Journal articles: 'Neuromorphic photonics'

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Ferreira de Lima, Thomas, Bhavin J. Shastri, Alexander N. Tait, Mitchell A. Nahmias, and Paul R. Prucnal. "Progress in neuromorphic photonics." Nanophotonics 6, no. 3 (2017): 577–99. http://dx.doi.org/10.1515/nanoph-2016-0139.

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AbstractAs society’s appetite for information continues to grow, so does our need to process this information with increasing speed and versatility. Many believe that the one-size-fits-all solution of digital electronics is becoming a limiting factor in certain areas such as data links, cognitive radio, and ultrafast control. Analog photonic devices have found relatively simple signal processing niches where electronics can no longer provide sufficient speed and reconfigurability. Recently, the landscape for commercially manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. By bridging the mathematical prowess of artificial neural networks to the underlying physics of optoelectronic devices, neuromorphic photonics could breach new domains of information processing demanding significant complexity, low cost, and unmatched speed. In this article, we review the progress in neuromorphic photonics, focusing on photonic integrated devices. The challenges and design rules for optoelectronic instantiation of artificial neurons are presented. The proposed photonic architecture revolves around the processing network node composed of two parts: a nonlinear element and a network interface. We then survey excitable lasers in the recent literature as candidates for the nonlinear node and microring-resonator weight banks as the network interface. Finally, we compare metrics between neuromorphic electronics and neuromorphic photonics and discuss potential applications.

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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 (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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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 (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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Ferreira de Lima, Thomas, Alexander N. Tait, Armin Mehrabian, et al. "Primer on silicon neuromorphic photonic processors: architecture and compiler." Nanophotonics 9, no. 13 (2020): 4055–73. http://dx.doi.org/10.1515/nanoph-2020-0172.

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AbstractMicroelectronic computers have encountered challenges in meeting all of today’s demands for information processing. Meeting these demands will require the development of unconventional computers employing alternative processing models and new device physics. Neural network models have come to dominate modern machine learning algorithms, and specialized electronic hardware has been developed to implement them more efficiently. A silicon photonic integration industry promises to bring manufacturing ecosystems normally reserved for microelectronics to photonics. Photonic devices have already found simple analog signal processing niches where electronics cannot provide sufficient bandwidth and reconfigurability. In order to solve more complex information processing problems, they will have to adopt a processing model that generalizes and scales. Neuromorphic photonics aims to map physical models of optoelectronic systems to abstract models of neural networks. It represents a new opportunity for machine information processing on sub-nanosecond timescales, with application to mathematical programming, intelligent radio frequency signal processing, and real-time control. The strategy of neuromorphic engineering is to externalize the risk of developing computational theory alongside hardware. The strategy of remaining compatible with silicon photonics externalizes the risk of platform development. In this perspective article, we provide a rationale for a neuromorphic photonics processor, envisioning its architecture and a compiler. We also discuss how it can be interfaced with a general purpose computer, i.e. a CPU, as a coprocessor to target specific applications. This paper is intended for a wide audience and provides a roadmap for expanding research in the direction of transforming neuromorphic photonics into a viable and useful candidate for accelerating neuromorphic computing.

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Nahmias, Mitchell A., Bhavin J. Shastri, Alexander N. Tait, Thomas Ferreira de Lima, and Paul R. Prucnal. "Neuromorphic Photonics." Optics and Photonics News 29, no. 1 (2018): 34. http://dx.doi.org/10.1364/opn.29.1.000034.

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Li, Tiantian, Yijie Li, Yuteng Wang, et al. "Neuromorphic Photonics Based on Phase Change Materials." Nanomaterials 13, no. 11 (2023): 1756. http://dx.doi.org/10.3390/nano13111756.

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Neuromorphic photonics devices based on phase change materials (PCMs) and silicon photonics technology have emerged as promising solutions for addressing the limitations of traditional spiking neural networks in terms of scalability, response delay, and energy consumption. In this review, we provide a comprehensive analysis of various PCMs used in neuromorphic devices, comparing their optical properties and discussing their applications. We explore materials such as GST (Ge2Sb2Te5), GeTe-Sb2Te3, GSST (Ge2Sb2Se4Te1), Sb2S3/Sb2Se3, Sc0.2Sb2Te3 (SST), and In2Se3, highlighting their advantages and challenges in terms of erasure power consumption, response rate, material lifetime, and on-chip insertion loss. By investigating the integration of different PCMs with silicon-based optoelectronics, this review aims to identify potential breakthroughs in computational performance and scalability of photonic spiking neural networks. Further research and development are essential to optimize these materials and overcome their limitations, paving the way for more efficient and high-performance photonic neuromorphic devices in artificial intelligence and high-performance computing applications.

