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Journal articles on the topic 'Photonic computing'

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Published: 15 February 2025

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1

Bocker, Richard P. "Photonic computing." Applied Optics 25, no. 18 (September 15, 1986): 3019. http://dx.doi.org/10.1364/ao.25.003019.

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2

Sun, Haoyang, Qifeng Qiao, Qingze Guan, and Guangya Zhou. "Silicon Photonic Phase Shifters and Their Applications: A Review." Micromachines 13, no. 9 (September 12, 2022): 1509. http://dx.doi.org/10.3390/mi13091509.

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With the development of silicon photonics, dense photonic integrated circuits play a significant role in applications such as light detection and ranging systems, photonic computing accelerators, miniaturized spectrometers, and so on. Recently, extensive research work has been carried out on the phase shifter, which acts as the fundamental building block in the photonic integrated circuit. In this review, we overview different types of silicon photonic phase shifters, including micro-electro-mechanical systems (MEMS), thermo-optics, and free-carrier depletion types, highlighting the MEMS-based ones. The major working principles of these phase shifters are introduced and analyzed. Additionally, the related works are summarized and compared. Moreover, some emerging applications utilizing phase shifters are introduced, such as neuromorphic computing systems, photonic accelerators, multi-purpose processing cores, etc. Finally, a discussion on each kind of phase shifter is given based on the figures of merit.
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Xu, Zhihao, Tiankuang Zhou, Muzhou Ma, ChenChen Deng, Qionghai Dai, and Lu Fang. "Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence." Science 384, no. 6692 (April 12, 2024): 202–9. http://dx.doi.org/10.1126/science.adl1203.

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The pursuit of artificial general intelligence (AGI) continuously demands higher computing performance. Despite the superior processing speed and efficiency of integrated photonic circuits, their capacity and scalability are restricted by unavoidable errors, such that only simple tasks and shallow models are realized. To support modern AGIs, we designed Taichi—large-scale photonic chiplets based on an integrated diffractive-interference hybrid design and a general distributed computing architecture that has millions-of-neurons capability with 160–tera-operations per second per watt (TOPS/W) energy efficiency. Taichi experimentally achieved on-chip 1000-category–level classification (testing at 91.89% accuracy in the 1623-category Omniglot dataset) and high-fidelity artificial intelligence–generated content with up to two orders of magnitude of improvement in efficiency. Taichi paves the way for large-scale photonic computing and advanced tasks, further exploiting the flexibility and potential of photonics for modern AGI.
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Tanida, Jun, and Yusuke Ogura. "Photonic DNA computing." Review of Laser Engineering 33, Supplement (2005): 239–40. http://dx.doi.org/10.2184/lsj.33.239.

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Kutluyarov, Ruslan V., Aida G. Zakoyan, Grigory S. Voronkov, Elizaveta P. Grakhova, and Muhammad A. Butt. "Neuromorphic Photonics Circuits: Contemporary Review." Nanomaterials 13, no. 24 (December 14, 2023): 3139. http://dx.doi.org/10.3390/nano13243139.

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Neuromorphic photonics is a cutting-edge fusion of neuroscience-inspired computing and photonics technology to overcome the constraints of conventional computing architectures. Its significance lies in the potential to transform information processing by mimicking the parallelism and efficiency of the human brain. Using optics and photonics principles, neuromorphic devices can execute intricate computations swiftly and with impressive energy efficiency. This innovation holds promise for advancing artificial intelligence and machine learning while addressing the limitations of traditional silicon-based computing. Neuromorphic photonics could herald a new era of computing that is more potent and draws inspiration from cognitive processes, leading to advancements in robotics, pattern recognition, and advanced data processing. This paper reviews the recent developments in neuromorphic photonic integrated circuits, applications, and current challenges.
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Li, Jiang, Chaoyue Liu, Haitao Chen, Jingshu Guo, Ming Zhang, and Daoxin Dai. "Hybrid silicon photonic devices with two-dimensional materials." Nanophotonics 9, no. 8 (May 14, 2020): 2295–314. http://dx.doi.org/10.1515/nanoph-2020-0093.

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AbstractSilicon photonics is becoming more and more attractive in the applications of optical interconnections, optical computing, and optical sensing. Although various silicon photonic devices have been developed rapidly, it is still not easy to realize active photonic devices and circuits with silicon alone due to the intrinsic limitations of silicon. In recent years, two-dimensional (2D) materials have attracted extensive attentions due to their unique properties in electronics and photonics. 2D materials can be easily transferred onto silicon and thus provide a promising approach for realizing active photonic devices on silicon. In this paper, we give a review on recent progresses towards hybrid silicon photonics devices with 2D materials, including two parts. One is silicon-based photodetectors with 2D materials for the wavelength-bands from ultraviolet (UV) to mid-infrared (MIR). The other is silicon photonic switches/modulators with 2D materials, including high-speed electro-optical modulators, high-efficiency thermal-optical switches and low-threshold all-optical modulators, etc. These hybrid silicon photonic devices with 2D materials devices provide an alternative way for the realization of multifunctional silicon photonic integrated circuits in the future.
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7

Dong, Bowei, Frank Brückerhoff-Plückelmann, Lennart Meyer, Jelle Dijkstra, Ivonne Bente, Daniel Wendland, Akhil Varri, et al. "Partial coherence enhances parallelized photonic computing." Nature 632, no. 8023 (July 31, 2024): 55–62. http://dx.doi.org/10.1038/s41586-024-07590-y.

