieving the best SNR improvement of more than 6 dB. Our study showcases a promising avenue to enable the practical implementation of all-optical phase regeneration in realistic long-distance fiber transmission networks.
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http://dx.doi.org/10.1364/OL.493475 | DOI Listing |
A SbS-based reconfigurable diffractive optical neural network (RDONN) for on-chip integration is proposed. The RDONN can be integrated into standard silicon-on-insulator systems, offering a compact, passive, all-optical solution for implementing machine learning functions. The weights of the proposed optical chip are reconfigurable without the need to modify hardware structures or re-fabricate the chip.
View Article and Find Full Text PDFSpatial differentiation is the key element for edge detection and holds unquestionable significance in the current information era. All-optical computation based on metasurfaces has emerged as a powerful platform for spatial differentiation due to its advantage of high integration and parallel processing. However, while most current works focus on one- or two-dimensional (2D) spatial differentiation, three-dimensional (3D) all-optical computation for compact spatial differentiator remains elusive.
View Article and Find Full Text PDFTransition metal phosphorus sulfides (MPS), a family of two-dimensional magnetic materials with a van der Waals structure, exhibit promising applications in nonlinear optical devices. The emergence of carrier coherence in MPS is a fascinating topic in coherently controlling the nonlinear effect (or other novel phenomena). Herein, we systematically investigated the third-order nonlinear optical responses of MPS (M = Ni, Fe, Mn) flake suspensions based on spatial self-phase modulation (SSPM) effect.
View Article and Find Full Text PDFWith the increase sizes of training datasets and models, the bottleneck in distributed machine learning (DML) training has shifted from computation to communication. To address this bottleneck, we propose an all-optical switching network architecture for accelerating the communication phase of DML training. Experimental results validate packets with error-free and 385 ns server-to-server low-latency communication at traffic load of 0.
View Article and Find Full Text PDFThis study investigates (EIG) in a nanohybrid configuration involving a semiconductor quantum dot (SQD) and a core-shell bimetallic nanoparticle coated with graphene. The goal is to optimize interactions between plasmons and excitons. This is achieved by utilizing nanoparticles covered with graphene, which enhances control over surface plasmons.
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