Neural networks within multi-core optic fibers

Design of All-Optical D Flip Flop Memory Unit Based on Photonic Crystal

This paper proposes a unique configuration for an all-optical D Flip Flop (D-FF) utilizing a quasi-square ring resonator (RR) and T-Splitter, as well as NOT and OR logic gates within a 2-dimensional square lattice photonic crystal (PC) structure. The components realizing the all-optical D-FF comprise of optical waveguides in a 2D square lattice PC of 45 × 23 silicon (Si) rods in a silica (SiO2) substrate. The utilization of these specific materials has facilitated the fabrication process of the design, diverging from alternative approaches that employ an air substrate, a method inherently unattainable in fabrication. The configuration underwent examination and simulation utilizing both plane-wave expansion (PWE) and finite-difference time-domain (FDTD) methodologies. The simulation outcomes demonstrate that the designed waveguides and RR effectively execute the operational principles of the D-FF by guiding light as intended. The suggested configuration holds promise as a logic block within all-optical arithmetic logic units (ALUs) designed for digital computing optical circuits. The design underwent optimization for operation within the C-band spectrum, particularly at 1550 nm. The outcomes reveal a distinct differentiation between logic states '1' and '0', enhancing robust decision-making on the receiver side and minimizing logic errors in the photonic decision circuit. The D-FF displays a contrast ratio (CR) of 4.77 dB, a stabilization time of 0.66 psec, and a footprint of 21 μm × 12 μm.

Artificial Intelligence and Advanced Materials

Artificial intelligence (AI) is gaining strength, and materials science can both contribute to and profit from it. In a simultaneous progress race, new materials, systems, and processes can be devised and optimized thanks to machine learning (ML) techniques, and such progress can be turned into innovative computing platforms. Future materials scientists will profit from understanding how ML can boost the conception of advanced materials. This review covers aspects of computation from the fundamentals to directions taken and repercussions produced by computation to account for the origins, procedures, and applications of AI. ML and its methods are reviewed to provide basic knowledge of its implementation and its potential. The materials and systems used to implement AI with electric charges are finding serious competition from other information-carrying and processing agents. The impact these techniques have on the inception of new advanced materials is so deep that a new paradigm is developing where implicit knowledge is being mined to conceive materials and systems for functions instead of finding applications to found materials. How far this trend can be carried is hard to fathom, as exemplified by the power to discover unheard of materials or physical laws buried in data.

Photonic unsupervised learning variational autoencoder for high-throughput and low-latency image transmission

Following the explosive growth of global data, there is an ever-increasing demand for high-throughput processing in image transmission systems. However, existing methods mainly rely on electronic circuits, which severely limits the transmission throughput. Here, we propose an end-to-end all-optical variational autoencoder, named photonic encoder-decoder (PED), which maps the physical system of image transmission into an optical generative neural network. By modeling the transmission noises as the variation in optical latent space, the PED establishes a large-scale high-throughput unsupervised optical computing framework that integrates main computations in image transmission, including compression, encryption, and error correction to the optical domain. It reduces the system latency of computation by more than four orders of magnitude compared with the state-of-the-art devices and transmission error ratio by 57% than on-off keying. Our work points to the direction for a wide range of artificial intelligence-based physical system designs and next-generation communications.

