Готові списки джерел за темами / Photonic computing

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Добірка наукової літератури з теми "Photonic computing"

Автор: Grafiati
Опубліковано: 15 лютого 2025
Оновлено: 25 липня 2025

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Статті в журналах з теми "Photonic computing"

1

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

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2

Ávila, Gerardo Antonio Castañón, Walter Cerroni, and Ana Maria Sarmiento-Moncada. "Integrated Photonics for IoT, RoF, and Distributed Fog–Cloud Computing: A Comprehensive Review." Applied Sciences 15, no. 13 (2025): 7494. https://doi.org/10.3390/app15137494.

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Анотація:
Integrated photonics is a transformative technology for enhancing communication and computation in Cloud and Fog computing networks. Photonic integrated circuits (PICs) enable significant improvements in data-processing speed, energy-efficiency, scalability, and latency. In Cloud infrastructures, PICs support high-speed optical interconnects, energy-efficient switching, and compact wavelength division multiplexing (WDM), addressing growing data demands. Fog computing, with its edge-focused processing and analytics, benefits from the compactness and low latency of integrated photonics for real-
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3

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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Анотація:
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
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4

Pasupuleti, Murali Krishna. "Photonic Neural Networks for Ultra-Fast, Energy-Efficient AI: A Paradigm Shift in Optical Computing and Machine Learning Architectures." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 07 (2025): 17–32. https://doi.org/10.62311/nesx/rpj2.

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Анотація:
Abstract: Photonic Neural Networks (PNNs) represent a transformative leap in artificial intelligence, offering ultra-fast processing speeds and significant energy efficiency compared to traditional electronic models. This research explores the architecture, implementation, and performance evaluation of PNNs as a paradigm shift in optical computing and machine learning. Using experimental simulations and predictive regression models, this study evaluates the potential of PNNs to outperform electronic neural networks in processing speed, power consumption, and scalability. Key findings indicate
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5

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 (2024): 202–9. http://dx.doi.org/10.1126/science.adl1203.

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Анотація:
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) en
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6

Liu, Jia, Shenghang Zhou, and Xiubao Sui. "Programmable Photonic Logic Array Based on Micro-Ring Resonators and All-Optical Modulation." Micromachines 16, no. 2 (2025): 238. https://doi.org/10.3390/mi16020238.

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Анотація:
All-optical computing is an emerging information processing technology. As a cutting-edge technology in the field of photonics, it effectively leverages the unique advantages of photons to achieve rapid computation. However, the lack of a fully functional and programmable design has slowed the progress of this type of optical computing system, especially in optical logic computing. In this paper, we design and propose a programmable photonic logic array based on all-optical computing methods. By efficiently combining on-chip photonic devices such as micro-ring resonators, we have realized a co
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7

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 (2023): 3139. http://dx.doi.org/10.3390/nano13243139.

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Анотація:
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 silic
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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 (2020): 2295–314. http://dx.doi.org/10.1515/nanoph-2020-0093.

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Анотація:
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 reali
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Dong, Bowei, Frank Brückerhoff-Plückelmann, Lennart Meyer, et al. "Partial coherence enhances parallelized photonic computing." Nature 632, no. 8023 (2024): 55–62. http://dx.doi.org/10.1038/s41586-024-07590-y.

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Анотація:
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
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Дисертації з теми "Photonic computing"

1

Cao, Yameng. "Semiconductor light sources for photonic quantum computing." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/56619.

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Анотація:
The isolation of qubits from decoherence is crucial to the prospect of building revolutionary quantum devices. This work is devoted to an optical study of the decoherence on spin qubits in self-assembled quantum dots. This thesis contributes towards a complete understanding of quantum decoherence, of which highlighted discoveries include bypassing the spectral diffusion in neutral quantum dot emission lines; observing for the first time the self-polarization phenomenon of nuclear spins, via the resonance-locking effect on a negatively charged quantum dot; and revealing the limiting factors on
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2

Talukder, Ria. "Computing with spiking photonic neural networks leveraging sparsity." Electronic Thesis or Diss., Besançon, Université Marie et Louis Pasteur, 2025. http://www.theses.fr/2025PAST2006.

