An optical threshold function based on polarization rotation in a single semiconductor optical amplifier

Optical threshold functions are a basic building block for alloptical signal processing, and this paper investigates a threshold function design reliant on a single active element. An optical threshold function based on nonlinear polarization rotation in a single semiconductor optical amplifier is proposed. It functions due to an induced modification of the birefringence of a semiconductor optical amplifier caused by an externally injected optical control signal. It is shown that switching from both the TE to the TM mode and vice versa is possible. The measured results are supported by simulation results based on the SOA rate equations. ©2007 Optical Society of Americ

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

High performance photonic reservoir computer based on a coherently driven passive cavity

Reservoir computing is a recent bio-inspired approach for processing time-dependent signals. It has enabled a breakthrough in analog information processing, with several experiments, both electronic and optical, demonstrating state-of-the-art performances for hard tasks such as speech recognition, time series prediction and nonlinear channel equalization. A proof-of-principle experiment using a linear optical circuit on a photonic chip to process digital signals was recently reported. Here we present a photonic implementation of a reservoir computer based on a coherently driven passive fiber cavity processing analog signals. Our experiment has error rate as low or lower than previous experiments on a wide variety of tasks, and also has lower power consumption. Furthermore, the analytical model describing our experiment is also of interest, as it constitutes a very simple high performance reservoir computer algorithm. The present experiment, given its good performances, low energy consumption and conceptual simplicity, confirms the great potential of photonic reservoir computing for information processing applications ranging from artificial intelligence to telecommunicationsComment: non

A programmable photonic memory

The significant advancements in integrated photonics have enabled high-speed and energy efficient systems for various applications from data communications and high-performance computing, to medical diagnosis, sensing and ranging. However, data storage in these systems has been dominated by electronic memories which necessitates signal conversion between optical and electrical as well as analog and digital domains, and data movement between processor and memory that reduce the speed and energy efficiency. To date, a scalable optical memory with optical control has remained an open problem. Here we report an integrated photonic set-reset latch as a fundamental optical static memory unit based on universal optical logic gates. While the proposed memory is compatible with different photonic platforms, its functionality is demonstrated on a programmable silicon photonic chip as a proof of concept. Optical set, reset, and complementary outputs, scalability to a large number of memory units via the independent latch supply light, and compatibility with different photonic platforms enable more efficient and lower latency optical processing systems

Fiber Optic Sensors and Fiber Lasers

The optical fiber industry is emerging from the market for selling simple accessories using optical fiber to the new optical-IT convergence sensor market combined with high value-added smart industries such as the bio industry. Among them, fiber optic sensors and fiber lasers are growing faster and more accurately by utilizing fiber optics in various fields such as shipbuilding, construction, energy, military, railway, security, and medical.This Special Issue aims to present novel and innovative applications of sensors and devices based on fiber optic sensors and fiber lasers, and covers a wide range of applications of optical sensors. In this Special Issue, original research articles, as well as reviews, have been published