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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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de Lima, Thomas Ferreira, Hsuan-Tung Peng, Alexander N. Tait, et al. "Machine Learning With Neuromorphic Photonics." Journal of Lightwave Technology 37, no. 5 (2019): 1515–34. http://dx.doi.org/10.1109/jlt.2019.2903474.

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Pitruzzello, Giampaolo. "Neuromorphic photonics for efficient computing." Nature Photonics 19, no. 4 (2025): 350–51. https://doi.org/10.1038/s41566-025-01654-9.

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Argyris, Apostolos. "Photonic neuromorphic technologies in optical communications." Nanophotonics 11, no. 5 (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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Stabile, R., G. Dabos, C. Vagionas, B. Shi, N. Calabretta, and N. Pleros. "Neuromorphic photonics: 2D or not 2D?" Journal of Applied Physics 129, no. 20 (2021): 200901. http://dx.doi.org/10.1063/5.0047946.

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George, Jonathan K., Armin Mehrabian, Rubab Amin, et al. "Neuromorphic photonics with electro-absorption modulators." Optics Express 27, no. 4 (2019): 5181. http://dx.doi.org/10.1364/oe.27.005181.

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Marquez, Bicky A., Matthew J. Filipovich, Emma R. Howard, et al. "Silicon photonics for artificial intelligence applications." Photoniques, no. 104 (September 2020): 40–44. http://dx.doi.org/10.1051/photon/202010440.

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Artificial intelligence enabled by neural networks has enabled applications in many fields (e.g. medicine, finance, autonomous vehicles). Software implementations of neural networks on conventional computers are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimic neurons and synapses in brain for distributed and parallel processing. Neuromorphic engineering enabled by silicon photonics can offer subnanosecond latencies, and can extend the domain of artificial intelligence applications to high-performance computing and ultrafast learning. We discuss current progress and challenges on these demonstrations to scale to practical systems for training and inference.

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Shastri, Bhavin J., Alexander N. Tait, T. Ferreira de Lima, et al. "Photonics for artificial intelligence and neuromorphic computing." Nature Photonics 15, no. 2 (2021): 102–14. http://dx.doi.org/10.1038/s41566-020-00754-y.

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Yi, Ailun, Chengli Wang, Liping Zhou, et al. "Silicon carbide for integrated photonics." Applied Physics Reviews 9, no. 3 (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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Sun, Haoyang, Qifeng Qiao, Qingze Guan, and Guangya Zhou. "Silicon Photonic Phase Shifters and Their Applications: A Review." Micromachines 13, no. 9 (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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Guo, Xuhan, Jinlong Xiang, Yujia Zhang, and Yikai Su. "Integrated Neuromorphic Photonics: Synapses, Neurons, and Neural Networks." Advanced Photonics Research 2, no. 6 (2021): 2170019. http://dx.doi.org/10.1002/adpr.202170019.

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Guo, Xuhan, Jinlong Xiang, Yujia Zhang, and Yikai Su. "Integrated Neuromorphic Photonics: Synapses, Neurons, and Neural Networks." Advanced Photonics Research 2, no. 6 (2021): 2000212. http://dx.doi.org/10.1002/adpr.202000212.

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Adão, Ricardo M. R., Manuel Caño-Garcia, Christian Maibohm, Bruno Romeira, and Jana B. Nieder. "Oscillator Finite-Difference Time-Domain (O-FDTD) electric field propagation model: integrated photonics and networks." EPJ Web of Conferences 255 (2021): 01005. http://dx.doi.org/10.1051/epjconf/202125501005.

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The recently developed Lorentz Oscillator Model-inspired Oscillator Finite-Difference Time-Domain (O-FDTD) is one of the simplest FDTD models ever proposed, using a single field equation for electric field propagation. We demonstrate its versatility on various scales and benchmark its simulation performance against theory, conventional FDTD simulations, and experimental observations. The model’s broad applicability is demonstrated for (but not limited to) three contrasting realms: integrated photonics components on the nano- and micrometer scale, city-wide propagating radiofrequency signals reaching into the hundreds of meters scale, and for the first time, in support of 3D optical waveguide design that may play a key role in neuromorphic photonic computational devices.