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AbstractAdvancements in optical coherence control1–5 have unlocked many cutting-edge applications, including long-haul communication, light detection and ranging (LiDAR) and optical coherence tomography6–8. Prevailing wisdom suggests that using more coherent light sources leads to enhanced system performance and device functionalities9–11. Our study introduces a photonic convolutional processing system that takes advantage of partially coherent light to boost computing parallelism without substantially sacrificing accuracy, potentially enabling larger-size photonic tensor cores. The reduction of the degree of coherence optimizes bandwidth use in the photonic convolutional processing system. This breakthrough challenges the traditional belief that coherence is essential or even advantageous in integrated photonic accelerators, thereby enabling the use of light sources with less rigorous feedback control and thermal-management requirements for high-throughput photonic computing. Here we demonstrate such a system in two photonic platforms for computing applications: a photonic tensor core using phase-change-material photonic memories that delivers parallel convolution operations to classify the gaits of ten patients with Parkinson’s disease with 92.2% accuracy (92.7% theoretically) and a silicon photonic tensor core with embedded electro-absorption modulators (EAMs) to facilitate 0.108 tera operations per second (TOPS) convolutional processing for classifying the Modified National Institute of Standards and Technology (MNIST) handwritten digits dataset with 92.4% accuracy (95.0% theoretically).
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8

Chetan, Anjna. "Integration of Photonic Circuits in Electronics for Enhanced Data Processing and Transfer." Journal for Research in Applied Sciences and Biotechnology 1, no. 2 (June 30, 2022): 83–89. http://dx.doi.org/10.55544/jrasb.1.2.9.

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The rapid growth of data-intensive applications, such as artificial intelligence (AI), big data analytics, and cloud computing, has highlighted the limitations of traditional electronic circuits, particularly in terms of data transfer rates, processing power, and energy efficiency. This study explores the integration of photonic circuits with electronic systems as a viable solution to these challenges. By leveraging the speed and efficiency of photons for data transmission, photonic circuits promise substantial improvements over conventional electronic circuits. The research employs a mixed-method approach, combining experimental analysis with a comprehensive literature review. Experimental results demonstrate that hybrid photonic-electronic circuits can achieve up to ten times faster data processing speeds, a 30% reduction in power consumption, increased bandwidth, and reduced latency compared to traditional electronic systems. These advancements address key issues such as resistive losses and heat generation, offering enhanced performance for high-demand applications. However, challenges related to signal conversion and thermal management persist. Future research is needed to refine photonic-electronic integration and explore advanced technologies, including quantum photonics, to further enhance data processing capabilities. Overall, the study highlights the significant potential of photonic circuits to revolutionize data systems, providing a path towards next-generation computing technologies.
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9

Argyris, Apostolos. "Photonic neuromorphic technologies in optical communications." Nanophotonics 11, no. 5 (January 19, 2022): 897–916. http://dx.doi.org/10.1515/nanoph-2021-0578.

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Abstract Machine learning (ML) and neuromorphic computing have been enforcing problem-solving in many applications. Such approaches found fertile ground in optical communications, a technological field that is very demanding in terms of computational speed and complexity. The latest breakthroughs are strongly supported by advanced signal processing, implemented in the digital domain. Algorithms of different levels of complexity aim at improving data recovery, expanding the reach of transmission, validating the integrity of the optical network operation, and monitoring data transfer faults. Lately, the concept of reservoir computing (RC) inspired hardware implementations in photonics that may offer revolutionary solutions in this field. In a brief introduction, I discuss some of the established digital signal processing (DSP) techniques and some new approaches based on ML and neural network (NN) architectures. In the main part, I review the latest neuromorphic computing proposals that specifically apply to photonic hardware and give new perspectives on addressing signal processing in optical communications. I discuss the fundamental topologies in photonic feed-forward and recurrent network implementations. Finally, I review the photonic topologies that were initially tested for channel equalization benchmark tasks, and then in fiber transmission systems, for optical header recognition, data recovery, and modulation format identification.
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Sun, Shuai, Mario Miscuglio, Xiaoxuan Ma, Zhizhen Ma, Chen Shen, Engin Kayraklioglu, Jeffery Anderson, Tarek El Ghazawi, and Volker J. Sorger. "Induced homomorphism: Kirchhoff’s law in photonics." Nanophotonics 10, no. 6 (March 22, 2021): 1711–21. http://dx.doi.org/10.1515/nanoph-2020-0655.

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Abstract When solving, modeling or reasoning about complex problems, it is usually convenient to use the knowledge of a parallel physical system for representing it. This is the case of lumped-circuit abstraction, which can be used for representing mechanical and acoustic systems, thermal and heat-diffusion problems and in general partial differential equations. Integrated photonic platforms hold the prospective to perform signal processing and analog computing inherently, by mapping into hardware specific operations which relies on the wave-nature of their signals, without trusting on logic gates and digital states like electronics. Here, we argue that in absence of a straightforward parallelism a homomorphism can be induced. We introduce a photonic platform capable of mimicking Kirchhoff’s law in photonics and used as node of a finite difference mesh for solving partial differential equation using monochromatic light in the telecommunication wavelength. Our approach experimentally demonstrates an arbitrary set of boundary conditions, generating a one-shot discrete solution of a Laplace partial differential equation, with an accuracy above 95% with respect to commercial solvers. Our photonic engine can provide a route to achieve chip-scale, fast (10 s of ps), and integrable reprogrammable accelerators for the next generation hybrid high-performance computing. Summary A photonic integrated platform which can mimic Kirchhoff’s law in photonics is used for approximately solve partial differential equations noniteratively using light, with high throughput and low-energy levels.
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11

Cartlidge, Edwin. "Photonic Computing for Sale." Optics and Photonics News 34, no. 1 (January 1, 2023): 26. http://dx.doi.org/10.1364/opn.34.1.000026.

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12

Tossoun, Bassem, Chaoran Huang, Angelina Totovic, Mahdi Nikdast, Miltos Moralis-Pegios, and Paolo Bardella. "Guest Editorial: Photonic Computing." Journal of Lightwave Technology 42, no. 22 (November 15, 2024): 7762–64. http://dx.doi.org/10.1109/jlt.2024.3490692.

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13

SUNADA, Satoshi. "Photonic Reservoir Computing: Exploiting Complex Photonics for Information Processing." Review of Laser Engineering 48, no. 5 (2020): 228. http://dx.doi.org/10.2184/lsj.48.5_228.

