Optical Computing System Research Articles

Design and realization of XOR, OR, and NAND light logic gates using GaAs heterostructure

This study introduces a novel structure based on a GaAs quantum well for implementing XOR, OR, and NAND light logic gates. This structure has unique advantages compared to classical p-n junction-based devices, including polarity-independent operation, radiation emission, and a more straightforward design. The suggested structure consists of two photoconductive devices and a logic gate device. When 850 nm light from the fiber optic cable illuminates the photoconductive device, the photoconductive device and the pulsed signal source are triggered simultaneously and supply the logic gate device, enabling it to perform the desired logic operation (XOR, OR, and NAND). Notably, at room temperature, the primary emission wavelength of these optical logic gate devices is 812 ± 1 nm. This design offers a compact, efficient, and versatile approach to light logic gates, with potential applications in optical computing and communication systems. Furthermore, the data transfer rate of these optical logic gates has been demonstrated to be comparable to that of electrical circuits currently used in technology.

Dual-Band High-Throughput and High-Contrast All-Optical Topology Logic Gates.

Optical computing offers advantages such as high bandwidth and low loss, playing a crucial role in signal processing, communication, and sensing applications. Traditional optical logic gates, based on nonlinear fibers and optical amplifiers, suffer from poor robustness and large footprints, hindering their on-chip integration. All-optical logic gates based on topological photonic crystals have emerged as a promising approach for developing robust and monolithic optical computing systems. Expanding topological photonic crystal logic gates from a single operating band to dual bands can achieve high throughput, significantly enhancing parallel computing capabilities. This study integrates the topological protection offered by valley photonic crystals with linear interference effects to design and implement seven optical computing logic gates on a silicon substrate. These gates, based on dual-band valley photonic crystal topological protection, include OR, XOR, NOT, NAND, NOR, and AND. The robustness of the implemented OR logic gates was verified in the presence of boundary defects. The results demonstrate that multi-band parallel computing all-optical logic gates can be achieved using topological photonic crystals, and these gates exhibit high robustness. The all-optical logic gates designed in this study hold significant potential for future applications in optical signal processing, optical communication, optical sensing, and other related areas.

Leveraging multiplexed metasurfaces for multi-task learning with all-optical diffractive processors.

Diffractive Neural Networks (DNNs) leverage the power of light to enhance computational performance in machine learning, offering a pathway to high-speed, low-energy, and large-scale neural information processing. However, most existing DNN architectures are optimized for single tasks and thus lack the flexibility required for the simultaneous execution of multiple tasks within a unified artificial intelligence platform. In this work, we utilize the polarization and wavelength degrees of freedom of light to achieve optical multi-task identification using the MNIST, FMNIST, and KMNIST datasets. Employing bilayer cascaded metasurfaces, we construct dual-channel DNNs capable of simultaneously classifying two tasks, using polarization and wavelength multiplexing schemes through a meta-atom library. Numerical evaluations demonstrate performance accuracies comparable to those of individually trained single-channel, single-task DNNs. Extending this approach to three-task parallel recognition reveals an expected performance decline yet maintains satisfactory classification accuracies of greater than 80 % for all tasks. We further introduce a novel end-to-end joint optimization framework to redesign the three-task classifier, demonstrating substantial improvements over the meta-atom library design and offering the potential for future multi-channel DNN designs. Our study could pave the way for the development of ultrathin, high-speed, and high-throughput optical neural computing systems.

All-optical AND, NAND, OR, NOR and NOT logic gates using two nested microrings in a racetrack ring resonator

Boolean logic gates are essential components for optical computing and communication systems. However, most existing methods for realizing them require complex structures, high power consumption, or multiple devices. Here, we propose a simple and compact system that can realize five Boolean logic gates, including AND, NAND, OR, NOR, and NOT, by applying different polarization modes and tuning intensities to the input signals within a SOI resonator system, formed by two nested micro rings in a racetrack ring resonator (TNMIRTR). A formula was derived for the optical transfer function of the system using the delay-line-signal method and the logic gates were simulated using the variational finite-difference time domain (varFDTD) method. The proposed structure operates by combining amplitude and polarization-conversion. TNMIRTR gate has several advantages, such as its micro-scale size, low cost, and ability to realize multiple logic gates within a single layout.