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Bi, Jinming, Yanran Li, Rong Lu, Honglin Song, and Jie Jiang. "Electrolyte-gated optoelectronic transistors for neuromorphic applications." Journal of Semiconductors 46, no. 2 (2025): 021401. https://doi.org/10.1088/1674-4926/24090042.

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Abstract The traditional von Neumann architecture has demonstrated inefficiencies in parallel computing and adaptive learning, rendering it incapable of meeting the growing demand for efficient and high-speed computing. Neuromorphic computing with significant advantages such as high parallelism and ultra-low power consumption is regarded as a promising pathway to overcome the limitations of conventional computers and achieve the next-generation artificial intelligence. Among various neuromorphic devices, the artificial synapses based on electrolyte-gated transistors stand out due to their low energy consumption, multimodal sensing/recording capabilities, and multifunctional integration. Moreover, the emerging optoelectronic neuromorphic devices which combine the strengths of photonics and electronics have demonstrated substantial potential in the neuromorphic computing field. Therefore, this article reviews recent advancements in electrolyte-gated optoelectronic neuromorphic transistors. First, it provides an overview of artificial optoelectronic synapses and neurons, discussing aspects such as device structures, operating mechanisms, and neuromorphic functionalities. Next, the potential applications of optoelectronic synapses in different areas such as artificial visual system, pain system, and tactile perception systems are elaborated. Finally, the current challenges are summarized, and future directions for their developments are proposed.

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Katumba, Andrew, Matthias Freiberger, Floris Laporte, et al. "Neuromorphic Computing Based on Silicon Photonics and Reservoir Computing." IEEE Journal of Selected Topics in Quantum Electronics 24, no. 6 (2018): 1–10. http://dx.doi.org/10.1109/jstqe.2018.2821843.

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Jha, Aashu, Chaoran Huang, and Paul R. Prucnal. "Reconfigurable all-optical nonlinear activation functions for neuromorphic photonics." Optics Letters 45, no. 17 (2020): 4819. http://dx.doi.org/10.1364/ol.398234.

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Yu, Sunkyu, Xianji Piao, and Namkyoo Park. "Neuromorphic Photonics: Neuromorphic Functions of Light in Parity-Time-Symmetric Systems (Adv. Sci. 15/2019)." Advanced Science 6, no. 15 (2019): 1970092. http://dx.doi.org/10.1002/advs.201970092.

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Totovic, Angelina R., George Dabos, Nikolaos Passalis, Anastasios Tefas, and Nikos Pleros. "Femtojoule per MAC Neuromorphic Photonics: An Energy and Technology Roadmap." IEEE Journal of Selected Topics in Quantum Electronics 26, no. 5 (2020): 1–15. http://dx.doi.org/10.1109/jstqe.2020.2975579.

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Weis, Martin. "Organic Semiconducting Polymers in Photonic Devices: From Fundamental Properties to Emerging Applications." Applied Sciences 15, no. 7 (2025): 4028. https://doi.org/10.3390/app15074028.

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This review examines the distinct advantages of organic semiconductors over conventional insulating polymers as optically active materials in photonic applications. We analyze the fundamental principles governing their unique optical and electronic properties, from basic conjugated polymer systems to advanced molecular architectures. The review systematically explores key material classes, including polyfluorenes, polyphenylene vinylenes, and polythiophenes, highlighting their dual electrical–optical functionality unavailable in passive polymer systems. Particular attention is given to polymer blends, composites, and hybrid organic–inorganic systems, demonstrating how semiconductor properties enable enhanced performance through materials engineering. We contrast passive components with active photonic devices, illustrating how the semiconductor nature of these polymers facilitates novel functionalities beyond simple light guiding. The review explores emerging applications in neuromorphic photonics, quantum systems, and bio-integrated devices, where the combined electronic–optical properties of organic semiconductors create unique capabilities impossible with insulating polymers. Finally, we discuss design strategies for optimizing these distinctive properties and present perspectives on future developments. This review establishes organic semiconductors as transformative materials for advancing photonic technologies through their combined electronic–optical functionality.