Learning to image and compute with multimode optical fibers

Multimode fibers (MMF) were initially developed to transmit digital information encoded in the time domain. There were few attempts in the late 60s and 70s to transmit analog images through MMF. With the availability of digital spatial modulators, practical image transfer through MMFs has the potential to revolutionize medical endoscopy. Because of the fiber's ability to transmit multiple spatial modes of light simultaneously, MMFs could, in principle, replace the millimeters-thick bundles of fibers currently used in endoscopes with a single fiber, only a few hundred microns thick. That, in turn, could potentially open up new, less invasive forms of endoscopy to perform high-resolution imaging of tissues out of reach of current conventional endoscopes. Taking endoscopy by its general meaning as looking into, we review in this paper novel ways of imaging and transmitting images using a machine learning approach. Additionally, we review recent work on using MMF to perform machine learning tasks. The advantages and disadvantages of using machine learning instead of conventional methods is also discussed. Methods of imaging in scattering media and particularly MMFs involves measuring the phase and amplitude of the electromagnetic wave, coming out of the MMF and using these measurements to infer the relationship between the input and the output of the MMF. Most notable techniques include analog phase conjugation [A. Yariv, "On transmission and recovery of three-dimensional image information in optical waveguides," J. Opt. Soc. Am., vol. 66, no. 4, pp. 301-306, 1976; A. Gover, C. Lee, and A. Yariv, "Direct transmission of pictorial information in multimode optical fibers," J. Opt. Soc. Am., vol. 66, no. 4, pp. 306-311, 1976; G. J. Dunning and R. Lind, "Demonstration of image transmission through fibers by optical phase conjugation," Opt. Lett., vol. 7, no. 11, pp. 558-560, 1982; A. Friesem, U. Levy, and Y. Silberberg, "Parallel transmission of images through single optical fibers," Proc. IEEE, vol. 71, no. 2, pp. 208-221, 1983], digital phase conjugation [I. N. Papadopoulos, S. Farahi, C. Moser, and D. Psaltis, "Focusing and scanning light through a multimode optical fiber using digital phase conjugation," Opt. Express, vol. 20, no. 10, pp. 10583-10590, 2012; I. N. Papadopoulos, S. Farahi, C. Moser, and D. Psaltis, "High-resolution, lensless endoscope based on digital scanning through a multimode optical fiber," Biomed. Opt. Express, vol. 4, no. 2, pp. 260-270, 2013] or the full-wave holographic transmission matrix method. The latter technique, which is the current gold standard, measures both the amplitude and phase of the output patterns corresponding to multiple input patterns to construct a matrix of complex numbers relaying the input to the output [Y. Choi, et al., "Scanner-free and wide-field endoscopic imaging by using a single multimode optical fiber," Phys. Rev. Lett., vol. 109, no. 20, p. 203901, 2012; A. M. Caravaca-Aguirre, E. Niv, D. B. Conkey, and R. Piestun, "Real-time resilient focusing through a bending multimode fiber," Opt. Express, vol. 21, no. 10, pp. 12881-12887; R. Y. Gu, R. N. Mahalati, and J. M. Kahn, "Design of flexible multi-mode fiber endoscope," Opt. Express, vol. 23, no. 21, pp. 26905-26918, 2015; D. Loterie, S. Farahi, I. Papadopoulos, A. Goy, D. Psaltis, and C. Moser, "Digital confocal microscopy through a multimode fiber," Opt. Express, vol. 23, no. 18, pp. 23845-23858, 2015]. This matrix is then used for imaging of the inputs or projection of desired patterns. Other techniques rely on iteratively optimizing the pixel value of the input image to perform a particular task (such as focusing or displaying an image) [R. Di Leonardo and S. Bianchi, "Hologram transmission through multi-mode optical fibers," Opt. Express, vol. 19, no. 1, pp. 247-254, 2011; T. Čižmár and K. Dholakia, "Shaping the light transmission through a multimode optical fibre: complex transformation analysis and applications in biophotonics," Opt. Express, vol. 19, no. 20, pp. 18871-18884, 2011; T. Čižmár and K. Dholakia, "Exploiting multimode waveguides for pure fibre-based imaging," Nat. Commun., vol. 3, no. 1, pp. 1-9, 2012; S. Bianchi and R. Di Leonardo, "A multi-mode fiber probe for holographic micromanipulation and microscopy," Lab Chip, vol. 12, no. 3, pp. 635-639, 2012; E. R. Andresen, G. Bouwmans, S. Monneret, and H. Rigneault, "Toward endoscopes with no distal optics: video-rate scanning microscopy through a fiber bundle," Opt. Lett., vol. 38, no. 5, pp. 609-611, 2013].

Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm

Artificial intelligence chips (AICs) are the intersection of integrated circuits and artificial intelligence (AI), involving structure design, algorithm analysis, chip fabrication and application scenarios. Due to their excellent ability in data processing, AICs show a long-term industrial prospect in big data services, cloud centers, etc. However, with the conceivable exhaustion of Moore’s Law, the size of traditional electronic AICs (EAICs) is gradually approaching the limit, and an architectural update is highly required. Photonic artificial intelligence chips (PAIC) utilize light beam propagation in the silicon waveguide, contributing to a high parallelism configuration, fast calculation speed and low latency. Due to light manipulation, PAICs perform well in anti-electromagnetic interference and energy conservation. This invited paper summarized the recent research on PAICs. The characteristics of different hardware structures are discussed. The current widely used training algorithm is given and the Photonic Design Automatic (PDA) simulation platform is introduced. In addition, the authors’ related work on PAICs is presented and we believe that PAICs may play a critical role in the deployment of data processing technology.

Multidimensional fiber echo state network analogue

Optical neuoromorphic technologies enable neural network-based signal processing through a specifically designed hardware and may confer advantages in speed and energy. However, the advances of such technologies in bandwidth and/or dimensionality are often limited by the constraints of the underlying material. Optical fiber presents a well-studied low-cost solution with unique advantages for low-loss high-speed signal processing. The fiber echo state network analogue (FESNA), fiber-based neuromorphic processor, has been the first technology suitable for multichannel high bandwidth (including THz) and dual-quadrature signal processing. Here we propose the multidimensional FESNA (MD-FESNA) processing by utilizing multi-mode fiber non-linearity. Thus, the developed MD-FESNA is the first neuromorphic technology which augments all aforementioned advantages of FESNA with multidimensional spatio-temporal processing. We demonstrate the performance and flexibility of the technology on the example of prediction tasks for hyperchaotic systems. These results will pave the way for a high-speed neuromorphic processing of multidimensional tasks, hardware for spatio-temporal neural networks and open new application venues for fiber-based spatio-temporal multiplexing.