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14

Marquez, Bicky A., Hugh Morison, Zhimu Guo, Matthew Filipovich, Paul R. Prucnal, and Bhavin J. Shastri. "Graphene-based photonic synapse for multi wavelength neural networks." MRS Advances 5, no. 37-38 (2020): 1909–17. http://dx.doi.org/10.1557/adv.2020.327.

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AbstractA synapse is a junction between two biological neurons, and the strength, or weight of the synapse, determines the communication strength between the neurons. Building a neuromorphic (i.e. neuron isomorphic) computing architecture, inspired by a biological network or brain, requires many engineered synapses. Furthermore, recent investigation in neuromorphic photonics, i.e. neuromorphic architectures on photonics platforms, have garnered much interest to enable high-bandwidth, low-latency, low-energy applications of neural networks in machine learning and neuromorphic computing. We propose a graphene-based synapse model as a core element to enable large-scale photonic neural networks based on on-chip multiwavelength techniques. This device consists of an electro-absorption modulator embedded in a microring resonator. We also introduce an encoding protocol that allows for the representation of synaptic weights on our photonic device with 15.7 bits of resolution using current control hardware. Recent work has suggested that graphene-based modulators could operate in excess of 100 GHz. Combined with our work, such a graphene-based synapse could enable applications for ultrafast and online learning.
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15

Xia, Chengpeng, Yawen Chen, Haibo Zhang, Hao Zhang, Fei Dai, and Jigang Wu. "Efficient neural network accelerators with optical computing and communication." Computer Science and Information Systems, no. 00 (2022): 66. http://dx.doi.org/10.2298/csis220131066x.

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Conventional electronic Artificial Neural Networks (ANNs) accelerators focus on architecture design and numerical computation optimization to improve the training efficiency. However, these approaches have recently encountered bottlenecks in terms of energy efficiency and computing performance, which leads to an increase interest in photonic accelerator. Photonic architectures with low energy consumption, high transmission speed and high bandwidth have been considered as an important role for generation of computing architectures. In this paper, to provide a better understanding of optical technology used in ANN acceleration, we present a comprehensive review for the efficient photonic computing and communication in ANN accelerators. The related photonic devices are investigated in terms of the application in ANNs acceleration, and a classification of existing solutions is proposed that are categorized into optical computing acceleration and optical communication acceleration according to photonic effects and photonic architectures. Moreover, we discuss the challenges for these photonic neural network acceleration approaches to highlight the most promising future research opportunities in this field.
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Taballione, Caterina, Malaquias Correa Anguita, Michiel de Goede, Pim Venderbosch, Ben Kassenberg, Henk Snijders, Narasimhan Kannan, et al. "20-Mode Universal Quantum Photonic Processor." Quantum 7 (August 1, 2023): 1071. http://dx.doi.org/10.22331/q-2023-08-01-1071.

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Integrated photonics is an essential technology for optical quantum computing. Universal, phase-stable, reconfigurable multimode interferometers (quantum photonic processors) enable manipulation of photonic quantum states and are one of the main components of photonic quantum computers in various architectures. In this paper, we report the realization of the largest quantum photonic processor to date. The processor enables arbitrary unitary transformations on its 20 input modes with an amplitude fidelity of FHaar=97.4% and FPerm=99.5% for Haar-random and permutation matrices, respectively, an optical loss of 2.9 dB averaged over all modes, and high-visibility quantum interference with VHOM=98%. The processor is realized in Si3N4 waveguides and is actively cooled by a Peltier element.
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Xu, Bo, Yuhao Huang, Yuetong Fang, Zhongrui Wang, Shaoliang Yu, and Renjing Xu. "Recent Progress of Neuromorphic Computing Based on Silicon Photonics: Electronic–Photonic Co-Design, Device, and Architecture." Photonics 9, no. 10 (September 27, 2022): 698. http://dx.doi.org/10.3390/photonics9100698.

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The rapid development of neural networks has led to tremendous applications in image segmentation, speech recognition, and medical image diagnosis, etc. Among various hardware implementations of neural networks, silicon photonics is considered one of the most promising approaches due to its CMOS compatibility, accessible integration platforms, mature fabrication techniques, and abundant optical components. In addition, neuromorphic computing based on silicon photonics can provide massively parallel processing and high-speed operations with low power consumption, thus enabling further exploration of neural networks. Here, we focused on the development of neuromorphic computing based on silicon photonics, introducing this field from the perspective of electronic–photonic co-design and presenting the architecture and algorithm theory. Finally, we discussed the prospects and challenges of neuromorphic silicon photonics.
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18

Kazanskiy, Nikolay L., Muhammad A. Butt, and Svetlana N. Khonina. "Optical Computing: Status and Perspectives." Nanomaterials 12, no. 13 (June 24, 2022): 2171. http://dx.doi.org/10.3390/nano12132171.

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For many years, optics has been employed in computing, although the major focus has been and remains to be on connecting parts of computers, for communications, or more fundamentally in systems that have some optical function or element (optical pattern recognition, etc.). Optical digital computers are still evolving; however, a variety of components that can eventually lead to true optical computers, such as optical logic gates, optical switches, neural networks, and spatial light modulators have previously been developed and are discussed in this paper. High-performance off-the-shelf computers can accurately simulate and construct more complicated photonic devices and systems. These advancements have developed under unusual circumstances: photonics is an emerging tool for the next generation of computing hardware, while recent advances in digital computers have empowered the design, modeling, and creation of a new class of photonic devices and systems with unparalleled challenges. Thus, the review of the status and perspectives shows that optical technology offers incredible developments in computational efficiency; however, only separately implemented optical operations are known so far, and the launch of the world’s first commercial optical processing system was only recently announced. Most likely, the optical computer has not been put into mass production because there are still no good solutions for optical transistors, optical memory, and much more that acceptance to break the huge inertia of many proven technologies in electronics.
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Cheng, Junwei, Hailong Zhou, and Jianji Dong. "Photonic Matrix Computing: From Fundamentals to Applications." Nanomaterials 11, no. 7 (June 26, 2021): 1683. http://dx.doi.org/10.3390/nano11071683.