Hyperspectral in-memory computing with optical frequency combs and programmable optical memories

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.

Enhancing Signal Recognition Accuracy in Delay-Based Optical Reservoir Computing: A Comparative Analysis of Training Algorithms

To improve the accuracy of signal recognition in delay-based optical reservoir computing (RC) systems, this paper proposes the use of nonlinear algorithms at the output layer to replace traditional linear algorithms for training and testing datasets and apply them to the identification of frequency-modulated continuous wave (FMCW) LiDAR signals. This marks the inaugural use of the system for the identification of FMCW LiDAR signals. We elaborate on the fundamental principles of a delay-based optical RC system using an optical-injected distributed feedback laser (DFB) laser and discriminate four FMCW LiDAR signals through this setup. In the output layer, three distinct training algorithms—namely linear regression, support vector machine (SVM), and random forest—were employed to train the optical reservoir. Upon analyzing the experimental results, it was found that regardless of the size of the dataset, the recognition accuracy of the two nonlinear training algorithms was superior to that of the linear regression algorithm. Among the two nonlinear algorithms, the Random Forest algorithm had a higher recognition accuracy than SVM when the sample size was relatively small.

The Goldilocks principle of learning unitaries by interlacing fixed operators with programmable phase shifters on a photonic chip

Programmable photonic integrated circuits represent an emerging technology that amalgamates photonics and electronics, paving the way for light-based information processing at high speeds and low power consumption. Programmable photonics provides a flexible platform that can be reconfigured to perform multiple tasks, thereby holding great promise for revolutionizing future optical networks and quantum computing systems. Over the past decade, there has been constant progress in developing several different architectures for realizing programmable photonic circuits that allow for realizing arbitrary discrete unitary operations with light. Here, we systematically investigate a general family of photonic circuits for realizing arbitrary unitaries based on a simple architecture that interlaces a fixed intervening layer with programmable phase shifter layers. We introduce a criterion for the intervening operator that guarantees the universality of this architecture for representing arbitrary N×N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N \ imes N$$\\end{document} unitary operators with N+1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N+1$$\\end{document} phase layers. We explore this criterion for different photonic components, including photonic waveguide lattices and meshes of directional couplers, which allows the identification of several families of photonic components that can serve as the intervening layers in the interlacing architecture. Our findings pave the way for efficiently designing and realizing novel families of programmable photonic integrated circuits for multipurpose analog information processing.

A Novel Performance Enhancement Optical Reservoir Computing System Based on Three-Loop Mutual Coupling Structure

A Novel Performance Enhancement Optical Reservoir Computing System Based on Three-Loop Mutual Coupling Structure

All-optical high-contrast femtosecond switching using nonlinearity from an epsilon-near-zero effect in plasmonic metamaterials.

Metamaterials opened a new realm to control light-matter interactions at sub-wavelength scale by engineering meta-atoms. Recently, the integration of several emerging nonlinear materials with metamaterial structures enables ultra-fast all-optical switching at the nanoscale and thus brings enormous possibilities to realize next-generation optical communication systems. This Letter presents a novel, to the best of our knowledge, design of plasmonic metamaterials for high-contrast femtosecond all-optical switching. We leverage magnetic plasmon (MP) resonance combined with the nonlinear effects of an epsilon-near-zero (ENZ)-material. The proposed design comprises a periodic array of two closely spaced Au-nanogratings deposited on an optically thick Au-substrate to excite MP-resonance. To enable a dynamically tunable resonance, the nanogrooves in meta-atoms are filled with an ENZ-material, cadmium-oxide (CdO). The intraband transition-induced optical nonlinearities in the ENZ-medium are studied using a two-temperature model. The MP-resonance ensures strong light-matter interactions enabling enhancement of the nonlinearities of the proposed structure. We observe that the pump-induced refractive index change in the CdO layer causes a redshift of the MP-resonance dip wavelength in the reflectance spectrum, leading to a high modulation depth of 0.83 at 1.55 µm. With an ultra-fast response time of 776 fs while maintaining a low pump-fluence of 75 µJ/cm2, the proposed metamaterial could help in realizing switches for next-generation optical computation systems.