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Анотація:
Les réseaux neuronaux à impulsions (Spiking Neural Networks, SNNs) sont des architectures neuromorphiques inspirées du cerveau biologique, offrant des avantages potentiels en termes d'efficacité énergétique et de rapidité grâce à l'exploitation de la parcimonie. Bien que les implémentations électroniques des SNNs basées sur la technologie CMOS soient prometteuses, elles rencontrent des défis en matière de passage à l’échelle et de parallélisme. En revanche, la photonique constitue une alternative puissante, tirant parti de la vitesse élevée des neurones photoniques excitables et de la propagat
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3

Birchall, Patrick Matthew. "Fundamental advantages and practicalities of quantum-photonic metrology and computing." Thesis, University of Bristol, 2018. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.752791.

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4

Vinckier, Quentin. "Analog bio-inspired photonic processors based on the reservoir computing paradigm." Doctoral thesis, Universite Libre de Bruxelles, 2016. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/237069.

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Анотація:
For many challenging problems where the mathematical description is not explicitly defined, artificial intelligence methods appear to be much more robust compared to traditional algorithms. Such methods share the common property of learning from examples in order to “explore” the problem to solve. Then, they generalize these examples to new and unseen input signals. The reservoir computing paradigm is a bio-inspired approach drawn from the theory of artificial Recurrent Neural Networks (RNNs) to process time-dependent data. This machine learning method was proposed independently by several res
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5

Denis-Le, Coarer Florian. "Neuromorphic computing using nonlinear ring resonators on a Silicon photonic chip." Electronic Thesis or Diss., CentraleSupélec, 2020. http://www.theses.fr/2020CSUP0001.

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Анотація:
Avec les volumes exponentiels de données numériques générées chaque jour, un besoin de traitement des données en temps réel et économe en énergie s'est fait sentir. Ces défis ont motivé la recherche sur le traitement non conventionnel de l'information. Parmi les techniques existantes, l'apprentissage machine est un paradigme très efficace de l'informatique cognitive. Il fournit, au travers de nombreuses implémentations dont celle des réseaux de neurones artificiels, un ensemble de techniques pour apprendre à un ordinateur ou un système physique à effectuer des tâches complexes, telles que la c
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6

Mwamsojo, Nickson. "Neuromorphic photonic systems for information processing." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS002.

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Анотація:
Par une utilisation performante de nombreux algorithmes dont les réseaux neuronaux, l'intelligence artificielle révolutionne le développement de la société numérique. Néanmoins, la tendance actuelle dépasse les limites prédites par la loi de Moore et celle de Koomey, ce qui implique des limitations éventuelles des implémentations numériques de ces systèmes. Pour répondre plus efficacement aux besoins calculatoires spécifiques de cette révolution, des systèmes physiques innovants tentent en amont d'apporter des solutions, nommées "neuro-morphiques" puisqu'elles imitent le fonctionnement des cer
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7

Alipour, Motaallem Seyed Payam. "Reconfigurable integrated photonic circuits on silicon." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51792.

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Анотація:
Integrated optics as a platform for signal processing offers significant benefits such as large bandwidth, low loss, and a potentially high degree of reconfigurability. Silicon (Si) has unique advantages as a material platform for integration, as well as properties such as a strong thermo-optic mechanism that allows for the realization of highly reconfigurable photonic systems. Chapter 1 is devoted to the discussion of these advantages, and Chapter 2 provides the theoretical background for the analysis of integrated Si-photonic devices. The thermo-optic property of Si, while proving extremely
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8

Mohamed, Abdalla Mohab Sameh. "Reservoir computing in lithium niobate on insulator platforms." Electronic Thesis or Diss., Ecully, Ecole centrale de Lyon, 2024. http://www.theses.fr/2024ECDL0051.