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Bile, Alessandro, Hamed Tari, Riccardo Pepino, Arif Nabizada, and Eugenio Fazio. "Solitonic Neural Network: A novel approach of Photonic Artificial Intelligence based on photorefractive solitonic waveguides." EPJ Web of Conferences 287 (2023): 13003. http://dx.doi.org/10.1051/epjconf/202328713003.

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Neuromorphic models are proving capable of performing complex machine learning tasks, overcoming the structural limitations imposed by software systems and electronic neuromorphic models. Unlike computers, the brain uses a unified geometry whereby memory and computation occur in the same physical location. The neuromorphic approach tries to reproduce the functional blocks of biological neural networks. In the photonics field, one possible and efficient way is to use integrated circuits based on soliton waveguides, ie channels self-written by light. Thanks to the nonlinearity of some crystals, propagating light can write waveguides and then can modulate them according to the information it carries. Thus, the created structures are not static but they can self-modify by varying the input information pattern. These hardware systems show a neuroplasticity which is very close to the one which characterize the brain functioning. The solitonic neuromorphic paradigm this work introduces is based on X-junction solitonic neurons as the fundamental elements for complex neural networks. These solitonic units are able to learn information both in supervised and unsupervised ways by unbalancing the X-junction. The storage of information coincides with the evolution of structure that changes plastically. Thus, complex solitonic networks can store information as propagation trajectories and use them for reasoning.

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Mourgias-Alexandris, George, Angelina Totovic, Apostolos Tsakyridis, et al. "Neuromorphic Photonics With Coherent Linear Neurons Using Dual-IQ Modulation Cells." Journal of Lightwave Technology 38, no. 4 (2020): 811–19. http://dx.doi.org/10.1109/jlt.2019.2949133.

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Spagnolo, Michele, Joshua Morris, Simone Piacentini, et al. "Experimental photonic quantum memristor." Nature Photonics 16, no. 4 (2022): 318–23. http://dx.doi.org/10.1038/s41566-022-00973-5.

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AbstractMemristive devices are a class of physical systems with history-dependent dynamics characterized by signature hysteresis loops in their input–output relations. In the past few decades, memristive devices have attracted enormous interest in electronics. This is because memristive dynamics is very pervasive in nanoscale devices, and has potentially groundbreaking applications ranging from energy-efficient memories to physical neural networks and neuromorphic computing platforms. Recently, the concept of a quantum memristor was introduced by a few proposals, all of which face limited technological practicality. Here we propose and experimentally demonstrate a novel quantum-optical memristor (based on integrated photonics) that acts on single-photon states. We fully characterize the memristive dynamics of our device and tomographically reconstruct its quantum output state. Finally, we propose a possible application of our device in the framework of quantum machine learning through a scheme of quantum reservoir computing, which we apply to classical and quantum learning tasks. Our simulations show promising results, and may break new ground towards the use of quantum memristors in quantum neuromorphic architectures.

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PAVANELLO, Fabio. "Lighting the way to better security - The NEUROPULS photonics-based approach." HiPEAC info, no. 72 (June 27, 2024): 20–21. https://doi.org/10.5281/zenodo.12565070.

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The expansion of edge computing is posing new challenges for cybersecurity: solutions need to offer robust security with low energy and low latency. To answer this challenge, the NEUROPULS project, funded by the European Union (EU), is taking a novel, neuromorphic approach, developing photonics-based hardware security primitives that draw on the properties of light for robust, yet lightweight, cybersecurity layers. HiPEAC caught up with NEUROPULS coordinator Fabio Pavanello (CNRS – Center for Radiofrequencies, Optics, and Microelectronics in the Alps – CROMA laboratory) to find out more.

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Singh, Pulkit, Rajan Singh, Ashok Kumar Cheeli, Pinnamaraju Sahitya, and Sriram Rahul. "Materials for semiconductor nanowires in nanoscale photonic and electronic devices." Journal of Physics: Conference Series 2837, no. 1 (2024): 012022. http://dx.doi.org/10.1088/1742-6596/2837/1/012022.

