Foundry-Enabled Scalable All-to-All Optical Interconnects Using Silicon Nitride Arrayed Waveguide Router Interposers and Silicon Photonic Transceivers

This paper summarizes our latest results of integrated all-to-all optical interconnect systems using compact, low-loss silicon nitride (SiN) arrayed waveguide grating router (AWGR) through AIM photonics' multiple-project-wafer services. In particular, we have designed, taped out, and initially characterized a chip-scale silicon photonic low-latency interconnect optical network switch (Si-LIONS) system with an 8 × 8 200 GHz spacing cyclic SiN AWGR, 64 microdisk modulators, and 64 on-chip germanium photodector (PD). The 8 × 8 SiN AWGR in design has a measured insertion loss of 1.8 dB and a crosstalk of -13 dB, with a footprint of 1.3 mm × 0.9 mm. We measured an error-free performance of the microdisk modulator at 10 Gb/s upon 1Vpp voltage swing. We demonstrated wavelength routing with error-free data transmission using the on-chip modulator, SiN AWGR, and an external PD. We have designed and taped out the optical interposer version of the all-to-all system using SiN waveguides and low-loss chip-to-interposer couplers. Finally, we illustrate our preliminary designs and results of 16 × 16 and 32 × 32 SiN AWGRs, and discuss the possibility of scaling beyond 1024 × 1024 all-to-all interconnections with reduced number of wavelengths (e.g., 64) using the Thin-CLOS architecture

On Cooperative Fault Management in Multi-Domain Optical Networks Using Hybrid Learning

This paper presents a hybrid learning approach for cooperative fault management in multi-domain optical networks (MD-ONs). The proposed approach relies on a broker-based MD-ON architecture for coordination of inter-domain service provisioning. We first propose a self-supervised learning design for soft failure detection. The self-supervised learning design makes use of a clustering algorithm for extracting normal and abnormal patterns from optical performance monitoring data and a supervised learning-based classifier trained with the learned patterns for online detection. To facilitate high soft failure detection accuracy in the absence of sufficient abnormal data for training, the proposed design estimates model uncertainties during predictions and identifies instances associated with high uncertainties as also soft failures. Then, we extend the self-supervised learning design and present a federated learning framework for the broker plane and DMs to learn cooperatively while complying with the privacy constraints of each domain. Finally, a data-driven soft failure localization scheme that operates by analyzing the patterns of data is proposed as a complement to the existing approaches. Performance evaluations indicate that the self-supervised learning design can achieve soft failure detection accuracy of up to ∼ 97% with 0.01%-0.04% false alarm rate, while federated learning enables DMs to realize >90% soft failure detection rates in the cases of highly unbalanced data distribution (two of the three domains possess zero abnormal data for training)

Hierarchical Learning for Cognitive End-to-End Service Provisioning in Multi-Domain Autonomous Optical Networks

This paper demonstrates, for the first time to our knowledge, hierarchical learning framework for inter-domain service provisioning in software-defined elastic optical networking (EON). By using a broker-based hierarchical architecture, the broker collaborates with the domain managers to realize efficient global service provisioning without violating the privacy constrains of each domain. In the proposed hierarchical learning scheme, machine learning-based cognition agents exist in the domain managers as well as in the broker. The proposed system is experimentally demonstrated on a two-domain seven-node EON testbed for with real-time optical performance monitors (OPMs). By using over 42000 datasets collected from OPM units, the cognition agents can be trained to accurately infer the Q-factor of an unestablished or established lightpath, enabling an impairment-aware end-to-end service provisioning with an prediction Q-factor deviation less than 0.6 dB

Robotic Telescopes and Networks: New Tools for Education and Science

Nowadays many telescopes around the world are automated and some networks of robotic telescopes are active or planned as shown by the lists we draw up. Such equipment could be used for the training of students and for science in the Universities of Developing Countries and of New Astronomical Countries, by sending them observational data via Internet or through remotely controlled telescopes. It seems that it is time to open up for discussion with UN and ESA organizations and also with IAU, how to implement links between robotic telescopes and such Universities applying for collaborations. Many scientific fields could thus be accessible to them, for example on stellar variability, near-earth object follow-up, gamma-ray burst counterpart tracking, and so on.Comment: 18 pages, review presented at Eight UN/ESA Workshop on Basic Space Science: Scientific Exploration from Space, held in Mafraq (Jordan), 13-17 March 1999, to be published in Astrophys. and Space Sc. (Kluwer

Layered switch architectures for high-capacity optical transport networks

Cataloged from PDF version of article.We propose and analyze layered switch architectures that possess high design flexibility, greatly reduced switch size, and high expandability. The improvement in loss and crosstalk due to the reduced switch size is also discussed. Theoretical models have been developed to compute the network blocking probability using these architectures. Low blocking probability and high network utilization are achieved because of the capability of communication between layers in adjacent switches. The results show that the proposed layered switch architectures are very attractive for high-capacity optical transport networks

Silicon Photonic Flex-LIONS for Bandwidth-Reconfigurable Optical Interconnects

This paper reports the first experimental demonstration of silicon photonic (SiPh) Flex-LIONS, a bandwidth-reconfigurable SiPh switching fabric based on wavelength routing in arrayed waveguide grating routers (AWGRs) and space switching. Compared with the state-of-the-art bandwidth-reconfigurable switching fabrics, Flex-LIONS architecture exhibits 21× less number of switching elements and 2.9× lower on-chip loss for 64 ports, which indicates significant improvements in scalability and energy efficiency. System experimental results carried out with an 8-port SiPh Flex-LIONS prototype demonstrate error-free one-to-eight multicast interconnection at 25 Gb/s and bandwidth reconfiguration from 25 Gb/s to 100 Gb/s between selected input and output ports. Besides, benchmarking simulation results show that Flex-LIONS can provide a 1.33× reduction in packet latency and >1.5× improvements in energy efficiency when replacing the core layer switches of Fat-Tree topologies with Flex-LIONS. Finally, we discuss the possibility of scaling Flex-LIONS up to N = 1024 ports (N = M × W) by arranging M^2 W-port Flex-LIONS in a Thin-CLOS architecture using W wavelengths

Self-Taught Anomaly Detection With Hybrid Unsupervised/Supervised Machine Learning in Optical Networks

This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised machine learning scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to 99% anomaly detection accuracy can be achieved with a false positive rate below 1%

Multi-FSR Silicon Photonic Flex-LIONS Module for Bandwidth-Reconfigurable All-to-All Optical Interconnects

This article proposes and experimentally demonstrates the first bandwidth-reconfigurable all-to-all optical interconnects using a multi-Free-Spectral-Ranges (FSR) integrated 8 × 8 SiPh Flex-LIONS module. The multi-FSR operation utilizes the first FSR (FSR1) to steer the bandwidth between selected node pairs and the zeroth FSR (FSR0) to guarantee a minimum diameter all-to-all topology among the interconnected nodes after reconfiguration. Successful Flex-LIONS design, fabrication, packaging, and system testing demonstrate error-free all-to-all interconnects for both FSR0 and FSR1 with a 5.3-dB power penalty induced by AWGR intra-band crosstalk under the worst-case polarization scenario. After reconfiguration in FSR1, the bandwidth between the selected pair of nodes is increased from 50 to 125 Gb/s while maintaining a 25 Gb/s/λ all-to-all interconnectivity in FSR0