Optical Computing Research Articles

Spatially varying nanophotonic neural networks.

The explosive growth in computation and energy cost of artificial intelligence has spurred interest in alternative computing modalities to conventional electronic processors. Photonic processors, which use photons instead of electrons, promise optical neural networks with ultralow latency and power consumption. However, existing optical neural networks, limited by their designs, have not achieved the recognition accuracy of modern electronic neural networks. In this work, we bridge this gap by embedding parallelized optical computation into flat camera optics that perform neural network computations during capture, before recording on the sensor. We leverage large kernels and propose a spatially varying convolutional network learned through a low-dimensional reparameterization. We instantiate this network inside the camera lens with a nanophotonic array with angle-dependent responses. Combined with a lightweight electronic back-end of about 2K parameters, our reconfigurable nanophotonic neural network achieves 72.76% accuracy on CIFAR-10, surpassing AlexNet (72.64%), and advancing optical neural networks into the deep learning era.

Evidence of the Quantum Optical Nature of High-Harmonic Generation

High-harmonic generation is a light up-conversion process occurring in a strong laser field, leading to coherent bursts of extreme ultrashort broadband radiation [Lewenstein , Phys. Rev. A , 2117 (1994)]. As a new perspective, we propose that ultrafast strong-field electronic or photonic processes such as high-harmonic generation can potentially generate nonclassical states of light well before the decoherence of the system occurs [Gorlach , Nat. Commun. , 4598 (2020); Stammer ., Phys. Rev. Lett. , 123603 (2022)]. This could address fundamental challenges in quantum technology such as scalability, decoherence, or the generation of massively entangled states [Lewenstein , Luca Argenti Michael Chini, 27 (2024)]. Here, we report experimental evidence of the nonclassical nature of the harmonic emission in several semiconductors excited by a femtosecond infrared laser. By investigating single- and double-beam intensity cross-correlation [Loudon, Rep. Prog. Phys. , 913 (1980)], we measure characteristic nonclassical features in the single-photon statistics. We observe two-mode squeezing in the generated harmonic radiation, which depends on the laser intensity that governs the transition from super-Poissonian to Poissonian photon statistics. The measured violation of the Cauchy-Schwarz inequality realizes a direct test of multipartite entanglement in high-harmonic generation [Wasak, Phys. Rev. A , 033616 (2014)]. This result is supported by the theory of multimodal detection and the Hamiltonian from which the effective squeezing modes of the harmonics can be derived [Gonoskov , Phys. Rev. B , 125110 (2024); Christ New J. Phys. , 033027 (2011)]. With this work, we show experimentally that high-harmonic generation is a new quantum bosonic platform that intrinsically produces nonclassical states of light with unique features such as multipartite broadband entanglement or multimode squeezing. The source operates at room temperature, using standard semiconductors and a standard commercial fiber laser, opening up new routes for the quantum industry, such as optical quantum computing, communication, and imaging. Published by the American Physical Society 2024

From Light to Logic: Recent Advances in Optoelectronic Logic Gate

This review delves into the advancements in optoelectronic logic gate (OELG) devices, emphasizing their transformative potential in computational technology through the integration of optical and electronic components. OELGs present significant advantages over traditional electronic logic gates, including enhanced processing speed, bandwidth, and energy efficiency. The evolution of OELG architectures from single‐device, single‐logic systems to more sophisticated multidevice, multilogic, and reconfigurable OELGs is comprehensively explored. Key advancements include the development of materials and device structures enabling multifunctional logic operations and the incorporation of in‐memory functionalities, critical for applications in high‐performance computing and real‐time data processing. This review also addresses the challenges that need to be overcome, such as stability, durability, integration with existing semiconductor technologies, and efficiency. By summarizing current research and proposing future directions, this review aims to guide the ongoing development of next‐generation optoelectronic architectures, poised to redefine the landscape of optical computing, communication, and data processing.

