All-optical Networks Research Articles

Two-dimensional material integrated micro-nano fiber, the new opportunity in all-optical signal processing

Abstract With the development of all-optical networks, all-optical devices have become a research hotspot in recent years. Two-dimensional materials, represented by graphene and black phosphorus, have attracted great interest in the scientific community due to their excellent optical, electrical, magnetic, and mechanical properties. Bridging the gap between fiber optics and nanotechnology, microfibers can interact with light and matter at the micro or even nanoscale. By combining two-dimensional materials with microfibers, composite waveguides can be formed. They have the advantages of high nonlinear effect, all-fiber structure, and high damage threshold, etc. The composite waveguide can be directly applied to optical fiber communication systems, and plays an important role in the field of all-optical signal processing with a huge application prospect. In this review, the properties of typical 2D materials are first introduced. Next, the preparation methods of the relevant equipments are introduced and compared. Then, the all-optical signal processing technology based on 2D material-integrated microfiber composite waveguide is reviewed. The latest developments of all-optical modulators, all-optical wavelength converters, all-optical logic gates and all-optical thresholding devices are presented. Finally, the challenges and opportunities for the future development of 2D materials-integrated microfiber optoelectronic devices are summarized.

Pulmonary Nodule Detection and Classification Using All-Optical Deep Diffractive Neural Network.

A deep diffractive neural network (D2NN) is a fast optical computing structure that has been widely used in image classification, logical operations, and other fields. Computed tomography (CT) imaging is a reliable method for detecting and analyzing pulmonary nodules. In this paper, we propose using an all-optical D2NN for pulmonary nodule detection and classification based on CT imaging for lung cancer. The network was trained based on the LIDC-IDRI dataset, and the performance was evaluated on a test set. For pulmonary nodule detection, the existence of nodules scanned from CT images were estimated with two-class classification based on the network, achieving a recall rate of 91.08% from the test set. For pulmonary nodule classification, benign and malignant nodules were also classified with two-class classification with an accuracy of 76.77% and an area under the curve (AUC) value of 0.8292. Our numerical simulations show the possibility of using optical neural networks for fast medical image processing and aided diagnosis.

Optical Logic Gates Excited by a Gauss Vortex Interference Beam Based on Spatial Self-Phase Modulation in 2D MoS2.

Vortex beams with optical orbital angular momentum have broad prospects in future high-speed and large-capacity optical communication. In this investigation of materials science, we found that low-dimensional materials have feasibility and reliability in the development of optical logic gates in all-optical signal processing and computing technology. We found that spatial self-phase modulation patterns through the MoS2 dispersions can be modulated by the initial intensity, phase, and topological charge of a Gauss vortex superposition interference beam. We utilized these three degrees of freedom as the input signals of the optical logic gate, and the intensity of a selected checkpoint on spatial self-phase modulation patterns as the output signal. By setting appropriate thresholds as logic codes 0 and 1, two sets of novel optical logic gates, including AND, OR, and NOT gates, were implemented. These optical logic gates are expected to have great potential in optical logic operations, all-optical networks, and all-optical signal processing.

Design of a photonic crystal fiber polarization beam splitter with simple structure and ultra-wide bandwidth

A novel polarization beam splitter (PBS) based on dual-core photonic crystal fiber (DC-PCF) is proposed in this work. The proposed DC-PCF PBS contains two kinds of lattices and three kinds of air holes to form the asymmetrical elliptic dual-core structure. By using the full-vector finite element method, the propagation characteristics of the proposed DC-PCF PBS are investigated. The simulation results show that the bandwidth of the proposed DC-PCF PBS can reach to 340 nm, which covers the S + C + L + U communication bands, the shortest splitting length is 1.97 mm, and the maximum extinction ratio appears near wavelength 1550 nm. Moreover, the insertion loss of the proposed DC-PCF PBS is very low. It is believed that the proposed DC-PCF PBS has important applications in the field of all-optical communication and network.

Plastic Photonic Synapse Based on VCSOA for Self-Learning in Photonic Spiking Neural Network

We propose and demonstrate a plastic photonic synapse to achieve all-optical synaptic plasticity based on the potentiated and depressed dynamics in commercially available vertical-cavity semiconductor optical amplifier (VCSOA). The effects of presynaptic and postsynaptic spike powers, bias current, initial wavelength detuning between presynaptic spike and VCSOA on the potentiated and depressed time windows are investigated. The numerical results show that all-optical synaptic plasticity can be achieved successfully based on VCSOA with delayed self-feedback loop. The proof-of-concept experimental results show that plastic photonic synapse can be achieved based on the VCSOA with subsequent iterations. Besides, the curve of VCSOA gain in the potentiated and depressed time windows is similar to that of spike timing dependent plasticity. Furthermore, all-optical unsupervised self-learning and recognition of first spike timing of an input pattern are realized numerically in the photonic neural network including two proposed plastic photonic synapses. The plastic photonic synapse based on a VCSOA is interesting and valuable for the self-learning in all-optical neural networks. Moreover, the proposed plastic photonic synapse offers great potential for dense, high-performance neuromorphic photonic systems.

