We demonstrate an optical static random access memory cell that provides read and write functionality at 5 Gb/s. The circuit comprises a hybridly integrated semiconductor optical amplifier-Mach-Zehnder interferometer (SOA-MZI) flip-flop serving as the memory unit and two additional SOA-based cross-gain modulation switches for controlling access to the memory cell.

With metal stripes being intrinsic components of plasmonic waveguides, plasmonics provides a “naturally” energy-efficient platform for merging broadband optical links with intelligent electronic processing, instigating a great promise for low-power and small-footprint active functional circuitry. The first active Dielectric-Loaded Surface Plasmon Polariton (DLSPP) thermo-optic (TO) switches with successful performance in single-channel 10 Gb/s data traffic environments have led the inroad towards bringing low-power active plasmonics in practical traffic applications. In this article, we introduce active plasmonics into Wavelength Division Multiplexed (WDM) switching applications, using the smallest TO DLSPP-based Mach-Zehnder interferometric switch reported so far and showing its successful performance in 4×10 Gb/s low-power and fast switching operation. The demonstration of the WDM-enabling characteristics of active plasmonic circuits with an ultra-low power × response time product represents a crucial milestone in the development of active plasmonics towards real telecom and datacom applications, where low-energy and fast TO operation with small-size circuitry is targeted.

Surface plasmon propagating modes supported by metal/dielectric interfaces in various configurations can be used for radiation guiding similarly to conventional dielectric waveguides. Plasmonic waveguides offer two attractive features: subdiffraction mode confinement and the presence of conducting elements at the mode‐field maximum. The first feature can be exploited to realize ultrahigh density of nanophotonics components, whereas the second feature enables the development of dynamic components controlling the plasmon propagation with ultralow signals, minimizing heat dissipation in switching elements. While the first feature is yet to be brought close to the domain of practical applications because of high propagation losses, the second one is already being investigated for bringing down power requirements in optical communication systems. In this review, the latest application‐oriented research on radiation modulation and routing using thermo‐optic dielectric‐loaded plasmonic waveguide components integrated with silicon‐based photonic waveguides is overviewed. Their employment under conditions of real telecommunications is addressed, highlighting challenges and perspectives.

We present theoretical and experimental performance analysis of 40 Gb/s Non-Return-to-Zero (NRZ) All-Optical Wavelength Conversion (AOWC) using a differentially-biased SOA-MZI. A frequency domain transfer function model for both the standard single-control SOA-MZI-based AOWC and for the differentially-biased SOA-MZI is analytically derived, exploiting first order perturbation theory techniques and showing that only the differentially-biased scheme can yield an almost flat low-pass filtering response enabling wavelength conversion at 40 Gb/s. The theoretically obtained results are also confirmed through experiments that demonstrate successful 40 Gb/s AOWC functionality for NRZ data signals only when a differentially-biased SOA-MZI configuration is employed, whereas an error-floor is obtained when 40 Gb/s NRZ AOWC with the standard single-control SOA-MZI scheme is attempted. The 1.7 dB negative power penalty obtained by the differentially-biased SOA-MZI architecture confirms its enhanced regenerative properties and its potential for extending 40 Gb/s optical transparent network dimensions by means of cascaded 2R AOWC stages.

A comprehensive theoretical analysis of end-fire coupling between dielectric-loaded surface plasmon polariton and rib/wire silicon-on-insulator (SOI) waveguides is presented. Simulations are based on the 3-D vector finite element method. The geometrical parameters of the interface are varied in order to identify the ones leading to optimum performance, i.e., maximum coupling efficiency. Fabrication tolerances about the optimum parameter values are also assessed. In addition, the effect of a longitudinal metallic stripe gap on coupling efficiency is quantified, since such gaps have been observed in fabricated structures. Finally, theoretical results are compared against insertion loss measurements, carried out for two distinct sets of samples comprising rib and wire SOI waveguides, respectively.

Background: This study targets the development of an explainable deep learning methodology for the automatic classification of coronary artery disease, utilizing SPECT MPI images. Deep learning is currently judged as non-transparent due to the model’s complex non-linear structure, and thus, it is considered a «black box», making it hard to gain a comprehensive understanding of its internal processes and explain its behavior. Existing explainable artificial intelligence tools can provide insights into the internal functionality of deep learning and especially of convolutional neural networks, allowing transparency and interpretation. Methods: This study seeks to address the identification of patients’ CAD status (infarction, ischemia or normal) by developing an explainable deep learning pipeline in the form of a handcrafted convolutional neural network. The proposed RGB-CNN model utilizes various pre- and post-processing tools and deploys a state-of-the-art explainability tool to produce more interpretable predictions in decision making. The dataset includes cases from 625 patients as stress and rest representations, comprising 127 infarction, 241 ischemic, and 257 normal cases previously classified by a doctor. The imaging dataset was split into 20% for testing and 80% for training, of which 15% was further used for validation purposes. Data augmentation was employed to increase generalization. The efficacy of the well-known Grad-CAM-based color visualization approach was also evaluated in this research to provide predictions with interpretability in the detection of infarction and ischemia in SPECT MPI images, counterbalancing any lack of rationale in the results extracted by the CNNs. Results: The proposed model achieved 93.3% accuracy and 94.58% AUC, demonstrating efficient performance and stability. Grad-CAM has shown to be a valuable tool for explaining CNN-based judgments in SPECT MPI images, allowing nuclear physicians to make fast and confident judgments by using the visual explanations offered. Conclusions: Prediction results indicate a robust and efficient model based on the deep learning methodology which is proposed for CAD diagnosis in nuclear medicine.