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Abstract Many aspects of civilization are impacted by electronic and optoelectronic equipment, including medical instruments, communications, computing, multimedia systems, and basic home appliances. the development of nanoscale devices has garnered significant attention due to the increasing demand for smaller, more powerful, and efficient systems across various industries. Nanoscale devices operate at the nanometer scale, typically involving structures and components with dimensions on the order of 1 to 100 nanometers. As a potent class of materials, semiconductor nanowires are creating a lot of new possibilities for innovative nanoscale photonic and electrical devices through carefully regulated development and organization. the exploration of electronic and optoelectronic nanodevices, along with integrated arrays, opens up a rich landscape of possibilities across various technological domains. This research not only pushes the boundaries of miniaturization but also paves the way for innovative applications in electronics, photonics, sensing, and emerging fields like quantum and neuromorphic computing that hold the potential for numerous applications in the future.

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Malchow, Konstantin, Sarah Hamdad, Bojun Cheng, Till Zellweger, Juerg Leuthold, and Alexandre Bouhelier. "Light in Memristive Atomic Scale Junction - Memristors go Photonics." EPJ Web of Conferences 287 (2023): 14004. http://dx.doi.org/10.1051/epjconf/202328714004.

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Memristive devices are an emerging new type of devices operating at the scale of a few or even single atoms. They largely exploited for emulating the electrical function of synapses and are thus currently investigated for performing in-memory and neuromorphic computing. In this contribution, we report the obser-vation of a novel feature in these devices. We show that memristors can also emit photons during their activity. We identified three mechanisms producing photons with vastly different properties. The crossover between emission regimes depends on the history of the memristor and its operating conductance. Our results suggests that this new generation of memristor pave the way for multidimensional neural networks using both electrons and photons as information carrier.

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Stephanie, Margareta Vania, Florian Honz, Nemanja Vokić, et al. "SOA-REAM Assisted Synaptic Receptor for Weighted-Sum Detection of Multiple Inputs." IEEE Journal of Lightwave Technology 41, no. 4 (2023): 1258–64. https://doi.org/10.1109/JLT.2022.3212111.

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Neuromorphic photonics is a promising research field due to its potential to tackle the limitations arising from the bottleneck of the von-Neumann computation architecture. Inspired by the characteristics and behavior of the biological brain, photonic neural networks are touted as a solution for solving complex problems that require GHz operation at low latency and low power consumption. An essential building block of such a neural network is a low-complexity multiply-accumulate operation, for which efficient functional implementations in the optical domain are sought for. Towards this direction, we present a synaptic receptor that functionally integrates weighting and signal detection. This optical multiply-accumulate operation is accomplished through a monolithic integrated semiconductor optical amplifier and reflective electro-absorption modulator, which together serve as a colorless frequency demodulator and detector of frequency-coded signals. Moreover, we show that two spike trains can be simultaneously processed with alternating signs and detected as a weighted sum. The performance of the proposed synaptic receptors is further validated through a low bit error ratio for signal rates of up to 10 Gb/s.

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Robertson, Joshua, Tao Deng, Julien Javaloyes, and Antonio Hurtado. "Controlled inhibition of spiking dynamics in VCSELs for neuromorphic photonics: theory and experiments." Optics Letters 42, no. 8 (2017): 1560. http://dx.doi.org/10.1364/ol.42.001560.

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Stark, Pascal, Folkert Horst, Roger Dangel, Jonas Weiss, and Bert Jan Offrein. "Opportunities for integrated photonic neural networks." Nanophotonics 9, no. 13 (2020): 4221–32. http://dx.doi.org/10.1515/nanoph-2020-0297.

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AbstractPhotonics offers exciting opportunities for neuromorphic computing. This paper specifically reviews the prospects of integrated optical solutions for accelerating inference and training of artificial neural networks. Calculating the synaptic function, thereof, is computationally very expensive and does not scale well on state-of-the-art computing platforms. Analog signal processing, using linear and nonlinear properties of integrated optical devices, offers a path toward substantially improving performance and power efficiency of these artificial intelligence workloads. The ability of integrated photonics to operate at very high speeds opens opportunities for time-critical real-time applications, while chip-level integration paves the way to cost-effective manufacturing and assembly.

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Gonzalez-Raya, Tasio, Joseph M. Lukens, Lucas C. Céleri, and Mikel Sanz. "Quantum Memristors in Frequency-Entangled Optical Fields." Materials 13, no. 4 (2020): 864. http://dx.doi.org/10.3390/ma13040864.

