Optical Computing and Solitons

Optical digital computing, leveraging optical signals for high-speed, efficient, and high-precision discretized digital computations and information processing, is widely applied in artificial intelligence, communication, and network. However, the development of multiplication/division, pivotal components within digital optical computing systems, has been constrained by the absence of effective computational physical dimensions conducive to manipulating optical signals and the stringent requirement for precise operational control. We propose a fixed-base multiplication and division scheme based on orbital angular momentum (OAM) modes using optical diffractive neural networks (ODNNs). Utilizing the OAM mode as the computational physical dimension, and employing ODNN to perform mode-parallel and independent transformations on it for achieving numerical equivalent shifts, it is feasible to realize 2n-fold multiplication and division operations. Using this mechanism, we have constructed a 3-layer ODNN to realize OAM mode fixed-base multiplication and division for n = 1, 2, and 3, with mode purities of the operation outputs reaching 99%. Furthermore, a dynamic switching between multiplication and division operations has been achieved within the same system through rotation of the phase matrix. Our design suggests a feasible pathway for fixed-base optical multiplication and division, and may offer useful insights for future research on optical digital computing architectures.

Optical computing systems provide high-speed and low-energy data processing but face deficiencies in computationally demanding training and simulation-to-reality gaps. We propose a gradient-based model-free optimization (G-MFO) method based on a Monte Carlo gradient estimation algorithm for computationally efficient in situ training of optical computing systems. This approach treats an optical computing system as a black box and back-propagates the loss directly to the optical computing weights' probability distributions, circumventing the need for a computationally heavy and biased system simulation. Our experiments on diffractive optical computing systems show that G-MFO outperforms hybrid training on the MNIST and FMNIST datasets. Furthermore, we demonstrate image-free and high-speed classification of cells from their marker-free phase maps. Our method's model-free and high-performance nature, combined with its low demand for computational resources, paves the way for accelerating the transition of optical computing from laboratory demonstrations to practical, real-world applications.

A novel optical digital computing system called Optical Parallel Array Logic System (OPALS) is presented, which can execute cellular logic1 in parallel. The system is constructed on the basis of the concept of optical array logic. The pure optical version of the OPALS can be realized by using a dynamic optical coding technique and optical correlation by a multi-focus imaging system. Although the OPALS is still a conceptual system, it would be expected to be a prototype of optical digital computing systems in future.

The next generation of advances in computer throughput and capability can be achieved only by algorithmic and architectural innovations and by exploiting parallelism and concurrency. In this context, optical computing offers intrinsic massive parallelism, high speed and low power dissipation. Realization of those advantages requires technologies for optical and optoelectronic devices and materials. Whether optics will be an alternative or a complement to electronics for computing depends to a large extent on the technological advances in optical devices and materials. Architectural considerations for optical computing also depend on novel devices and materials. Optical computing requires light sources, detectors, switches and optical refractive, reflective and diffractive components. These devices should have distinct characteristics and should also be compatible with electronic VLSI technologies. Optoelectronic and photonic technologies are a fast-growing area in the industries related to optical fiber communications, optical disk memories, optoelectronic displays, laser printers, fax machines and photocopiers. There have also been significant advances in particular devices and materials for optical analog, digital, parallel and neural computing. A remarkable technological advance is the application of electronic VLSI fabrication technologies into optics and optoelectronics, resulting in new devices and new technologies such as multiple quantum well effect devices, binary optics, micro optic components, etc. In what follows, we will examine recent developments of the major optical and optoelectronic devices and materials for optical computing.

Non-uniform image deblurring using an optical computing system

The performance / price ratio of digital data processing is steadily increasing, while the performance / price ratio of optical data processing remains nearly constant, although at a favourable level. Given this trend, one might ask: is there still a future for optical data processing? This question cannot be answered in general, since optical data processing is very competent for some jobs, but clumsy or incapable at other jobs. The category of jobs suitable for optics is characterised by features like: high data rate, large storage requirement, moderate accuracy, repetitive program consisting mainly of linear and quadratic operations. Certain statistical computing problems belong into this category. We will present two examples and analyze their data processing efficiencies. The examples are useful in astro-nomy (speckle interferometry) and in biology (motility studies on bacteria).

The development and investigation of novel photonic structures for the differentiation of optical signals, which are considered as promising building blocks for the optoelectronic information processing devices, has attracted considerable attention in recent years. In this tutorial, we discuss the optical computation of the spatial derivatives of the profile of a 2D optical beam impinging on a resonant diffractive structure. As a diffentiator example, we consider a simple resonant metal-dielectric-metal (MDM) structure consisting of an upper metal layer, a dielectric layer, and a metal substrate. Using the multiple wave interference model, we prove that the reflection coefficient of the MDM structure may strictly vanish, which enables obtaining high-quality differentiation. The rigorous numerical simulation results demonstrate the possibility of optical computation of the first derivative of the incident beam profile in the case of oblique incidence, and of the second derivative in the case of normal incidence. The presented results may find application in novel analog optical computing and optical information processing systems.