SDSFLF: fault localization framework for optical communication using software digital switching network

Optical network is an emerging technology for data communication inworldwide. The information is transmitted from the source to destination through the fiber optics. All optical network (AON) provides good transmission transparency, good expandability, large bandwidth, lower bit error rate (BER), and high processing speed. Link failure and node failure haveconsistently occurred in the traditional methods. In order to overcome the above mentioned issues, this paper proposes a robust software defined switching enabled fault localization framework (SDSFLF) to monitor the node and link failure in an AON. In this work, a novel faulty node localization (FNL) algorithm is exploited to locate the faulty node. Then, the software defined faulty link detection (SDFLD) algorithm that addresses the problem of link failure. The failures are localized in multi traffic stream (MTS) and multi agent system (MAS). Thus, the throughput is improved in SDSFLF compared than other existing methods like traditional routing and wavelength assignment (RWA), simulated annealing (SA) algorithm, attackaware RWA (A-RWA) convex, longest path first (LPF) ordering, and biggest source-destination node degree (BND) ordering. The performance of the proposed algorithm is evaluated in terms of network load, wavelength utilization, packet loss rate, and burst loss rate. Hence, proposed SDSFLF assures that high performance is achieved than other traditional techniques.

All-Optical Recurrent Neural Network With Reconfigurable Activation Function

Optical Neural Networks (ONNs) can be promising alternatives for conventional electrical neural networks as they offer ultra-fast data processing with low energy consumption. However, lack of suitable nonlinearity is standing in their way of achieving this goal. While this problem can be circumvented in feed-forward neural networks, the performance of the recurrent neural networks (RNNs) depends heavily on their nonlinearity. In this paper, we first propose and numerically demonstrate a novel reconfigurable optical activation function, named ROA, based on adding or subtracting the outputs of two saturable absorbers (SAs). RAO can provide both bounded and unbounded outputs by facilitating an electrically programmable adder/subtractor design. Second, with the help of RAO, which can be altered to resemble Tanh or Sigmoid activation functions, we take a step further and numerically demonstrate OptoRNN, a new design for an all-optical RNN in free-space optics. Moreover, by benefiting from mathematical modeling of the multiplication noise, as well as an altered loss function, we enable vector-matrix multiplication (VMM) six times more parallel than conventional optical VMM method for the linear part of the OptoRNN. Finally, through comprehensive simulation studies, we demonstrate that utilizing the OptoRNN, we can achieve 96.3% train accuracy for sequential-MNIST dataset.

MoS2 as Nonlinear Optical Material for Optical Neural Networks

Despite the significant advancements in photonic computation in recent years, the inadequacy of optical nonlinearities limits the scalability of optical deep networks (ONNs). Molybdenum disulfide (MoS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ), with excellent nonlinear properties, is emerging as a promising candidate for nonlinear processing. Here, we investigate the saturable absorption of MoS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> by continuous wave lasers and illustrate the capability of MoS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> as an activation unit for nonlinear mapping in ONNs. Moreover, a simulation-based fully connected neural network is fabricated for mimicking the operation of ONNs and demonstrating image classification. The results show that the recognition accurateness ranged from 89% to 94%, depending on the morphology of MoS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . This article provides a guideline for the selection of nonlinear units and opens up the possibility of implementing all-optical neural networks.

Optimal design of a 4 × 4 MMI thermal optical switch with trapezoidal air trenches.

Optical switches are important in signal routing and switching. In this paper, a thermal optical switch with trapezoidal air trenches is proposed. The proposed structure consists of two cascaded 4×4 multimode interference (MMI) couplers. The beam propagation method is used to optimize the dimension and analyze the characteristics. Simulation results show excess loss (EL) and insertion loss (IL) are less than 0.14dB and 0.22dB at the wavelength of 1550nm, respectively. Besides, the extinction ratio (ER) is higher than 21dB. This design has the advantages of small size, low loss, and high flexibility, which is promising for application to all-optical network routing.

1 × 5 broadband photonic crystal power splitter designed by the Powell algorithm.