Simulating Power Generation from Photovoltaics in the Polish Power System Based on Ground Meteorological Measurements—First Tests Based on Transmission System Operator Data

The Polish power system is undergoing a slow process of transformation from coal to one that is renewables dominated. Although coal will remain a fundamental fuel in the coming years, the recent upsurge in installed capacity of photovoltaic (PV) systems should draw significant attention. Owning to the fact that the Polish Transmission System Operator recently published the PV hourly generation time series in this article, we aim to explore how well those can be modeled based on the meteorological measurements provided by the Institute of Meteorology and Water Management. The hourly time series of PV generation on a country level and irradiation, wind speed, and temperature measurements from 23 meteorological stations covering one month are used as inputs to create an artificial neural network. The analysis indicates that available measurements combined with artificial neural networks can simulate PV generation on a national level with a mean percentage error of 3.2%.

Solving Overlapping Pattern Issues in On-Chip Learning of Bio-Inspired Neuromorphic System with Synaptic Transistors

Recently, bio-inspired neuromorphic systems have been attracting widespread interest thanks to their energy-efficiency compared to conventional von Neumann architecture computing systems. Previously, we reported a silicon synaptic transistor with an asymmetric dual-gate structure for the direct connection between synaptic devices and neuron circuits. In this study, we study a hardware-based spiking neural network for pattern recognition using a binary modified National Institute of Standards and Technology (MNIST) dataset with a device model. A total of three systems were compared with regard to learning methods, and it was confirmed that the feature extraction of each pattern is the most crucial factor to avoiding overlapping pattern issues and obtaining a high pattern classification ability.

Fiber echo state network analogue for high-bandwidth dual-quadrature signal processing

All-optical platforms for recurrent neural networks can offer higher computational speed and energy efficiency. To produce a major advance in comparison with currently available digital signal processing methods, the new system would need to have high bandwidth and operate both signal quadratures (power and phase). Here we propose a fiber echo state network analogue (FESNA) - the first optical technology that provides both high (beyond previous limits) bandwidth and dual-quadrature signal processing. We demonstrate applicability of the designed system for prediction tasks and for the mitigation of distortions in optical communication systems with multilevel dual-quadrature encoded signals.

Quantum Characteristics of a Nanomechanical Resonator Coupled to a Superconducting LC Resonator in Quantum Computing Systems

The mechanical and quantum properties of a nanomechanical resonator can be improved by connecting it to a superconducting resonator in a way that the resonator exhibits new phenomena that are possibly available to novel quantum technologies. The quantum characteristics of a nanomechanical resonator coupled to a superconducting resonator have been investigated on the basis of rigorous quantum solutions of the combined system. The solutions of the Schrödinger equation for the coupled system have been derived using the unitary transformation approach. The analytic formula of the wave functions has been obtained by applying the adiabatic condition for time evolution of the coupling parameter. The behavior of the quantum wave functions has been analyzed for several different values of parameters. The probability densities depicted in the plane of the two resonator coordinates are distorted and rotated due to the coupling between the resonators. In addition, we have shown that there are squeezing effects in the wave packet along one of the two resonator coordinates or along both the two depending on the magnitude of several parameters, such as mass, inductance, and angular frequencies.

Color image identification and reconstruction using artificial neural networks on multimode fiber images: towards an all-optical design

The rapid growth of applications that rely on artificial neural network (ANN) concepts gives rise to a staggering increase in the demand for hardware implementations of neural networks. New types of hardware that can support the requirements of high-speed associative computing while maintaining low power consumption are sought, and optical artificial neural networks fit the task well. Inherently, optical artificial neural networks can be faster, support larger bandwidth, and produce less heat than their electronic counterparts. Here we propose the design of an optical ANN-based imaging system that has the ability to self-study image signals from an incoherent light source in different colors. Our design consists of a combination of a multimode fiber and a multi-core optical fiber realizing a neural network. We show that the signals, transmitted through the multimode fiber, can be used for image identification purposes and can also be reconstructed using ANNs with a low number of nodes. An all-optical solution can then be achieved by realizing these networks with the multi-core optical neural network fiber.

Resource Allocation in the Cognitive Radio Network-Aided Internet of Things for the Cyber-Physical-Social System: An Efficient Jaya Algorithm

Currently, there is a growing demand for the use of communication network bandwidth for the Internet of Things (IoT) within the cyber-physical-social system (CPSS), while needing progressively more powerful technologies for using scarce spectrum resources. Then, cognitive radio networks (CRNs) as one of those important solutions mentioned above, are used to achieve IoT effectively. Generally, dynamic resource allocation plays a crucial role in the design of CRN-aided IoT systems. Aiming at this issue, orthogonal frequency division multiplexing (OFDM) has been identified as one of the successful technologies, which works with a multi-carrier parallel radio transmission strategy. In this article, through the use of swarm intelligence paradigm, a solution approach is accordingly proposed by employing an efficient Jaya algorithm, called PA-Jaya, to deal with the power allocation problem in cognitive OFDM radio networks for IoT. Because of the algorithm-specific parameter-free feature in the proposed PA-Jaya algorithm, a satisfactory computational performance could be achieved in the handling of this problem. For this optimization problem with some constraints, the simulation results show that compared with some popular algorithms, the efficiency of spectrum utilization could be further improved by using PA-Jaya algorithm with faster convergence speed, while maximizing the total transmission rate.