Research on optical computing system architecture for simple recurrent neural networks

Moore’s Law, relating to the speed and capabilities of computers is becoming less applicable. In this ‘post-Moore’ era, a cross-disciplinary team based in the Constructive Electronics Laboratory, Kyushu University, Japan, is investigating optical computing system infrastructures, with a view to driving computing technology forward in a way that negates the need to comply with Moore’s Law. Associate Professor Satoshi Kawakami is an expert in electric circuits and computer architecture who is part of the team. The team’s expertise covers materials, devices, circuits, architectures and algorithms and is geared towards pioneering new computing technologies in the post-Moore era. Kawakami believes that the continuous improvement of computer systems with higher performance and lower power consumption/energy consumption will be essential to realise a sustainable advanced information society and wants to maximise the advantages of devices and hide their disadvantages at the system level, which will necessitate collaboration with higher system layers. Another important goal is reducing power consumption by improving the efficiency of computers. In one current project, the researchers are exploring optical computing system infrastructure for simple recurrent neural networks. The team is keen to re-examine the ideal state of optical circuits from the perspective of the entire system, including electrical memory and interfaces.

Ultra-compact electro-optic phase modulator based on a lithium niobate topological slow light waveguide.

Electro-optic modulators (EOMs) are essential devices of optical communications and quantum computing systems. In particular, ultra-compact EOMs are necessary for highly integrated photonic chips. Thin film lithium niobate materials are a promising platform for designing highly efficient EOMs. However, EOMs based on conventional waveguide structures are at a millimeter scale and challenging to scale down further, greatly hindering the capability of on-chip integration. Here, we design an EOM based on lithium niobate valley photonic crystal (VPC) structures for the first time. Due to the high effective refractive index introduced by the strong slow light effect, the EOM can achieve an ultra-compact size of 4 μm×14 μm with a half-wave voltage of 1.4 V. The EOM has a high transmittance of 0.87 in the 1068 nm because of the unique spin-valley locking effect in VPC structures. The design is fully compatible with current nanofabrication technology and immune to fabrication defects. Therefore, it opens a new possibility in designing lithium niobate electro-optic modulators and will find broad applications in optical communication and quantum photonic devices.

All-optical complex-valued convolution based on four-wave mixing

Optical complex-valued convolution can extract the feature of complex-valued data by processing both amplitude and phase information, enabling a wide range of future applications in artificial intelligence and high-speed optical computation. However, because optical signals at different wavelengths cannot interfere, optical systems based on wavelength multiplexing usually can only realize real-valued computation. Here, we experimentally demonstrate an all-optical computing scheme using Kerr-based optical four-wave mixing (FWM) that can perform complex-valued convolution of multi-wavelength signals. Specifically, this all-optical complex-valued convolution operation can be implemented based on the coherent superposition of converted light generated by multiple FWM processes. The computational throughput of this scheme can be expanded by increasing the number of optical wavelengths and the signal baud rate. To exemplify the application, we successfully applied this all-optical complex-valued convolution to four different orientations of image edge extraction. Our scheme can provide a basis for wavelength-parallel optical computing systems with the demanded complex-valued computation capability.

High-performance real-world optical computing trained by in situ gradient-based model-free optimization.

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.