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In emerging artificial intelligence applications, massive matrix operations require high computing speed and energy efficiency. Optical computing can realize high-speed parallel information processing with ultra-low energy consumption on photonic integrated platforms or in free space, which can well meet these domain-specific demands. In this review, we firstly introduce the principles of photonic matrix computing implemented by three mainstream schemes, and then review the research progress of optical neural networks (ONNs) based on photonic matrix computing. In addition, we discuss the advantages of optical computing architectures over electronic processors as well as current challenges of optical computing and highlight some promising prospects for the future development.
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Daria, Vincent R. "Holographic photonic neuron." Neuromorphic Computing and Engineering 1, no. 2 (December 1, 2021): 024009. http://dx.doi.org/10.1088/2634-4386/ac3ba5.

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Abstract The promise of artificial intelligence (AI) to process complex datasets has brought about innovative computing paradigms. While recent developments in quantum-photonic computing have reached significant feats, mimicking our brain’s ability to recognize images are poorly integrated in these ventures. Here, I incorporate orbital angular momentum (OAM) states in a classical Vander Lugt optical correlator to create the holographic photonic neuron (HoloPheuron). The HoloPheuron can memorize an array of matched filters in a single phase-hologram, which is derived by linking OAM states with elements in the array. Successful correlation is independent of intensity and yields photons with OAM states of lℏ, which can be used as a transmission protocol or qudits for quantum computing. The unique OAM identifier establishes the HoloPheuron as a fundamental AI device for pattern recognition that can be scaled and integrated with other computing platforms to build-up a robust neuromorphic quantum-photonic processor.
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21

Soref, Richard. "Reconfigurable Integrated Optoelectronics." Advances in OptoElectronics 2011 (May 4, 2011): 1–15. http://dx.doi.org/10.1155/2011/627802.

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Integrated optics today is based upon chips of Si and InP. The future of this chip industry is probably contained in the thrust towards optoelectronic integrated circuits (OEICs) and photonic integrated circuits (PICs) manufactured in a high-volume foundry. We believe that reconfigurable OEICs and PICs, known as ROEICs and RPICs, constitute the ultimate embodiment of integrated photonics. This paper shows that any ROEIC-on-a-chip can be decomposed into photonic modules, some of them fixed and some of them changeable in function. Reconfiguration is provided by electrical control signals to the electro-optical building blocks. We illustrate these modules in detail and discuss 3D ROEIC chips for the highest-performance signal processing. We present examples of our module theory for RPIC optical lattice filters already constructed, and we propose new ROEICs for directed optical logic, large-scale matrix switching, and 2D beamsteering of a phased-array microwave antenna. In general, large-scale-integrated ROEICs will enable significant applications in computing, quantum computing, communications, learning, imaging, telepresence, sensing, RF/microwave photonics, information storage, cryptography, and data mining.
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Garofolo, James, and Ben Wu. "Photonic analog signal processing and neuromorphic computing [Invited]." Chinese Optics Letters 22, no. 3 (2024): 032501. http://dx.doi.org/10.3788/col202422.032501.

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23

Ingty Agnihotri, Pravar, and Ajay Joshi. "PHOTONIC INTEGRATED CIRCUITS: GIANT LEAP IN COMPUTING TECHNOLOGY." International Journal of Advanced Research 12, no. 09 (September 30, 2024): 366–74. http://dx.doi.org/10.21474/ijar01/19464.

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There is a continued demand for increase in computing requirements from sensor, smartphones, to servers. However, the technology improvements in clock speed, power, and memory bandwidth from traditional electronic integrated circuits (EIC) has started to saturate. This has triggered further research in exploration of photon integrated circuits (PIC) that utilize photons (or particles of light) as opposed to electrons to form a functioning circuit. Research suggests that by integrating the benefits of EIC and PIC, one is able to harness the best of both worlds. The inherent advantages of photonics technology such as speed, energy-efficiency, and density, coupled with the advances in material technology, make them suitable to address the challenge of designing heterogeneous chips. This paper presents the fundamentals of the photonic technology, applicability to solving digital computing problems, ability to integrate into PICs, and its capability to complement electronic integrated circuits for powering the computing demands of emerging applications.
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Feng, Chenghao, Zhoufeng Ying, Zheng Zhao, Jiaqi Gu, David Z. Pan, and Ray T. Chen. "Wavelength-division-multiplexing (WDM)-based integrated electronic–photonic switching network (EPSN) for high-speed data processing and transportation." Nanophotonics 9, no. 15 (September 17, 2020): 4579–88. http://dx.doi.org/10.1515/nanoph-2020-0356.

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AbstractIntegrated photonics offers attractive solutions for realizing combinational logic for high-performance computing. The integrated photonic chips can be further optimized using multiplexing techniques such as wavelength-division multiplexing (WDM). In this paper, we propose a WDM-based electronic–photonic switching network (EPSN) to realize the functions of the binary decoder and the multiplexer, which are fundamental elements in microprocessors for data transportation and processing. We experimentally demonstrate its practicality by implementing a 3–8 (three inputs, eight outputs) switching network operating at 20 Gb/s. Detailed performance analysis and performance enhancement techniques are also given in this paper.
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Sunada, Satoshi, and Tomoya Yamaguchi. "Time-domain image processing using photonic reservoir computing." EPJ Web of Conferences 287 (2023): 13007. http://dx.doi.org/10.1051/epjconf/202328713007.

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Photonic computing has attracted much attention due to its great potential to accelerate artificial neural network operations. However, the processing of a large amount of data, such as image data, basically requires large-scale photonic circuits and is still challenging due to its low scalability of the photonic integration. Here, we propose a scalable image processing approach, which uses a temporal degree of freedom of photons. In the proposed approach, the spatial information of a target object is compressively transformed to a time-domain signal using a gigahertz-rate random pattern projection technique. The time-domain signal is optically acquired at a single-input channel and processed with a microcavity-based photonic reservoir computer. We experimentally demonstrate that this photonic approach is capable of image recognition at gigahertz rates.
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Li, Mengkun, and Yongjian Wang. "An Energy-Efficient Silicon Photonic-Assisted Deep Learning Accelerator for Big Data." Wireless Communications and Mobile Computing 2020 (December 16, 2020): 1–11. http://dx.doi.org/10.1155/2020/6661022.

