OPALS: Optical Parallel Array Logic System

An optical computer is a computer that uses light instead of electricity (i.e. photons rather than electrons) to manipulate. store and transmit data. Most research projects focus on replacing current computer components with optical equivalents, resulting in an optical digital computer system processing binary data. A study of using a physical medium for computation‐solitons is presented here. As particle like nonlinear waves, solitons carry and exchange information, thus acting as computing agents. Oprical solitons have pulse widths measured in pico seconds or femtoseconds. The general idea of implementing logic usng using solitons is investigated.

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.

Photon is established as a strong and promising candidate in all-optical signal processing and superfast computing. Again, it is found that photon has a successful approach to be used as a quantum mechanical particle. For this reason, it is used as a carrier of information in optical systems instead of electrons in electronic systems. All-optical quantum systems can provide very high speed, secured and noise free communication and computation. Over the last few decades, several all-optical logic processors, algebraic processors, logic gates and their integrated systems are developed by many researchers. All-optical quantum logic gates with qubit data are the basic building block of an optical quantum computer. In quantum computation, qubits or quantum bits are the quantum analogue of classical boolean bits and are used to store information. Several popular encoding techniques (like phase encoding, frequency encoding, intensity encoding, polarization encoding etc.) are used to develop the qubits. Here, in this paper, the gate matrix for quantum optical tristate CNOT logic gate is developed for the first time with a frequency encoding technique. Quantum optical CNOT gate is one of the most important logic gates in the logic family that is extensively used for performing information processing task in optical systems. Tristate-based optical logic operations are proved to increase the data handling capacity. Also, the speed of different types of optical logic operations can be increased with tristate-based optical logic systems. Introduction of contradiction state is another advantage of tristate logic systems. Frequency encoding principle is the best way to encode tristate, quaternary state and multivalued logic states. In the article, the authors have developed a proper truth table and gate matrix of frequency encoded tristate CNOT gate. The all-optical scheme for tristate CNOT gate can be implemented with semiconductor optical amplifier (SOA)-based optical switches. At the same time advantages of quantum optical operations are exploited.KeywordsSemiconductor optical amplifierTristate logicFrequency encoding principle

Non-uniform image deblurring using an optical computing system

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.

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 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 and Optoelectronic Computing

We propose a technique to implement optical digital discrete correlation based on pupil control method with coherent illumination. The technique arranges the location of pixels of an input image of a digital optical computing system. Effectiveness and capability of the technique for the correlation are numerically verified.

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.

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.

Optical computing has gained much attention due to its high speed, low energy consumption, and the fact that it is naturally parallelizable and multiplexable, etc. Single-bit optical logic gates based on a four-hidden-layer diffractive optical neural network (DONN) have been demonstrated with paired apertures. Here, we show a parallel-logic operation strategy based on two-hidden-layer DONN, showcasing their efficiency by multiple-bit (up to 16-bit) optical logic (e.g., NAND) operations. In addition, we demonstrate how NAND-DONN units can be utilized to achieve NOR and AND operations by flipping and cascading the DONN.

The algorithm for reconstructing a 2D input signal (the image measured) by a finite realization of a 2D signal at the digital optical recording system output with the use of the fast Fourier transform is analyzed. The image reconstruction error is estimated.

An all optical approach for developing a system involving Kerr material based switches for cryptographic encoding of binary data depending on the input parity