Holographic multiplexing metasurface with twisted diffractive neural network

As the cornerstone of AI generated content, data drives human-machine interaction and is essential for developing sophisticated deep learning agents. Nevertheless, the associated data storage poses a formidable challenge from conventional energy-intensive planar storage, high maintenance cost, and the susceptibility to electromagnetic interference. In this work, we introduce the concept of metasurface disk, meta-disk, to expand the capacity limits of optical holographic storage by leveraging uncorrelated structural twist. We develop a physical twisted neural network to describe the optical behavior of the meta-disk and conduct a comprehensive lateral error analysis, where the meta-disk stores large volumes of information through internal structural multiplexing. Two-layer 640 µm x 640 µm meta-disk is sufficient to store over hundreds of high-fidelity images with SSIM of 0.8. By harnessing advanced three-dimensional (3D) printing technology, optical holographic storage is experimentally demonstrated with Pancharatnam-Berry metasurfaces. Our technology provides essential backing for the next generation of optical storage, display, encryption, and multifunctional optical analog computing.

Diffraction‐Driven Parallel Convolution Processing with Integrated Photonics

AbstractTraditional electronic processors often struggle with bandwidth limitations and high power consumption when executing extensive linear operations for deep learning tasks. Optical computing has emerged as a promising alternative, offering parallel and energy‐efficient computation capabilities. Yet, the development of high‐density optical computing architectures on integrated photonic platforms remains limited, hindered by constraints in neuron scalability and control engineering complexities. Addressing these challenges, this work presents a diffraction‐driven multi‐kernel optical convolution unit (MOCU) that enables on‐chip parallel convolution processing. By utilizing cascaded silica 1D metalines as pre‐trained large‐scale weights and employing spatial multiplexing at the output, MOCU allows simultaneous passive computation of diverse convolutions within a single unit. This architecture facilitates the construction of optical convolutional neural networks (OCNNs), enabling efficient machine vision processing with a streamlined design. To mitigate errors in MOCU‐embedded OCNNs, a lightweight electronic neural network operates concurrently to calibrate systematic deviations via a low‐rank adaptation (LoRA) algorithm, with minimal overhead. The fabricated MOCU chip demonstrates the highest independent 8‐kernel convolutions in parallel, each with a kernel size and occupying just 0.06 . This architecture effectively merges photonic and electronic technologies, offering a scalable design pathway for energy‐efficient, high‐density deep learning hardware.

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

Abstract 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.

Laser beams offer simplified calculations for quantum computing

Well-established optical computing methods can be used to execute vector-matrix algebra for quantum computing.

Integrated convolutional kernel based on two-dimensional photonic crystals.

Optical neural networks (ONNs) exhibit significant potential for accelerating artificial intelligence task processing due to their low latency, high bandwidth, and parallel processing capabilities. Photonic crystals (PhCs) are extensively utilized in integrated optoelectronics because of their unique photonic bandgap properties and precise control of light waves. In this study, we propose an optical reconfigurable convolutional kernel based on PhCs. This kernel can perform convolutional operations on weights by constructing a PhC weight bank. The convolutional kernel demonstrates exceptional performance within the developed optical convolutional neural network framework, successfully realizing various image edge processing tasks. It achieves blind recognition accuracies of 97.81% for the MNIST dataset and 80.31% for the Fashion-MNIST dataset. This study not only demonstrates the feasibility of constructing optical neural networks based on PhCs but to our knowledge, also offers new avenues for the future development of optical computing.

Enhanced Optical Bistability of a Metasurface Based on Asymmetrically Optimized Mirror-Induced Magnetic Anapole States

In the field of modern optical computing and communication, optical bistability plays a crucial role. With a weak third-order nonlinear coefficient, low switch thresholds of optical bistability from Si-based nanophotonic structures remain a challenge. In this work, a metasurface consisting of silicon nanostrip arrays placed on the optically thick silver film is proposed. The light–matter interaction is enhanced by mirror-inducing the magnetic anapole states (MASs) and asymmetrically optimizing its silicon nanostrip. Numerical results show that the average enhancement factor (EF) of an electric field can be greatly enhanced to be 1524.8. Moreover, the optical bistability of the proposed metasurface achieves the thresholds of ION-OFF and IOFF-ON of 8.5 MW/cm2 and 7.1 MW/cm2, respectively, which is the lowest threshold when compared to the previous works based on silicon nanostructures. The angular dependance of the bistability performance is also investigated. This work facilitates the proposed hybrid metasurface in the fields of miniaturized all-optical switches and modulators, which are key components in optical computing and communication.

