377 research outputs found
Deep Learning-Driven Extraction of Superluminescent Diodes Parameters
We present a deep learning-based method for the automatic extraction of physical parameters from optical spectra and power values of a chirped, tapered, dual-section quantum dot superluminescent diode. The neural network is able to estimate a set of parameters that are capable of reproducing the behavior of the target device with high accuracy
Novel Design and Operation of Photonic- integrated WSS for Ultra-wideband Applications
Photonic integrated solutions for switching applications can yield large bandwidth and high reconfigurability while requiring low power and footprint. We propose a modular, scalable photonic integrated multi-band wavelength selective switch, able to independently route the input fiber channels to an arbitrary number of output ports
Performance evaluation of data-driven techniques for the softwarized and agnostic management of an NĂ—N photonic switch
The emerging Software Defined Networking (SDN) paradigm paves the way for flexible and automatized management at each layer. The SDN-enabled optical network requires each network element’s software abstraction to enable complete control by the centralized network controller. Nowadays, silicon photonics due to its low energy consumption, low latency, and small footprint is a promising technology for implementing photonic switching topologies, enabling transparent lightpath routing in re-configurable add-drop multiplexers. To this aim, a model for the complete management of photonic switching systems’ control states is fundamental for network control. Typically, photonics-based switches are structured by exploiting the modern technology of Photonic Integrated Circuit (PIC) that enables complex elementary cell structures to be driven individually. Thus PIC switches’ control states are combinations of a large set of elementary controls, and their definition is a challenging task. In this scenario, we propose the use of several data-driven techniques based on Machine Learning (ML) to model the control states of a PIC N×N photonic switch in a completely blind manner. The proposed ML-based techniques are trained and tested in a completely topological and technological agnostic way, and we envision their application in a real-time control plane. The proposed techniques’ scalability and accuracy are validated by considering three different switching topologies: the Honey-Comb Rearrangeable Optical Switch (HCROS), Spanke-Beneš, and the Beneš network. Excellent results in terms of predicting the control states are achieved for all of the considered topologies
Machine learning Assisted Accurate Estimation of QoT Impairments of Photonics Switching System on 400ZR
We propose a machine learning-based technique that accurately estimates quality-of-transmission (QoT) impairments of an optical switch on 400ZR. The proposed scheme works in an entirely agnostic way reduces inaccuracy in QoT impairments estimaÂtion by 1.5 dB
Autonomous Data-driven Model for Extraction of VCSEL Circuit-level Parameters
In recent years, a number of computationally efficient models have been developed that adequately describe the static and dynamic behavior of the Vertical Cavity Surface Emitting Laser (VCSEL). In order to correctly recreate the behavior of existing laser sources, a large number of physical parameters must be specified. Finding these unknown physical characteristics in experimental curves may be time-consuming, and mainly requires trial and error processes or regression analysis. Instead of manually analyzing experimental data to find the best VCSEL parameters, we propose a Machine Learning (ML) based solution to automate the process. The proposed approach exploits the parametric dataset obtained from Light-current and Small-signal modulation responses to extract the required model parameters. Excellent results are obtained in terms of relative prediction error
Modular and scalable photonic integrated multi-band wavelength-selective switch
Today’s optical transmission landscape is seeing a rapid increase in resource demand, due to bandwidth-intensive
applications, emerging standards, such as 5G, as well as the expansion of the Internet-of-Things (IoT) paradigm. This
requires an expansion of the current optical network infrastructure and capability, accommodating the increasing
demand [1]. From the network operator standpoint, two main solutions are available: new infrastructure can be
deployed, which represents the expensive solution, or the residual capacity of the existing network can be exploited
through multi-band paradigms, which represents the more cost-effective solution [2].
To achieve the full utilization of the remaining available fiber spectrum, new technologies such as Band-Division
Multiplexing (BDM) must be enabled on top of the already existing Wavelength-Division Multiplexing (WDM) based
network. This requires switching and filtering elements suited for an ultra-wide bandwidth of operation, allowing
consistent performances in the whole needed spectrum. For this purpose, photonic integrated circuits (PICs)
represent an ideal solution, as they provide a large bandwidth of operation while maintaining low footprint, cost,
and power consumption. To this end, we propose a fully integrated modular wavelength-selective switch (WSS),
able to independently route each of the input signal channels towards the desired output port, operating on the
S+C+L optical transmission windows
Modular Photonic-Integrated Device for Multi-Band Wavelength-Selective Switching
We propose a Silicon Photonics based WSS for S+C+L bands, independently routing any input channel to the desired output fiber. BER and OSNR for a system with 30 total channels are evaluated with Synopsys Optsim
Performance Analysis of Novel Multi-band Photonic-integrated WSS Operated on 400ZR
We present a detailed performance analysis of a novel photonic integrated wide-band wavelength selective switch operating in S+c+L bands. The results demonstrate that the proposed device offers low loss and frequency flat behavior for the considered band in a single or cascade implementation
Two-step machine learning assisted extraction of VCSEL parameters
We propose a Machine Learning (ML) assisted procedure to extract Vertical Cavity Surface Emitting Lasers (VCSELs) parameters from Light-Current (L-I) and S21 curves using a two-step algorithm to ensure high accuracy of the prediction. In the first step, temperature effects are not included and a Deep Neural Network (DNN) is trained on a dataset of 10000 mean-field VCSEL simulations, obtained changing nine temperature-independent parameters. The agent is used to retrieve those parameters from experimental results at a fixed temperature. Secondly, additional nine temperature-dependent parameters are analyzed while keeping as constant the extracted ones and changing the operation temperature. In this way a second dataset of 10000 simulations is created and a new agent in trained to extract those parameters from temperature-dependent L-I and S21 curves
Machine Learning Assisted Extraction of Vertical Cavity Surface Emitting Lasers Parameters
We propose a machine learning-based framework to extract circuit-level VCSEL model parameters. The proposed approach predicts the parameters exploiting the light-current curve and small-signal modulation responses with two steps at constant and variable temperature, respectively. Promising results are achieved in terms of relative prediction error
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