We demonstrate a hybrid-integrated linear-chirp frequency-modulated continuous wave laser based on self-injection locking to a high-Q lithium niobate multimode microring resonator with a 1 kHz linewidth and 1 MHz modu...
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We demonstrate the first $\mathbf{1.2}\mathbf{Tb}/\mathbf{s}/\boldsymbol{\lambda}$ line rate IM/DD optical interconnects using ultra-high-order modulation and Stokes vector receiver. This achievement utilizes polariza...
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ISBN:
(数字)9798350379266
ISBN:
(纸本)9798350379273
We demonstrate the first $\mathbf{1.2}\mathbf{Tb}/\mathbf{s}/\boldsymbol{\lambda}$ line rate IM/DD optical interconnects using ultra-high-order modulation and Stokes vector receiver. This achievement utilizes polarization division multiplexing and 154-GBd PS-PAM20 signaling, resulting in a net $\mathbf{932}\mathbf{Gb}/\mathbf{s}/\boldsymbol{\lambda}$ transmission record.
We built a standalone prototype for generating soliton microcombs. Our prototype supports both manual and automatic soliton generation methods, laying a good foundation for the commercial application of microcombs whi...
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We built a standalone prototype for generating soliton microcombs. Our prototype supports both manual and automatic soliton generation methods, laying a good foundation for the commercial application of microcombs whi...
To achieve a flat and uniform sensing surface for fiber-tip surface plasmon resonance devices, we have developed a plasmonic crystal cavity in metal-insulator-metal structure. Biotinylated protein binding has been det...
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We demonstrate a hybrid-integrated linear-chirp frequency-modulated continuous wave laser based on self-injection locking to a high-Q lithium niobate multimode microring resonator with a 1 kHz linewidth and 1 MHz modu...
详细信息
To achieve a flat and uniform sensing surface for fiber-tip surface plasmon resonance devices, we have developed a plasmonic crystal cavity in metal-insulator-metal structure. Biotinylated protein binding has been det...
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Reconfigurable optical add-drop multiplexers (ROADMs) based on wavelength selective switches (WSSs) serve as the cornerstone for dynamic signal routing in all-opticalnetworks. With growing demands for higher capacity...
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Reconfigurable optical add-drop multiplexers (ROADMs) based on wavelength selective switches (WSSs) serve as the cornerstone for dynamic signal routing in all-opticalnetworks. With growing demands for higher capacity and flexibility, the deployment of large-scale, high-degree ROADM networks has significantly increased. However, this expansion increases the impact of failures in ROADMs, which may cause widespread service disruption across all fibers connected to a ROADM node. Therefore, it is crucial to monitor and visualize the status of all ROADMs in opticalnetworks. Current approaches are limited by their reliance on scarce real-system failure data or the need for additional monitors. To address these challenges, in this paper, we propose a physics-based digital twin assisted link status visualization (DT-LSV) framework for monitoring ROADM status, specifically focusing on filter shift (FS) and filter tightening (FT) failures. Our approach is only based on the receiver digital signal processing (DSP) and configuration parameters, eliminating the need for additional monitors. In the absence of labeled failure data, DT-LSV utilizes normal data, and as labeled failure data accumulates, it gradually learns to further enhance performance. By integrating a parameter learning module into the theoretical model, we construct a physics-based digital twin (DT) model. Using this model and real data from receiver DSP, we formulate and solve an optimization problem for accurate ROADM status visualization. Our framework is validated through extensive experiments, achieving a 100% accuracy in localizing ≥ 5 GHz FS (with a 0.66 dB signal-to-noise ratio (SNR) penalty) and ≥ 8.5 GHz FT anomalies (with a 1.31 dB SNR penalty). The root mean square error (RMSE) of failure magnitude estimation is 0.60 GHz for FS and 0.41 GHz for FT. Additionally, the high efficiency of the framework is demonstrated, the importance of the parameter learning module is underscored, and the effectiveness
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