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Deep learning has become the most mainstream technology in artificial intelligence (AI) because it can be comparable to human performance in complex tasks. However, in the era of big data, the ever-increasing data volume and model scale makes deep learning require mighty computing power and acceptable energy costs. For electrical chips, including most deep learning accelerators, transistor performance limitations make it challenging to meet computing’s energy efficiency requirements. Silicon photonic devices are expected to replace transistors and become the mainstream components in computing architecture due to their advantages, such as low energy consumption, large bandwidth, and high speed. Therefore, we propose a silicon photonic-assisted deep learning accelerator for big data. The accelerator uses microring resonators (MRs) to form a photonic multiplication array. It combines photonic-specific wavelength division multiplexing (WDM) technology to achieve multiple parallel calculations of input feature maps and convolution kernels at the speed of light, providing the promise of energy efficiency and calculation speed improvement. The proposed accelerator achieves at least a 75x improvement in computational efficiency compared to the traditional electrical design.
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Nakajima, Mitsumasa, Takuma Tsurugaya, Kenji Tanaka, and Toshikazu Hashimoto. "Photonic Implementation of Reservoir Computing." NTT Technical Review 20, no. 8 (August 2022): 58–63. http://dx.doi.org/10.53829/ntr202208fa8.

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Van der Sande, Guy, Daniel Brunner, and Miguel C. Soriano. "Advances in photonic reservoir computing." Nanophotonics 6, no. 3 (May 12, 2017): 561–76. http://dx.doi.org/10.1515/nanoph-2016-0132.

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AbstractWe review a novel paradigm that has emerged in analogue neuromorphic optical computing. The goal is to implement a reservoir computer in optics, where information is encoded in the intensity and phase of the optical field. Reservoir computing is a bio-inspired approach especially suited for processing time-dependent information. The reservoir’s complex and high-dimensional transient response to the input signal is capable of universal computation. The reservoir does not need to be trained, which makes it very well suited for optics. As such, much of the promise of photonic reservoirs lies in their minimal hardware requirements, a tremendous advantage over other hardware-intensive neural network models. We review the two main approaches to optical reservoir computing: networks implemented with multiple discrete optical nodes and the continuous system of a single nonlinear device coupled to delayed feedback.
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Zhou Zhiping, 周治平, 许鹏飞 Xu Pengfei, and 董晓文 Dong Xiaowen. "Computing on Silicon Photonic Platform." Chinese Journal of Lasers 47, no. 6 (2020): 0600001. http://dx.doi.org/10.3788/cjl202047.0600001.

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Förtsch, Michael, and Tobias M. Wintermantel. "Quantum computing – the photonic approach." PhotonicsViews 19, no. 6 (November 27, 2022): 35–37. http://dx.doi.org/10.1002/phvs.202200051.

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31

Heurtel, Nicolas, Andreas Fyrillas, Grégoire de Gliniasty, Raphaël Le Bihan, Sébastien Malherbe, Marceau Pailhas, Eric Bertasi, et al. "Perceval: A Software Platform for Discrete Variable Photonic Quantum Computing." Quantum 7 (February 21, 2023): 931. http://dx.doi.org/10.22331/q-2023-02-21-931.

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We introduce Perceval, an open-source software platform for simulating and interfacing with discrete-variable photonic quantum computers, and describe its main features and components. Its Python front-end allows photonic circuits to be composed from basic photonic building blocks like photon sources, beam splitters, phase-shifters and detectors. A variety of computational back-ends are available and optimised for different use-cases. These use state-of-the-art simulation techniques covering both weak simulation, or sampling, and strong simulation. We give examples of Perceval in action by reproducing a variety of photonic experiments and simulating photonic implementations of a range of quantum algorithms, from Grover's and Shor's to examples of quantum machine learning. Perceval is intended to be a useful toolkit for experimentalists wishing to easily model, design, simulate, or optimise a discrete-variable photonic experiment, for theoreticians wishing to design algorithms and applications for discrete-variable photonic quantum computing platforms, and for application designers wishing to evaluate algorithms on available state-of-the-art photonic quantum computers.
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Zhang, Jinying, Yulin Si, Yexiaotong Zhang, Bingnan Wang, and Xinye Wang. "Dual-Band High-Throughput and High-Contrast All-Optical Topology Logic Gates." Micromachines 15, no. 12 (December 13, 2024): 1492. https://doi.org/10.3390/mi15121492.

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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.
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Ríos, Carlos, Nathan Youngblood, Zengguang Cheng, Manuel Le Gallo, Wolfram H. P. Pernice, C. David Wright, Abu Sebastian, and Harish Bhaskaran. "In-memory computing on a photonic platform." Science Advances 5, no. 2 (February 2019): eaau5759. http://dx.doi.org/10.1126/sciadv.aau5759.

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Collocated data processing and storage are the norm in biological computing systems such as the mammalian brain. As our ability to create better hardware improves, new computational paradigms are being explored beyond von Neumann architectures. Integrated photonic circuits are an attractive solution for on-chip computing which can leverage the increased speed and bandwidth potential of the optical domain, and importantly, remove the need for electro-optical conversions. Here we show that we can combine integrated optics with collocated data storage and processing to enable all-photonic in-memory computations. By employing nonvolatile photonic elements based on the phase-change material, Ge2Sb2Te5, we achieve direct scalar and matrix-vector multiplication, featuring a novel single-shot Write/Erase and a drift-free process. The output pulse, carrying the information of the light-matter interaction, is the result of the computation. Our all-optical approach is novel, easy to fabricate and operate, and sets the stage for development of entirely photonic computers.
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34

WADE, ANDREW. "NEW CHIP FOR LIGHTSPEED COMPUTING." Engineer 301, no. 7924 (February 2021): 7. http://dx.doi.org/10.12968/s0013-7758(22)90411-8.