Enhancing and confining light in hybrid plasmonic nanowire-integrated V-groove silicon waveguides

In recent years, the field of dielectric-plasmonic photonics has made remarkable strides, leading to the successful development of various technologies. The realization of sophisticated optical circuits on a single platform has become increasingly viable. Here we propose and investigate a hybrid dielectric waveguide integrated with plasmonics. This hybrid optical waveguide comprises a copper nanowire situated in close proximity to a silicon V-groove channel, separated by a nanoscale gap. This configuration is particularly advantageous, as achieving precise alignment of the nanowire within the V-groove addresses a fundamental challenge in engineering a fully functional integrated component. Additionally, a silicon nitride film coats the V-groove. Utilizing finite element analysis, we conduct numerical simulations to analyze field properties and modal propagation at a specific wavelength of 1550 nm. Our simulations reveal that meticulous optimization of the nanowire and V-groove channel’s geometrical parameters enables effective tailoring of the hybrid mode. This optimization results in strong mode coupling between the dielectric waveguide mode and the surface plasmon, leading to substantial field enhancement, confinement, and extended propagation length. These waveguides also hold promise for sensing applications, facilitating the detection of sample variations and locations due to pronounced mode characteristics. The proposed hybrid approach demonstrates potential for integration into high-level photonic circuits and on-chip optical computing systems.

Multiplex technique of data transmission in residual class systems

The research object: data transmission in optical communication lines. The subject of research is algorithms for the construction of digital data and methods of their transmission over buses in optical computer systems and in backbone fiber-optic systems. The problem to be solved is the need to devise new methods that ensure increased reliability and cryptographic stability of optical transmission systems. To solve the task, the issue of expanding the theory of timer signal structures and the system of residual classes for the organization of multifactorial multiplex data transmission through the channels of modern information transmission systems was investigated. The factor space is defined (as an example) for fiber optic transmission systems where different multiplexing options are used or may be used. The possibility of adapting algorithms for the construction of digital signal structures for their further transmission in the system of residual classes by various methods of multiplexing has been substantiated. The main principles of transmission were considered: the principle of independence of multiplexing the transmission of residues on each module and the principle of logical dependence and physical independence of the system of channels for transmission of residue values for a specific module of a specific system of residue classes. The basic principle is that at each specific point in time in the multivariate binary space, only one of the possible values of each factor can be equal to unity. A comparison with existing transmission systems shows that the proposed technique could provide data transmission at a speed of up to 16 Tbit/s in a transmission bandwidth of 200 THz. At the same time, the capacity of the alphabet of transmitted characters will be 39468 different characters. It also provides a significant increase in the reliability of the entire transmission system

A neural networks approach for designing compact all-optical photonic crystal based AND logic gate

Abstract This paper introduces a new method for creating an all-optical AND gate by utilizing a two-dimensional photonic crystal configuration for the first time. This gate design is intended for applications in optical computing and all-optical logic, offering the potential for rapid computation and parallel processing. The described gate is characterized by its compact dimensions and comprises two inputs and a single output. The high and low logic states are defined based on power values, where logic 0 corresponds to low power and logic 1 corresponds to high power emitted from the light source. To enhance the design process, artificial neural networks (ANNs) are utilized. ANNs offer a powerful tool for optimizing and fine-tuning the photonic crystal structure parameters to achieve the desired logic functionality. With the help of the applied ANNs, the design process is eased and high performance is achieved for the proposed photonic crystal structure. By integrating ANNs into the design process, this research opens up new possibilities for advancing the field of photonic logic circuits. Combining photonic crystals and ANN optimization provides a powerful approach to designing complex and efficient optical computing systems. The results show that the obtained power values are high for 1 logic state and low for the 0 logic state, which verifies the AND gate accuracy table. The achieved accurate results verify the validity of the proposed approach for achieving precise and reliable all-optical logic operations.

Programmable optical switching integrated chip for 4-bit binary true/inverse/complement code conversions based on fluorinated photopolymers.