Two-dimensional non-Abelian Thouless pump

Non-Abelian Thouless pumps are periodically driven systems designed by the non-Abelian holonomy principle, in which quantized transport of degenerate eigenstates emerges, exhibiting noncommutative features such that the outcome depends on the pumping sequence. The study of non-Abelian Thouless pump is currently restricted to 1D systems, while extending it to higher-dimensional systems will not only provide effective means to probe non-Abelian physics in high-dimensional topological systems, but also expand the dimension and type of associated non-Abelian geometric phase matrix for potential applications. Here, we propose the design and experimental realization of 2D non-Abelian Thouless pumps on a photonic chip with 2D photonic waveguide arrays, where degenerate photonic modes are topologically pumped simultaneously along two real-space directions. We reveal the associated non-Abelian group and experimentally demonstrate the non-Abelian feature by measuring the pumping sequence dependent output. The proposed 2D non-Abelian Thouless pump shows promising applications for robust optical interconnections and optical computing.

Tunable Liquid Crystal Twisted‐Planar Fresnel Lens for Vortex Topology Determination

AbstractThe development of straightforward and effective methods to determine the topological charge of phase singular beams is crucial for broadening the applications of optical vortices. Here, a straightforward and elegant method for determining the topological charge of an optical vortex using an innovative switchable achromatic nematic liquid crystal Fresnel lens is proposed. The approach allows for the unambiguous determination of both the absolute value and the sign of the topological charge of a singular beam across a wide range of the visible spectrum, as demonstrated both experimentally and theoretically. The research outcomes are promising for applications in optical information transmission systems, cryptography, and quantum computing.

Nonvolatile reconfigurable polarization rotator at datacom wavelengths based on a Sb2Se3/Si waveguide

Silicon photonics has become a key platform for photonic integrated circuits (PICs) due to its high refractive index and compatibility with complementary metal-oxide-semiconductor manufacturing. However, the inherent birefringence in silicon waveguides requires efficient polarization management. Here, we report a reconfigurable polarization rotator (PR) using a Sb2Se3/Si waveguide operating at datacom wavelengths (1310 nm), providing nonvolatile switching with zero static power consumption. The polarization conversion relies on the interference of hybrid electric-magnetic (EH) modes, which can be reconfigured by changing the Sb2Se3 state between amorphous and crystalline. Our experimental device exhibits a polarization conversion efficiency (PCE) and a polarization extinction ratio (PER) as high as -0.08 dB and 17.65 dB, respectively, in a compact footprint of just 21 µm length. Therefore, the proposed reconfigurable PR offers a compact and energy-efficient solution for polarization management in silicon photonics, with potential applications in data communication networks and emerging applications benefiting from polarization information encodings, such as optical neural networks and quantum computing.

Cascadable optical nonlinear activation function based on Ge-Si.

To augment the capabilities of optical computing, specialized nonlinear devices as optical activation functions are crucial for enhancing the complexity of optical neural networks. However, existing optical nonlinear activation function devices often encounter challenges in preparation, compatibility, and multi-layer cascading. Here, we propose a cascadable optical nonlinear activation function architecture based on Ge-Si structured devices. Leveraging dual-source modulation, this architecture achieves cascading and wavelength switching by compensating for loss. Experimental comparisons with traditional Ge-Si devices validate the cascading capability of the new architecture. We first verified the versatility of this activation function in a MNIST task, and then in a multi-layer optical dense neural network designed for complex gesture recognition classification, the proposed architecture improves accuracy by an average of 23% compared to a linear network and 15% compared to a network with a traditional activation function architecture. With its advantages of cascadability and high compatibility, this work underscores the potential of all-optical activation functions for large-scale optical neural network scaling and complex task handling.