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35

Yi, Ailun, Chengli Wang, Liping Zhou, Yifan Zhu, Shibin Zhang, Tiangui You, Jiaxiang Zhang, and Xin Ou. "Silicon carbide for integrated photonics." Applied Physics Reviews 9, no. 3 (September 2022): 031302. http://dx.doi.org/10.1063/5.0079649.

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Photonic integrated circuits (PICs) based on lithographically patterned waveguides provide a scalable approach for manipulating photonic bits, enabling seminal demonstrations of a wide range of photonic technologies with desired complexity and stability. While the next generation of applications such as ultra-high speed optical transceivers, neuromorphic computing and terabit-scale communications demand further lower power consumption and higher operating frequency. Complementing the leading silicon-based material platforms, the third-generation semiconductor, silicon carbide (SiC), offers a significant opportunity toward the advanced development of PICs in terms of its broadest range of functionalities, including wide bandgap, high optical nonlinearities, high refractive index, controllable artificial spin defects and complementary metal oxide semiconductor-compatible fabrication process. The superior properties of SiC have enabled a plethora of nano-photonic explorations, such as waveguides, micro-cavities, nonlinear frequency converters and optically-active spin defects. This remarkable progress has prompted the rapid development of advanced SiC PICs for both classical and quantum applications. Here, we provide an overview of SiC-based integrated photonics, presenting the latest progress on investigating its basic optoelectronic properties, as well as the recent developments in the fabrication of several typical approaches for light confinement structures that form the basic building blocks for low-loss, multi-functional and industry-compatible integrated photonic platform. Moreover, recent works employing SiC as optically-readable spin hosts for quantum information applications are also summarized and highlighted. As a still-developing integrated photonic platform, prospects and challenges of utilizing SiC material platforms in the field of integrated photonics are also discussed.
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NARUSE, Makoto, Atsushi UCHIDA, Kazuharu UCHIYAMA, and Kouichi AKAHANE. "Photonic Computing Highlighting Ultimate Nature of Light: Decision Making by Photonics." IEICE ESS Fundamentals Review 15, no. 4 (April 1, 2022): 310–17. http://dx.doi.org/10.1587/essfr.15.4_310.

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37

Tyler, Neil. "Next Generation Photonic Qubits." New Electronics 54, no. 13 (August 24, 2021): 8. http://dx.doi.org/10.12968/s0047-9624(22)60495-4.

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Zhang, Lulu, Yongzhi Zhang, Furong Liu, Qingyuan Chen, Yangbo Lian, and Quanlong Ma. "On-Chip Photonic Synapses with All-Optical Memory and Neural Network Computation." Micromachines 14, no. 1 (December 27, 2022): 74. http://dx.doi.org/10.3390/mi14010074.

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Inspired by the human brain, neural network computing was expected to break the bottleneck of traditional computing, but the integrated design still faces great challenges. Here, a readily integrated membrane-system photonic synapse was demonstrated. By pre-pulse training at 1064 nm (cutoff wavelength), the photonic synapse can be regulated both excitatory and inhibitory at tunable wavelengths (1200–2000 nm). Furthermore, more weights and memory functions were shown through the photonic synapse integrated network. Additionally, the digital recognition function of the single-layer perceptron neural network constructed by photonic synapses has been successfully demonstrated. Most of the biological synaptic functions were realized by the photonic synaptic network, and it had the advantages of compact structure, scalable, adjustable wavelength, and so on, which opens up a new idea for the study of the neural synaptic network.
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Antonik, Piotr, Serge Massar, and Guy Van Der Sande. "Photonic reservoir computing using delay dynamical systems." Photoniques, no. 104 (September 2020): 45–48. http://dx.doi.org/10.1051/photon/202010445.

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The recent progress in artificial intelligence has spurred renewed interest in hardware implementations of neural networks. Reservoir computing is a powerful, highly versatile machine learning algorithm well suited for experimental implementations. The simplest highperformance architecture is based on delay dynamical systems. We illustrate its power through a series of photonic examples, including the first all optical reservoir computer and reservoir computers based on lasers with delayed feedback. We also show how reservoirs can be used to emulate dynamical systems. We discuss the perspectives of photonic reservoir computing.
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Quack, Niels, Alain Yuji Takabayashi, Hamed Sattari, Pierre Edinger, Gaehun Jo, Simon J. Bleiker, Carlos Errando-Herranz, et al. "Integrated silicon photonic MEMS." Microsystems & Nanoengineering 9, no. 1 (March 20, 2023). http://dx.doi.org/10.1038/s41378-023-00498-z.

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AbstractSilicon photonics has emerged as a mature technology that is expected to play a key role in critical emerging applications, including very high data rate optical communications, distance sensing for autonomous vehicles, photonic-accelerated computing, and quantum information processing. The success of silicon photonics has been enabled by the unique combination of performance, high yield, and high-volume capacity that can only be achieved by standardizing manufacturing technology. Today, standardized silicon photonics technology platforms implemented by foundries provide access to optimized library components, including low-loss optical routing, fast modulation, continuous tuning, high-speed germanium photodiodes, and high-efficiency optical and electrical interfaces. However, silicon’s relatively weak electro-optic effects result in modulators with a significant footprint and thermo-optic tuning devices that require high power consumption, which are substantial impediments for very large-scale integration in silicon photonics. Microelectromechanical systems (MEMS) technology can enhance silicon photonics with building blocks that are compact, low-loss, broadband, fast and require very low power consumption. Here, we introduce a silicon photonic MEMS platform consisting of high-performance nano-opto-electromechanical devices fully integrated alongside standard silicon photonics foundry components, with wafer-level sealing for long-term reliability, flip-chip bonding to redistribution interposers, and fibre-array attachment for high port count optical and electrical interfacing. Our experimental demonstration of fundamental silicon photonic MEMS circuit elements, including power couplers, phase shifters and wavelength-division multiplexing devices using standardized technology lifts previous impediments to enable scaling to very large photonic integrated circuits for applications in telecommunications, neuromorphic computing, sensing, programmable photonics, and quantum computing.
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Xie, Yiwei, Jiachen Wu, Shihan Hong, Cong Wang, Shujun Liu, Huan Li, Xinyan Ju, Xiyuan Ke, Dajian Liu, and Daoxin Dai. "Towards large-scale programmable silicon photonic chip for signal processing." Nanophotonics, February 19, 2024. http://dx.doi.org/10.1515/nanoph-2023-0836.