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A quantum memristor is a passive resistive circuit element with memory, engineered in a given quantum platform. It can be represented by a quantum system coupled to a dissipative environment, in which a system–bath coupling is mediated through a weak measurement scheme and classical feedback on the system. In quantum photonics, such a device can be designed from a beam splitter with tunable reflectivity, which is modified depending on the results of measurements in one of the outgoing beams. Here, we show that a similar implementation can be achieved with frequency-entangled optical fields and a frequency mixer that, working similarly to a beam splitter, produces state superpositions. We show that the characteristic hysteretic behavior of memristors can be reproduced when analyzing the response of the system with respect to the control, for different experimentally attainable states. Since memory effects in memristors can be exploited for classical and neuromorphic computation, the results presented in this work could be a building block for constructing quantum neural networks in quantum photonics, when scaling up.

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Amin, Rubab, Jonathan K. George, Hao Wang, et al. "An ITO–graphene heterojunction integrated absorption modulator on Si-photonics for neuromorphic nonlinear activation." APL Photonics 6, no. 12 (2021): 120801. http://dx.doi.org/10.1063/5.0062830.

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Tsakyridis, Apostolos, Miltiadis Moralis-Pegios, George Giamougiannis, et al. "Photonic neural networks and optics-informed deep learning fundamentals." APL Photonics 9, no. 1 (2024): 011102–011102. https://doi.org/10.1063/5.0169810.

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The recent explosive compute growth, mainly fueled by the boost of artificial intelligence (AI) and deep neural networks (DNNs), is currently instigating the demand for a novel computing paradigm that can overcome the insurmountable barriers imposed by conventional electronic computing architectures. Photonic neural networks (PNNs) implemented on silicon integration platforms stand out as a promising candidate to endow neural network (NN) hardware, offering the potential for energy efficient and ultra-fast computations through the utilization of the unique primitives of photonics, i.e., energy efficiency, THz bandwidth, and low-latency. Thus far, several demonstrations have revealed the huge potential of PNNs in performing both linear and non-linear NN operations at unparalleled speed and energy consumption metrics. Transforming this potential into a tangible reality for deep learning (DL) applications requires, however, a deep understanding of the basic PNN principles, requirements, and challenges across all constituent architectural, technological, and training aspects. In this Tutorial, we, initially, review the principles of DNNs along with their fundamental building blocks, analyzing also the key mathematical operations needed for their computation in photonic hardware. Then, we investigate, through an intuitive mathematical analysis, the interdependence of bit precision and energy efficiency in analog photonic circuitry, discussing the opportunities and challenges of PNNs. Followingly, a performance overview of PNN architectures, weight technologies, and activation functions is presented, summarizing their impact in speed, scalability, and power consumption. Finally, we provide a holistic overview of the optics-informed NN training framework that incorporates the physical properties of photonic building blocks into the training process in order to improve the NN classification accuracy and effectively elevate neuromorphic photonic hardware into high-performance DL computational settings.

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Katumba, Andrew, Xin Yin, Joni Dambre, and Peter Bienstman. "A Neuromorphic Silicon Photonics Nonlinear Equalizer For Optical Communications With Intensity Modulation and Direct Detection." Journal of Lightwave Technology 37, no. 10 (2019): 2232–39. http://dx.doi.org/10.1109/jlt.2019.2900568.

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Hurtado, Antonio, and Julien Javaloyes. "Controllable spiking patterns in long-wavelength vertical cavity surface emitting lasers for neuromorphic photonics systems." Applied Physics Letters 107, no. 24 (2015): 241103. http://dx.doi.org/10.1063/1.4937730.

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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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Raj, Vidur, Adan Azem, Max Russell Patterson, Devendra Kumar Namburi, Jeff F. Young, and Robert H. Hadfield. "Waveguide integrated superconducting nanowire single-photon detectors for integrated photonics." Journal of Physics D: Applied Physics, May 15, 2025. https://doi.org/10.1088/1361-6463/add946.