In this work, programmable optical switching integrated chips for 4-bit binary true/inverse/complement optical code conversions (OCCs) are proposed based on fluorinated photopolymers. Fluorinated bis-phenol-A novolac resin (FAR) with low absorption loss and fluorinated polyacrylate (FPA) with high thermal stability are self-synthesized as core and cladding layer, respectively. The basic architecture of operating unit for the photonic chip designed is composed of directional coupler Mach-Zehnder interferometer (DC-MZI) thermo-optic (TO) switching, X-junction, and Y-bunching waveguide structures. The waveguide module by cascading 16 operating units could realize OCCs function through optical transmission matrix. The response time of the 4-bit binary OCCs is measured as about 300 µs. The insertion loss and extinction ratio of the actual chip are obtained as about 10.5 dB and 15.2 dB, respectively. The electric driving power consumption for OCCs is less than 6 mW. The true/inverse/complement OCCs are achieved by the programmable modulation circuit. The proposed technique is suitable for achieving optical digital computing system with high-speed signal processing and low power consumption.

Photonic Crystal Flip-Flops: Recent Developments in All Optical Memory Components.

This paper reviews recent advancements in all-optical memory components, particularly focusing on various types of all-optical flip-flops (FFs) based on photonic crystal (PC) structures proposed in recent years. PCs, with their unique optical properties and engineered structures, including photonic bandgap control, enhanced light-matter interaction, and compact size, make them especially suitable for optical FFs. The study explores three key materials, silicon, chalcogenide glass, and gallium arsenide, known for their high refractive index contrast, compact size, hybrid integration capability, and easy fabrication processes. Furthermore, these materials exhibit excellent compatibility with different technologies like CMOS and fiber optics, enhancing their versatility in various applications. The structures proposed in the research leverage mechanisms such as waveguides, ring resonators, scattering rods, coupling rods, edge rods, switches, resonant cavities, and multi-mode interference. The paper delves into crucial properties and parameters of all-optical FFs, including response time, contrast ratio, and operating wavelength. Optical FFs possess significant advantages, such as high speed, low power consumption, and potential for integration, making them a promising technology for advancing optical computing and optical memory systems.

Image transformation based on optical reservoir computing for image security

Visually secure image encryption method can provide an added dimension of protection for image compared to conventional image encryption scheme that encrypts a plain image to a noise-like cipher image. Here we introduce a novel perspective based on image transformation to perform visually secure image encryption by constructing an optical reservoir computing (ORC) system. In this paper, we first design a novel optical dynamics model to construct a dual-channel signal injection ORC system, and to provide chaotic keys when the optical dynamics model operating at chaotic states. Then, the established ORC system is used to reveal the underlying transformation relationship between a plain image and a visually meaningful image both with the same size. One important role of such relationship is to provide evidence for the determination of cipher image that allows visually meaningful image to be directly set as cipher image, prompting a high visual security of that, and another role is to facilitate the calculation of necessary decryption keys in order to obtain a high-quality decrypted image. Compared with other existing encryption methods, this work overcomes a challenge of the inability in simultaneously achieving low burden cost, high visual security of cipher image, and high quality of decrypted image, and the simulation results and performance analysis demonstrate that the proposed scheme is effective and secure. Moreover, this study also offers an inspirational implication to develop novel alternative framework with respect to the study of visually secure image encryption and will help other researchers to develop new image security techniques based on transformation methods.

Parallel photonic acceleration processor for matrix-matrix multiplication.

We propose and experimentally demonstrate a highly parallel photonic acceleration processor based on a wavelength division multiplexing (WDM) system and a non-coherent Mach-Zehnder interferometer (MZI) array for matrix-matrix multiplication. The dimensional expansion is achieved by WDM devices, which play a crucial role in realizing matrix-matrix multiplication together with the broadband characteristics of an MZI. We implemented a 2 × 2 arbitrary nonnegative valued matrix using a reconfigurable 8 × 8 MZI array structure. Through experimentation, we verified that this structure could achieve 90.5% inference accuracy in a classification task for the Modified National Institute of Standards and Technology (MNIST) handwritten dataset. This provides a new effective solution for large-scale integrated optical computing systems based on convolution acceleration processors.