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Abstract Optical signal processing has been playing a crucial part as powerful engine for various information systems in the practical applications. In particular, achieving large-scale programmable chips for signal processing are highly desirable for high flexibility, low cost and powerful processing. Silicon photonics, which has been developed successfully in the past decade, provides a promising option due to its unique advantages. Here, recent progress of large-scale programmable silicon photonic chip for signal processing in microwave photonics, optical communications, optical computing, quantum photonics as well as dispersion controlling are reviewed. Particularly, we give a discussion about the realization of high-performance building-blocks, including ultra-low-loss silicon photonic waveguides, 2 × 2 Mach–Zehnder switches and microring resonator switches. The methods for configuring large-scale programmable silicon photonic chips are also discussed. The representative examples are summarized for the applications of beam steering, optical switching, optical computing, quantum photonic processing as well as optical dispersion controlling. Finally, we give an outlook for the challenges of further developing large-scale programmable silicon photonic chips.
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42

AbuGhanem, Muhammad. "Information processing at the speed of light." Frontiers of Optoelectronics 17, no. 1 (September 29, 2024). http://dx.doi.org/10.1007/s12200-024-00133-3.

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AbstractIn recent years, quantum computing has made significant strides, particularly in light-based technology. The introduction of quantum photonic chips has ushered in an era marked by scalability, stability, and cost-effectiveness, paving the way for innovative possibilities within compact footprints. This article provides a comprehensive exploration of photonic quantum computing, covering key aspects such as encoding information in photons, the merits of photonic qubits, and essential photonic device components including light squeezers, quantum light sources, interferometers, photodetectors, and waveguides. The article also examines photonic quantum communication and internet, and its implications for secure systems, detailing implementations such as quantum key distribution and long-distance communication. Emerging trends in quantum communication and essential reconfigurable elements for advancing photonic quantum internet are discussed. The review further navigates the path towards establishing scalable and fault-tolerant photonic quantum computers, highlighting quantum computational advantages achieved using photons. Additionally, the discussion extends to programmable photonic circuits, integrated photonics and transformative applications. Lastly, the review addresses prospects, implications, and challenges in photonic quantum computing, offering valuable insights into current advancements and promising future directions in this technology. Graphic abstract
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43

Chen, Wenyu, Shiyuan Liu, and Jinlong Zhu. "Pixelated non-volatile programmable photonic integrated circuits with 20-level intermediate states." International Journal of Extreme Manufacturing, February 22, 2024. http://dx.doi.org/10.1088/2631-7990/ad2c60.

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Abstract Multi-level programmable photonic integrated circuits and optical metasurfaces have gained widespread attention in many fields, such as neuromorphic photonics, optical communications, and quantum information. In this paper, we propose pixelated programmable Si3N4 photonic integrated circuits with record-high 20-level intermediate states at 785 nm wavelength. Such flexibility in phase or amplitude modulation is achieved by a programmable Sb2S3 matrix, the footprint of whose elements can be as small as 1.2 μm, limited only by the optical diffraction limit of an in-house developed pulsed laser writing system. We believe, our work lays the foundation for laser-writing ultra-high-level (20 levels and even more) programmable photonic systems and metasurfaces based on phase change materials, which could catalyze diverse applications such as programmable neuromorphic photonics, biosensing, optical computing, photonic quantum computing, and reconfigurable metasurfaces.
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44

Li, Yandong, Minwoo Jung, Yang Yu, Yuchen Han, Baile Zhang, and Gennady Shvets. "Topological Directional Coupler." Laser & Photonics Reviews, June 14, 2024. http://dx.doi.org/10.1002/lpor.202301313.

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AbstractInterferometers and beam splitters are fundamental building blocks for photonic neuromorphic and quantum computing machinery. In waveguide‐based photonic integrated circuits, beam‐splitting is achieved with directional couplers that rely on transition regions where the waveguides are adiabatically bent to suppress back‐reflection. In this study, a novel, compact approach for introducing guided mode coupling is presented. Along the multimodal domain wall between topological photonic crystals, the photonic spin is conserved to suppress back‐reflection, and the topological protection of the valley degree of freedom is relaxed to implement tunable beam splitting. Rapid advancements in chip‐scale topological photonics suggest that the proposed simultaneous utilization of multiple topological degrees of freedom could benefit the development of novel photonic computing platforms.
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45

Wu, Changming, Heshan Yu, Seokhyeong Lee, Ruoming Peng, Ichiro Takeuchi, and Mo Li. "Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network." Nature Communications 12, no. 1 (January 4, 2021). http://dx.doi.org/10.1038/s41467-020-20365-z.

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AbstractNeuromorphic photonics has recently emerged as a promising hardware accelerator, with significant potential speed and energy advantages over digital electronics for machine learning algorithms, such as neural networks of various types. Integrated photonic networks are particularly powerful in performing analog computing of matrix-vector multiplication (MVM) as they afford unparalleled speed and bandwidth density for data transmission. Incorporating nonvolatile phase-change materials in integrated photonic devices enables indispensable programming and in-memory computing capabilities for on-chip optical computing. Here, we demonstrate a multimode photonic computing core consisting of an array of programable mode converters based on on-waveguide metasurfaces made of phase-change materials. The programmable converters utilize the refractive index change of the phase-change material Ge2Sb2Te5 during phase transition to control the waveguide spatial modes with a very high precision of up to 64 levels in modal contrast. This contrast is used to represent the matrix elements, with 6-bit resolution and both positive and negative values, to perform MVM computation in neural network algorithms. We demonstrate a prototypical optical convolutional neural network that can perform image processing and recognition tasks with high accuracy. With a broad operation bandwidth and a compact device footprint, the demonstrated multimode photonic core is promising toward large-scale photonic neural networks with ultrahigh computation throughputs.
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46

Wu, Changming, Haoqin Deng, Yi-Siou Huang, Heshan Yu, Ichiro Takeuchi, Carlos A. Ríos Ocampo, and Mo Li. "Freeform direct-write and rewritable photonic integrated circuits in phase-change thin films." Science Advances 10, no. 1 (January 5, 2024). http://dx.doi.org/10.1126/sciadv.adk1361.