Design and implementation of the dual-center programming platform for ternary optical computer and electronic computer

In order to utilize the advantages of optical computing and promote the application and popularization of Ternary Optical Computer (TOC), this paper proposes a dual-center programming model consisting of electronic processor and optical processor, presents the theory and technologies of the dual-center model in detail, and for the first time explains the SAN ZHI GUANG (SZG) file chain technology, gives the implementation method of the dual-center model. The model tries to effectively manage the resources of optical processor and solve the problem of the distance and network connection mode of using the TOC. Experimental results show that the dual-center model is correct and the implementation method is feasible. It can improve the usability of the TOC and further simplify the TOC programming process, and makes common users apply the TOC and electronic computer to work cooperatively for the same task.

Concurrent Image Differentiation and Integration Processings Enabled By Polarization‐Multiplexed Metasurface

AbstractOptical computing and image processing performed by sensor front‐end metasurfaces is receiving increasing interest because of advantages such as significant reduction of latency time, energy consumption, and system complexity. Despite the rapid progress, concurrent processing, the most important feature of electronic computing, has not yet been well implemented in optical computing. Here, a metasurface‐based optical image processor that can perform optical differentiation and integration tasks simultaneously is proposed. This optical front‐end processor integrates two coherent transfer functions corresponding to differential and integral convolution kernels into a built‐in metasurface by polarization encoding, allowing concurrent processing of multiple all‐optical computational tasks. The simultaneous differentiation and integration operations on images for edge enhancement and denoising are demonstrated at multiple visible wavelengths. This concurrent processing architecture paves a promising pathway toward multifunctional and higher‐speed image processing for machine vision and biomedical imaging and shows the potential to expedite and potentially supplant certain digital neural network algorithms.

All-optical reconfigurable optical neural network chip based on wavelength division multiplexing

Optical computing has become an important way to achieve low power consumption and high computation speed. Optical neural network (ONN) is one of the key branches of optical computing due to its wide range of applications. However, the integrated ONN schemes proposed in previous works have some disadvantages, such as fixed network structure, complex matrix-vector multiplication (MVM) unit, and few all-optical nonlinear activation function (NAF) methods. Moreover, for the most compact MVM schemes based on wavelength division multiplexing (WDM), it is infeasible to employ intrinsic nonlinear effects to implement NAF, which brings frequent O-E-O conversion in ONN chips. Besides, it is also hard to realize a reconfigurable ONN with coherent MVMs, while it is much easier to implement in WDM schemes. We propose for the first time an all-optical silicon-based ONN chip based on WDM by adopting a new adjustment mechanism: optical gradient force (OGF). The proposed scheme is reconfigurable with tunable layers, variable neurons per layer, and adjustable NAF curves. In the task of classification of the MNIST dataset, our chip can realize an accuracy of 85.13% with 4 full-connected layers and only 50 neurons in total. In addition, we analyze the influence of the OGF-based NAF under fabrication errors and propose a calibration method. Compared to the previous works, our scheme has the two-fold advantages of compactness and reconfiguration, and it paves the way for the all-optical ONN based on WDM and opens the path to unblocking the bottleneck of integrated large-dimension ONNs.

Resource‐Saving and High‐Robustness Image Sensing Based on Binary Optical Computing

Resource‐Saving and High‐Robustness Image Sensing Based on Binary Optical Computing

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.

All-optical combinational logical units featuring fifth-order cascade

Modern computational technologies are increasingly encountering significant limitations, driving a shift towards alternative paradigms such as optical computing. In this study, we introduce novel all-optical combinational logic units based on diffractive neural networks (D2NNs), designed to perform high-order logical operations both efficiently and swiftly using only two modulation layers. This innovative design offers increased processing speed, improved energy efficiency, robust environmental stability, and high error tolerance, making it exceptionally well-suited for a broad spectrum of applications in optical computing and communications. By leveraging transfer learning, we successfully developed a fifth-order cascaded combinational logic circuit for practical information transmission system. Furthermore, we reveal a pioneering application of our device in Optical Time Division Multiplexing (OTDM), demonstrating its capability to manage high-speed data transfer seamlessly without the need for electronic conversion. Extensive simulations and experimental validations highlight the potential of our model as a foundational technology for future optical computing architectures, paving the way toward more sustainable and efficient optical data processing platforms.

Coherent all Optical Reservoir Computing for Equalization of Impairments in Coherent Fiber Optic Communication Systems

Coherent all Optical Reservoir Computing for Equalization of Impairments in Coherent Fiber Optic Communication Systems