Physics and Applications of Laser Diode Chaos

An overview of chaos in laser diodes is provided which surveys experimental achievements in the area and explains the theory behind the phenomenon. The fundamental physics underpinning this behaviour and also the opportunities for harnessing laser diode chaos for potential applications are discussed. The availability and ease of operation of laser diodes, in a wide range of configurations, make them a convenient test-bed for exploring basic aspects of nonlinear and chaotic dynamics. It also makes them attractive for practical tasks, such as chaos-based secure communications and random number generation. Avenues for future research and development of chaotic laser diodes are also identified.Comment: Published in Nature Photonic

Optical multi-stable operations of coupled lasers

Optical memories are optical bi(multi-)stable systems whose states can be switched all optically. Acting as a fundamental building block for digital optical signal processing, they have received considerable attention. Many types of optical memories have been explored, which all have in common that they are optical storage elements with two states. Multi-stable optical logic building blocks are interesting for applications in telecommunication systems, since they have potential to process a large number of wavelength channels in parallel. In this thesis, we present two types of multi-stable operation of coupled lasers. The first one is based on coupled ring lasers, which share a single active element and a feedback arm. A single ring laser with feedback can be regarded as an oscillator, since the intensity of the lasing light in the lasing cavity is periodically oscillating. When two such oscillators are coupled together, sharing the same active element and the same feedback arm, they synchronize in a common oscillation frequency if their individual oscillation periodicities are close to each other; otherwise they show bistability between the two oscillators. Switching between different stable states can be realized by injecting external light, in this sense, the system act as an optical memory. Moreover, this concept can easily realize multi-state operation, since only one active element is required. An eight-state optical memory is demonstrated. The second type of multi-stable operation of coupled lasers is based on serially interconnected lasers using the principle of gain quenching. The light from the dominant laser suppresses its neighboring lasers through gain saturation, but still receives amplification by the active element of the suppressed lasers, compensating for coupling losses. This light passes through each of the successive lasers, simultaneously suppressing and being amplified. By this mechanism all the other lasers are suppressed. Only one of the lasers can lase at a time, thus the state of the optical memory is determined by the wavelength of the dominant laser, as same as the first type. A five-state optical memory based on this concept is experimentally demonstrated. Moreover, we use the optical memories as a fundamental logic unit to realize sophisticated optical logic. We present an optical shift register that consists of two serially connected optical memories driven by common clock pulses. The concept is demonstrated at an operation speed of 20 kHz, which is limited by the laser cavities implemented by 10 meter long fiber pigtailed components. Furthermore, we cascade the optical shift register and an optical XOR gate to realize an optical pseudorandom number generator based on optical memories

Optical Communication

Optical communication is very much useful in telecommunication systems, data processing and networking. It consists of a transmitter that encodes a message into an optical signal, a channel that carries the signal to its desired destination, and a receiver that reproduces the message from the received optical signal. It presents up to date results on communication systems, along with the explanations of their relevance, from leading researchers in this field. The chapters cover general concepts of optical communication, components, systems, networks, signal processing and MIMO systems. In recent years, optical components and other enhanced signal processing functions are also considered in depth for optical communications systems. The researcher has also concentrated on optical devices, networking, signal processing, and MIMO systems and other enhanced functions for optical communication. This book is targeted at research, development and design engineers from the teams in manufacturing industry, academia and telecommunication industries

Principles of Neuromorphic Photonics

In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure

Roadmap on all-optical processing

The ability to process optical signals without passing into the electrical domain has always attracted the attention of the research community. Processing photons by photons unfolds new scenarios, in principle allowing for unseen signal processing and computing capabilities. Optical computation can be seen as a large scientific field in which researchers operate, trying to find solutions to their specific needs by different approaches; although the challenges can be substantially different, they are typically addressed using knowledge and technological platforms that are shared across the whole field. This significant know-how can also benefit other scientific communities, providing lateral solutions to their problems, as well as leading to novel applications. The aim of this Roadmap is to provide a broad view of the state-of-the-art in this lively scientific research field and to discuss the advances required to tackle emerging challenges, thanks to contributions authored by experts affiliated to both academic institutions and high-tech industries. The Roadmap is organized so as to put side by side contributions on different aspects of optical processing, aiming to enhance the cross-contamination of ideas between scientists working in three different fields of photonics: optical gates and logical units, high bit-rate signal processing and optical quantum computing. The ultimate intent of this paper is to provide guidance for young scientists as well as providing research-funding institutions and stake holders with a comprehensive overview of perspectives and opportunities offered by this research field

Optoelectronic Reservoir Computing

Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input layer and a single output layer. Within these constraints many implementations are possible. Here we report an opto-electronic implementation of reservoir computing based on a recently proposed architecture consisting of a single non linear node and a delay line. Our implementation is sufficiently fast for real time information processing. We illustrate its performance on tasks of practical importance such as nonlinear channel equalization and speech recognition, and obtain results comparable to state of the art digital implementations.Comment: Contains main paper and two Supplementary Material