We propose a novel, to the best of our knowledge, 1×5 broadband power splitter based on the photonic crystal. The Powell algorithm is used to reverse-design the proposed broadband power splitter. The results show that the transmittance of each output port of the broadband photonic crystal power splitter can be adjusted by changing the radii and offsets of the dielectric rods at the junction area of each waveguide. According to the target splitting ratio, the reverse design of the structural parameters using the Powell algorithm significantly improves the optimization efficiency and splitting performance of the broadband power splitter. The designed power splitters have a wide working bandwidth of 1525-1565 nm, a flexible and designable power splitting ratio, excellent splitting performance, and a compact size, which have great application prospects in all-optical communication networks, high-density photon integration, and other fields.

Enhanced surface effects and optical property modulation of Ge2Sb2Te5 by pulsed laser irradiation

In this work, Ge2Sb2Te5 (GST) thin films are irradiated by a 1064 nm pulsed laser heat treatment system with different beam profiles. The surface effects induced by different laser conditions are studied systematically by atomic force microscope, spectroscopic ellipsometry, and Raman spectroscopy. It is found that a top-hat beam profile with uniform intensity distribution demonstrates the advantages of a non-destructive and homogeneous surface, which is critical for large-scale processing uniformity. The threshold laser fluence for the amorphization process is predicted by simulation and further proved by the laser irradiation experiment to be 27.9 mJ/cm2 at 1 ns pulse width. We further show that modulation of complex refractive indices of GST thin films can be achieved with different duty ratios (spatial ratio of amorphization part) from 0% to 100%. Our approach paves the way for the precise control of the optical properties of PCMs in emerging optical applications such as photonic switches, optical memories, and all-optical neural networks.

A Review on Nonlinearity in Semiconductor optical Amplifer as Application in all Optical Signal Processing

The optical signal processing is the future of communication network. High speed all-optical packet routing is one of the essential applications of all-optical networks which is only possible with the development of all-optical logic technology. The development of optical logic elements and circuits are the steps in the growth of this technology and higher speed up to Tbit/sec can be achieved. The optical carrier frequency range 1013 to 1016 Hz provides enormous potential bandwidth with superior information carrying capacity over a long transmission distance. The need of higher capacity is continuing to encourage research in wavelength division multiplexing (WDM) and optical time division multiplexing (OTDM) based transmission systems, which need optical demultiplexing and wavelength conversion technology. Therefore, for high-speed optical networks, it is required to develop the all-optical gates to avoid power consumption in opto-electronics conversion. All optical logic gates perform computing operations, storage and transmission of data using light also known as optical computing. Optical technology promises massive upgrades in the efficiency and speed of computers, aswell as significant shrinkage in their size and cost. The non-linear optical device to be used i.e. Semiconductor Optical Amplifier (SOA) has proved to be the promising for all optical functions like wavelength conversion, logic functions, signal representation in all optical domain. Its compact size, high gain, fast response, strong refractive index variation, easy to manufacture and integration, and power efficiency makes it most optimum device for optical signal processing. In this paper, the application of SOA in optical processing is thoroughly reviewed and orient towards the latest application in neural networks.

Flat Ball: Dynamic topology for energy management of optical interconnection networks in data centers

Cloud computing and web applications have further highlighted the need for powerful data centers with large bandwidth and low power consumption. In order to provide such a large bandwidth, today's electronic data centers require high power levels. Furthermore, communications and devices are not always used to their full potential. Optical networks can be a proper option for data centers, as they can offer large and variable bandwidth with lower power consumption compared to their electronic counterparts. In this regard, a dynamic all-optical network architecture is proposed in this paper, with bandwidth reconfiguration capability and low latency to reduce energy consumption. This proposed architecture, called Flat Ball, offers dynamic bandwidth utilizing passive optical devices with low power consumption and latency. Under realistic data center traffic scenarios, the latency of this network is up to 50% lower than its electrical counterpart, while providing a much higher throughput and consuming up to 60% less power.

Efficient and Fast All-Optical Modulator with In Situ Grown MoTe2 Nanosheets on Silicon

All-optical modulators play a key role in building all-optical networks for efficient optical signal processing and have attracted intensive attention. In this paper, all-optical modulators with in situ grown molybdenum telluride (MoTe2) nanosheets on silicon are proposed and demonstrated for the first time with a self-developed complementary-metal-oxide-semiconductor (CMOS)-compatible fabrication processes. For the silicon-based all-optical modulators with a MoTe2-integrated microring resonator and microdisk resonator, the modulation is as efficient as 17.76 and 158.48 pm/mW while the rise/fall time is 3.7/3.6 and 1.5/3.3 μs, respectively. This is the highest tuning efficiency and the fastest response speed among all-optical silicon modulators based on the photothermal effect of two-dimensional materials under the out-of-plane pumping configuration. Furthermore, the manufacturing processes of the present devices are developed from in situ grown MoTe2 nanosheets on a silicon-on-insulator platform, which is CMOS-compatible, stable, and suitable for large-scale photonic integration.