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Abstract Integrated photonics is expected to play a key role in the scalability of quantum systems for applications such as quantum computing, quantum communications, quantum internet, and quantum metrology. One of the primary components of quantum integrated photonics is a single photon detector, which reads out the quantum information encoded in photons. Amongst available single-photon detection schemes, superconducting nanowire single photon detectors (SNSPDs) remain the most promising technology for effective on-chip coupling, because they can be seamlessly integrated with a wide range of waveguide materials and substrates and have shown unparalleled performance from visible to the mid-infrared regime. Here, we review different aspects of SNSPDs and schemes for their on-chip integration for different integrated photonics applications. Although mostly concentrated on quantum applications, we also cover some of the important wider photonics applications including imaging, LiDAR, neuromorphic computing, and single-photon spectroscopy, and conclude the review with a future outlook discussing emerging research areas enabled by photonic integrated circuits based on SNSPDs.

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Totovic, Angelina, Christos Pappas, Manos Kirtas, et al. "WDM equipped universal linear optics for programmable neuromorphic photonic processors." Neuromorphic Computing and Engineering, May 23, 2022. http://dx.doi.org/10.1088/2634-4386/ac724d.

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Abstract Non-von-Neumann computing architectures and Deep Learning training models have sparked a new computational era where neurons are forming the main architectural backbone and vector, matrix and tensor multiplications comprise the basic mathematical toolbox. This paradigm shift has triggered a new race among hardware technology candidates; within this frame, the field of neuromorphic photonics promises to convolve the targeted algebraic portfolio along a computational circuitry with unique speed, parallelization, and energy efficiency advantages. Fueled by the inherent energy efficient analog matrix multiply operations of optics, the staggering advances of photonic integration and the enhanced multiplexing degrees offered by light, neuromorphic photonics has stamped the resurgence of optical computing brining a unique perspective in low-energy and ultra-fast linear algebra functions. However, the field of neuromorphic photonics has relied so far on two basic architectural schemes, i.e., coherent linear optical circuits and incoherent WDM approaches, where wavelengths have still not been exploited as a new mathematical dimension. In this paper, we present a radically new approach for promoting the synergy of WDM with universal linear optics and demonstrate a new, high-fidelity crossbar-based neuromorphic photonic platform, able to support matmul with multidimensional operands. Going a step further, we introduce the concept of programmable input and weight banks, supporting in situ reconfigurability, forming in this way the first WDM-equipped universal linear optical operator and demonstrating different operational modes like matrix-by-matrix and vector-by-tensor multiplication. The benefits of our platform are highlighted in a Fully Convolutional Neural Network layout that is responsible for parity identification in the MNIST handwritten digit dataset, with physical layer simulations revealing an accuracy of ~94%, degraded by only 2% compared to respective results obtained when executed entirely by software. Finally, our in-depth analysis provides the guidelines for neuromorphic photonic processor performance improvement, revealing along the way that 4-bit quantization is sufficient for inputs, whereas the weights can be implemented with as low as 2-bits of precision, offering substantial benefits in terms of driving circuitry complexity and energy savings.

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Blow, Eric C., Simon Bilodeau, Weipeng Zhang, et al. "Radio‐Frequency Linear Analysis and Optimization of Silicon Photonic Neural Networks." Advanced Photonics Research, April 21, 2024. http://dx.doi.org/10.1002/adpr.202300306.

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Broadband analog signal processors utilizing silicon photonics have demonstrated a significant impact in numerous application spaces, offering unprecedented bandwidths, dynamic range, and tunability. In the past decade, microwave photonic techniques have been applied to neuromorphic processing, resulting in the development of novel photonic neural network architectures. Neuromorphic photonic systems can enable machine learning capabilities at extreme bandwidths and speeds. Herein, low‐quality factor microring resonators are implemented to demonstrate broadband optical weighting. In addition, silicon photonic neural network architectures are critically evaluated, simulated, and optimized from a radio‐frequency performance perspective. This analysis highlights the linear front‐end of the photonic neural network, the effects of linear and nonlinear loss within silicon waveguides, and the impact of electrical preamplification.

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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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van Niekerk, Matthew, Anthony Rizzo, Hector Rubio, et al. "Massively scalable wavelength diverse integrated photonic linear neuron." Neuromorphic Computing and Engineering, September 2, 2022. http://dx.doi.org/10.1088/2634-4386/ac8ecc.