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Photonic integrated circuits (PICs) with rapid prototyping and reprogramming capabilities promise revolutionary impacts on a plethora of photonic technologies. We report direct-write and rewritable photonic circuits on a low-loss phase-change material (PCM) thin film. Complete end-to-end PICs are directly laser-written in one step without additional fabrication processes, and any part of the circuit can be erased and rewritten, facilitating rapid design modification. We demonstrate the versatility of this technique for diverse applications, including an optical interconnect fabric for reconfigurable networking, a photonic crossbar array for optical computing, and a tunable optical filter for optical signal processing. By combining the programmability of the direct laser writing technique with PCM, our technique unlocks opportunities for programmable photonic networking, computing, and signal processing. Moreover, the rewritable photonic circuits enable rapid prototyping and testing in a convenient and cost-efficient manner, eliminate the need for nanofabrication facilities, and thus promote the proliferation of photonics research and education to a broader community.
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47

Wu, Bo, Hailong Zhou, Jianji Dong, and Xinliang Zhang. "Programmable integrated photonic coherent matrix: Principle, configuring, and applications." Applied Physics Reviews 11, no. 1 (January 31, 2024). http://dx.doi.org/10.1063/5.0184982.

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Every multi-input multi-output linear optical system can be deemed as a matrix multiplier that carries out a desired transformation on the input optical information, such as imaging, modulation, and computing. The strong programmability of the optical matrix has been explored and proved to be able to bring more flexibility and greater possibilities to the applications such as optical signal processing and general optical digital and analog computing. Furthermore, the burgeoning integrated photonics with advanced manufacturing and light manipulating technology pave the way for large-scale reconfigurable photonic coherent matrix. This paper reviews the programmable photonic coherent matrix in the integrated platform. First, the theoretical basis and optimizing methods of three types of integrated photonic coherent matrix (Mach–Zehnder interferometer mesh, multi-plane diffraction, and crossbar array) are introduced. Next, we overview the configuring method of this optical matrix. Furthermore, their applications in optical signal processing, optical neural network, optical logic operation, recurrent optical matrix acceleration, and optical quantum computing are comprehensively reviewed. Finally, the challenges and opportunities of programmable integrated photonic coherent matrix are discussed.
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48

Bartolucci, Sara, Patrick Birchall, Hector Bombín, Hugo Cable, Chris Dawson, Mercedes Gimeno-Segovia, Eric Johnston, et al. "Fusion-based quantum computation." Nature Communications 14, no. 1 (February 17, 2023). http://dx.doi.org/10.1038/s41467-023-36493-1.

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AbstractThe standard primitives of quantum computing include deterministic unitary entangling gates, which are not natural operations in many systems including photonics. Here, we present fusion-based quantum computation, a model for fault tolerant quantum computing constructed from physical primitives readily accessible in photonic systems. These are entangling measurements, called fusions, which are performed on the qubits of small constant sized entangled resource states. Probabilistic photonic gates as well as errors are directly dealt with by the quantum error correction protocol. We show that this computational model can achieve a higher threshold than schemes reported in literature. We present a ballistic scheme which can tolerate a 10.4% probability of suffering photon loss in each fusion, which corresponds to a 2.7% probability of loss of each individual photon. The architecture is also highly modular and has reduced classical processing requirements compared to previous photonic quantum computing architectures.
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49

Morichetti, Francesco. "Grand challenges in neuromorphic photonics and photonic computing." Frontiers in Photonics 4 (January 29, 2024). http://dx.doi.org/10.3389/fphot.2023.1336510.

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50

Romeira, Bruno, Ricardo R. M. Adão, Jana Berit Nieder, Qusay Al-taai, Weikang Zhang, Robert H. Hadfield, Edward Wasige, et al. "Brain-inspired nanophotonic spike computing: challenges and prospects." Neuromorphic Computing and Engineering, June 16, 2023. http://dx.doi.org/10.1088/2634-4386/acdf17.

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Abstract Nanophotonic spiking neural networks based on neuron-like excitable subwavelength (submicrometre) devices are of key importance for realizing brain-inspired, power-efficient artificial intelligence (AI) systems with high degree of parallelism and energy efficiency. Despite significant advances in neuromorphic photonics, compact and efficient nanophotonic elements for spiking signal emission and detection, as required for spike-based computation, remain largely unexplored. In this invited perspective, we outline the main challenges, early achievements, and opportunities toward a key-enabling photonic neuro-architecture using III-V/Si integrated spiking nodes based on nanoscale resonant tunnelling diodes (nanoRTDs). We utilize nanoRTDs as nonlinear artificial neurons capable of spiking at high-speeds. We discuss the prospects for monolithic integration of nanoRTDs with nanoscale light-emitting diodes (nanoLEDs), nanolaser diodes (nanoLDs), and nanophotodetectors (nanoPDs) to realize neuron emitter and receiver spiking nodes. Such layout would have a small footprint, fast operation, and low power consumption, all key requirements for efficient optoelectronic spiking operation. We discuss how silicon photonics interconnects, integrated photorefractive interconnects, and 3D waveguide polymeric interconnections can be used for interconnecting the emitter-receiver spiking photonic neural nodes. Finally, using numerical simulations of artificial neuron models, we present spike-based spatio-temporal learning methods for applications in relevant functional AI tasks, such as image pattern recognition, edge detection, and spiking neural networks for inference and learning. Future developments in neuromorphic spiking photonic nanocircuits, as outlined here, will significantly boost the processing and transmission capabilities of next-generation nanophotonic spike-based neuromorphic architectures for energy-efficient AI applications.
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