We propose a simple analytical method for designing arbitrary-order optical differentiators operating in reflection by successively arranging several first-order optical differentiators. The approach is based on the scattering matrix analysis, does not involve numerical optimization and is valid for differentiators of arbitrary configurations. The numerical simulation results demonstrate high-quality differentiation of a picosecond pulse for up to the fourth order. The proposed method can find application in the design of analogue optical computing and optical information processing systems.

The rapid rise of machine learning drives demand for extensive matrix-vector multiplication operations, thereby challenging the capacities of traditional von Neumann computing systems. Researchers explore alternatives, such as in-memory computing architecture, to find energy-efficient solutions. In particular, there is renewed interest in optical computing systems, which could potentially handle matrix-vector multiplication in a more energy-efficient way. Despite promising initial results, developing high-throughput optical computing systems to rival electronic hardware remains a challenge. Here, we propose and demonstrate a hyperspectral in-memory computing architecture, which simultaneously utilizes space and frequency multiplexing, using optical frequency combs and programmable optical memories. Our carefully designed three-dimensional opto-electronic computing system offers remarkable parallelism, programmability, and scalability, overcoming typical limitations of optical computing. We have experimentally demonstrated highly parallel, single-shot multiply-accumulate operations with precision exceeding 4 bits in both matrix-vector and matrix-matrix multiplications, suggesting the system’s potential for a wide variety of deep learning and optimization tasks. Our approach presents a realistic pathway to scale beyond peta operations per second, a major stride towards high-throughput, energy-efficient optical computing.

Optical Computing . Edited by B. S. Wherrett and F. A. P. Tooley. SUSSP Publications, Edinburgh University Physics Department: 1989 Pp. 314. Distributed by the Institute of Physics, £45. Optical Processing and Computing. Edited by H. H. Arsenault, T. Szoplik and B. Macukow. Academic: 1989. Pp. 493 £42.50, $;59.50

The increasing speed of fibre-optic-based telecommunications has focused attention on high-speed optical processing of digital information. Complex optical processing requires a high-density, high-speed, low-power optical memory that can be integrated with planar semiconductor technology for buffering of decisions and telecommunication data. Recently, ring lasers with extremely small size and low operating power have been made, and we demonstrate here a memory element constructed by interconnecting these microscopic lasers. Our device occupies an area of 18 x 40 microm2 on an InP/InGaAsP photonic integrated circuit, and switches within 20 ps with 5.5 fJ optical switching energy. Simulations show that the element has the potential for much smaller dimensions and switching times. Large numbers of such memory elements can be densely integrated and interconnected on a photonic integrated circuit: fast digital optical information processing systems employing large-scale integration should now be viable.

This paper describes how, in spite of occasional performance limitations generally due to process defects, large-area solid-state image sensors can be applied to scan pictorial data. First, it is estimated to what extent the direct imaging of pictorial matter onto a defective area-scanning device for acceptable scanning quality is possible. A novel concept is then proposed in which the optically generated Fourier-transform hologram of a picture rather than the picture itself is imaged onto a defective solid-state photosensitive array, scanned, digitized, filtered, and reconstructed by fast Fourier transform (FFT) techniques on a digital processor. The operating principle is discussed and the effects of scanner defects, digitization, and filtering on the scanning quality are investigated by computer simulation.

The s-SEED as a logic gate has been demonstrated [1] and we are building optical systems using them [2] to implement the architectures proposed by Murdocca [3]. Here we investigate the systems requirements we must meet in order to do this. We show that the s-SEED is a significantly easier optical logic device with which to build optical digital computers, than previously demonstrated devices. We demonstrate two methods of fully utilizing its advantages in the systems we are building. It is also the purpose of this paper to stimulate the design of other devices which will have the advantages of s-SEEDs.

Room temperature excitonic nonlinear absorption in multiple quantum wells (MQW) has great potential for making low-power high-speed optical switching devices. The required ultrathin multilayers have been grown by both molecular beam epitaxy (MBE) and metalorganic chemical vapor deposition (MOCVD). Efficient, narrow-linewidth luminescence and low-threshold laser operation have been achieved in MQW’s with both techniques. However, virtually all of the reported work to date dealing with nonlinear optical properties has employed MBE-grown materials CHEMLA /1/, CHEMLA /2/, MILLER /3/. The implementation of arrays of photonic switches in optical computing and processing systems will require large area devices with uniformity that exceeds the capability of current technology. The potential advantages of MOCVD for large area, multiple wafer growth of these complex structures provides motivation to explore this technique for their fabrication. In this paper, the growth by MOCVD of GaAs/AlGaAs MQW structures that exhibit narrow-line, room temperature excitonic absorption are reported. We further describe the linear and nonlinear optical properties of these structures and discuss the implications of this work for the construction of large area arrays of switches.

The work deals with an influence of aberrations of optical systems that are used in optical information processing systems. A detailed analysis of the influence of aberrations on the image formed using the optical system is carried out. Furthermore, the effect of aberrations of the wave field, which illuminates the object, is also studied. A theoretical analysis is carried out using the third order aberration theory, and several examples are shown for an object of a circular shape.