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Abstract As computing resource demands continue to escalate in the face of big data, cloud-connectivity and the internet of things, it has become imperative to develop new low-power, scalable architectures. Neuromorphic photonics, or photonic neural networks, have become a feasible solution for the physical implementation of eﬀicient algorithms directly on-chip. This application is primarily due to the linear nature of light and the scalability of silicon photonics, specifically leveraging the wide-scale complementary metal-oxide-semiconductor (CMOS) manufacturing infrastructure used to fabricate microelectronics chips. Current neuromorphic photonic implementations stem from two paradigms: wavelength coherent and incoherent. Here, we introduce a novel architecture that supports coherent and incoherent operation to increase the capability and capacity of photonic neural networks with a dramatic reduction in footprint compared to previous demonstrations. As a proof-of-principle, we experimentally demonstrate simple addition and subtraction operations on a foundry-fabricated silicon photonic chip. Additionally, we experimentally validate an on-chip network to predict the logical 2-bit gates AND, OR, and XOR to accuracies of 96.8%,99%, and 98.5%, respectively. This architecture is compatible with highly wavelength parallel sources, enabling massively scalable photonic neural networks.

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Xu, Lei, Jiawei Zhang, Eli A. Doris, et al. "Building Scalable Silicon Microring Resonator‐Based Neuromorphic Photonic Circuits Using Post‐Fabrication Processing with Photochromic Material." Advanced Optical Materials, March 20, 2025. https://doi.org/10.1002/adom.202402706.

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AbstractNeuromorphic photonics has become one of the research forefronts in photonics, with its benefits in low‐latency signal processing and potential in significant energy consumption reduction when compared with digital electronics. With artificial intelligence (AI) computing accelerators in high demand, one of the high‐impact research goals is to build scalable neuromorphic photonic integrated circuits which can accelerate the computing of AI models at high energy efficiency. A complete neuromorphic photonic computing system comprises seven stacks: materials, devices, circuits, microarchitecture, system architecture, algorithms, and applications. Here, we consider microring resonator (MRR)‐based network designs toward building scalable silicon integrated photonic neural networks (PNN), and variations of MRR resonance wavelength from the fabrication process and their impact on PNN scalability. Further, post‐fabrication processing using organic photochromic layers over the silicon platform is shown to be effective for trimming MRR resonance wavelength variation, which can significantly reduce energy consumption from the MRR‐based PNN configuration. Post‐fabrication processing with photochromic materials to compensate for the variation in MRR fabrication will allow a scalable silicon system on a chip without sacrificing today's performance metrics, which will be critical for the commercial viability and volume production of large‐scale silicon photonic circuits.

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Wang, Dingchen, Shilei Dai, Anran Yuan, et al. "Stretchable Hydrogel Optical Memristor for Photonic Near‐Sensor Neuromorphic Skin." Advanced Functional Materials, May 28, 2025. https://doi.org/10.1002/adfm.202419937.

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AbstractThe development of skin‐like optical memristors is crucial for wearable photonic neuromorphic computing, which enables ultrafast artificial neural networks for edge information processing. However, most existing optical memristors are made of rigid materials, posing challenges in integrating with soft photonic sensors and increasing the risk of device failure under large strains. Additionally, the strain‐perturbed photonic matrix‐vector multiplier (PMVM) may malfunction when stretched. In this paper, a design strategy that utilizes transverse surface colorization (TSC) of hydrogel waveguides is presented to create stretchable optical memristors that can implement a strain‐invariant PMVM. These stretchable optical memristors provide more than 4‐bit distinct, non‐volatile memory states (>104 s). By utilizing stretchable optical memristors as bending sensors and strain‐invariant PMVM, an integrated photonic near‐sensor neuromorphic skin capable of real‐time hand gesture recognition (latency < 0.5 ns) is demonstrated with 95.5% accuracy even under 30% strain. This seamless integration of sensing, memory, and processing capabilities in a photonic near‐sensor neuromorphic skin paves the way for future wearable photonics.

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Musorin, Alexander I., Aleksandr S. Shorokhov, Alexander A. Chezhegov, et al. "Photonics approaches for neuromorphic computing." Uspekhi Fizicheskih Nauk, July 2023. http://dx.doi.org/10.3367/ufnr.2023.07.039505.

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Musorin, Alexander I., Aleksandr S. Shorokhov, Alexander A. Chezhegov, et al. "Photonics approaches for neuromorphic computing." Physics-Uspekhi, July 2023. http://dx.doi.org/10.3367/ufne.2023.07.039505.

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Romeira, Bruno, Ricardo R. M. Adão, Jana Berit Nieder, 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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Journal articles on the topic 'Neuromorphic photonics'

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