Signal distortion awakened from optical memory estimated using a calculation method with spatiotemporal separation.

The fidelity of photonic storage and retrieval is an essential criterion in long-distance all-optical network nodes. However, the recovered signals from optical memories based on the photon echo (PE) protocol are accompanied by undesired waveform variation and temporal drift. In this study, we use a numerical calculation method with spatiotemporal separation to investigate the essence of signal distortion. The results show that the asynchronous evolution of the macroscopic population difference and the macroscopic dipole moment with time is responsible for echo signal real distortion caused by phase shifts at the in-phase point of the recorded information. The constructive interference of the dipoles at the moment of reaching the in-phase point induces the photon emission, and this point with a nonspecific phase will be naturally accompanied by waveform changes, a small amount of time advance and delay of the PE signal, which is actually a false signal distortion. Such radiation mechanism of the inhomogeneous broadening media provides a perspective to accurately and correctly recognize the temporal drift and waveform variation of the recovered optical signal.

Computation at the speed of light: metamaterials for all-optical calculations and neural networks

The explosion in the amount of information that is being processed is prompting the need for new computing systems beyond existing electronic computers. Photonic computing is emerging as an attractive alternative due to performing calculations at the speed of light, the change for massive parallelism, and also extremely low energy consumption. We review the physical implementation of basic optical calculations, such as differentiation and integration, using metamaterials, and introduce the realization of all-optical artificial neural networks. We start with concise introductions of the mathematical principles behind such optical computation methods and present the advantages, current problems that need to be overcome, and the potential future directions in the field. We expect that our review will be useful for both novice and experienced researchers in the field of all-optical computing platforms using metamaterials.

Wavelength Assignment Techniques with Converters for All Optical Networks (AONs)

We present an optical routing and wavelength assignment (RWA) technique for the best placement of the wavelength converters. The wavelength converter placement was considered independently at the complete nodes and partial nodes. In comparison to existing approaches, our technique performs better and requires fewer wavelength converters to get the required performance. As much as 7%, the likelihood of blocking is decreased. The study also demonstrates that the top four nodes have the biggest influence over the blocking performance of the network. It is clear that placing the converters in the right location is just as crucial to improving performance as simply adding more converters.

Multiple inputs all-optical logic gates based on nanoring insulator-metal-insulator plasmonic waveguides

In this paper, we report new nanoscale plasmonic multiple inputs logic gates based on insulator-metal-insulator (IMI) nanoring waveguides. The proposed all-optical gates are numerically analyzed by the finite element method. NOT, AND, NAND, NOR, and EX-NOR all-optical logic gates were suitably designed and investigated based on the linear interface between the propagated waves through the waveguides. The operation wavelength was 1550 nm. The simulation results show that the optical transmission threshold of (0.26) which performs the operation of planned logic gates is accomplished. Moreover, simulation results show that our compact structure of all-optical logic gates may have potential applications in all-optical integrated networks.

All-optical information conversion in Rb vapor based on the spatial cross-phase modulation.

All-optical information conversion, conveying optical signals without electro-optical transformation, plays a vital role in the all-optical devices and optical communication. We achieve the all-optical information conversion in Rb vapor by utilizing the spatial cross-phase modulation. The refractive index of atomic medium is spatially modulated by the strong switch laser beam, which makes it as a nonlinear focusing lens for the weak signal laser beam. As a result, the far-field diffraction ring patterns of the signal laser beam interacted with atoms can effectively carry the nonlinear phase shift information of the switch laser beam. The channel numbers, channel capacities and channel storage densities of information transmission from switch laser beam to signal laser beam are investigated in the terms of switch laser intensity and vapor temperature. Finally, a special "sxu" alphabetic string, encoded by ASCII code, is introduced to verify this all-optical information conversion scheme. This work paves the way for studying optical information processing and all-optical networking with atomic ensembles.

Programmable low-power consumption all-optical nonlinear activation functions using a micro-ring resonator with phase-change materials.

A programmable hardware implementation of all-optical nonlinear activation functions for different scenarios and applications in all-optical neural networks is essential. We demonstrate a programmable, low-loss all-optical activation function device based on a silicon micro-ring resonator loaded with phase change materials. Four different nonlinear activation functions of Relu, ELU, Softplus and radial basis functions are implemented for incident signal light of the same wavelength. The maximum power consumption required to switch between the four different nonlinear activation functions in calculation is only 1.748 nJ. The simulation of classification of hand-written digit images also shows that they can perform well as alternative nonlinear activation functions. The device we design can serve as nonlinear units in photonic neural networks, while its nonlinear transfer function can be flexibly programmed to optimize the performance of different neuromorphic tasks.