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Анотація:
Cette étude concerne le calcul par réservoir à retard temporel, en anglais Time-Delay Reservoir Computing (TDRC) dans les plateformes de photonique intégré, en particulier la plateforme Lithium Niobate On Insulator (LNOI). Nous proposons une nouvelle architecture intégrée « tout optique », avec seulement un déphaseur comme paramètre modifiable pouvant atteindre de bonnes performances sur plusieurs tâches de référence de calcul par réservoir. Nous étudions également l'espace de conception de cette architecture et le fonctionnement asynchrone du TDRC, qui s'écarte du cadre plus courant consistan
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9

Baylon, Fuentes Antonio. "Ring topology of an optical phase delayed nonlinear dynamics for neuromorphic photonic computing." Thesis, Besançon, 2016. http://www.theses.fr/2016BESA2047/document.

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Анотація:
Aujourd'hui, la plupart des ordinateurs sont encore basés sur des concepts développés il y a plus de 60 ans par Alan Turing et John von Neumann. Cependant, ces ordinateurs numériques ont déjà commencé à atteindre certaines limites physiques via la technologie de la microélectronique au silicium (dissipation, vitesse, limites d'intégration, consommation d'énergie). Des approches alternatives, plus puissantes, plus efficaces et moins consommatrices d'énergie, constituent depuis plusieurs années un enjeu scientifique majeur. Beaucoup de ces approches s'inspirent naturellement du cerveau humain, d
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Thraskias, Christos A., Eythimios N. Lallas, Niels Neumann, et al. "Survey of Photonic and Plasmonic Interconnect Technologies for Intra-Datacenter and High-Performance Computing Communications." Institute of Electrical and Electronics Engineers (IEEE), 2018. https://tud.qucosa.de/id/qucosa%3A35391.

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Анотація:
Large scale data centers (DC) and high performance computing (HPC) systems require more and more computing power at higher energy efficiency. They are already consuming megawatts of power, and a linear extrapolation of trends reveals that they may eventually lead to unrealistic power consumption scenarios in order to satisfy future requirements (e.g., Exascale computing). Conventional complementary metal oxide semiconductor (CMOS)-based electronic interconnects are not expected to keep up with the envisioned future board-to-board and chip-to-chip (within multi-chip-modules) interconnect requir
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Книги з теми "Photonic computing"

1

Brunner, Daniel, Miguel C. Soriano, and Guy Van der Sande, eds. Photonic Reservoir Computing. De Gruyter, 2019. http://dx.doi.org/10.1515/9783110583496.

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2

Nicolescu, Gabriela, Sébastien Le Beux, and Mahdi Nikdast. Photonic Interconnects for Computing Systems. River Publishers, 2022. http://dx.doi.org/10.1201/9781003339076.

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3

P, Hotaling Steven, Pirich Andrew R, and Society of Photo-optical Instrumentation Engineers., eds. Photonic quantum computing: 23-24 April 1997, Orlando, Florida. SPIE, 1997.

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4

P, Hotaling Steven, Pirich Andrew R, and Society of Photo-optical Instrumentation Engineers., eds. Photonic quantum computing II: 15-16 April 1998, Orlando, Florida. SPIE, 1998.

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5

Wang, Howard. Photonic Switches and Networks for High-Performance Computing and Data Centers. [publisher not identified], 2015.

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6

1966-, Iftekharuddin Khan M., Awwal Abdul A. S, and Society of Photo-optical Instrumentation Engineers., eds. Photonic devices and algorithms for computing: 22-23 July 1999, Denver, Colorado. SPIE, 1999.

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7

1966-, Iftekharuddin Khan M., Awwal Abdul A. S, and Society of Photo-optical Instrumentation Engineers., eds. Photonic devices and algorithms for computing II: 2-3 August 2000, San diego, USA. SPIE, 2000.

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8

1966-, Iftekharuddin Khan M., Awwal Abdul A. S, and Society of Photo-optical Instrumentation Engineers., eds. Photonic devices and algorithms for computing III: 29-30 July, 2001, San Diego, USA. SPIE, 2001.

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9

1966-, Iftekharuddin Khan M., Awwal Abdul A. S, and Society of Photo-optical Instrumentation Engineers., eds. Photonic devices and algorithms for computing VI: 2-3 August, 2004, Denver, Colorado, USA. SPIE, 2004.

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10

1966-, Iftekharuddin Khan M., Awwal Abdul A. S, Society of Photo-optical Instrumentation Engineers., and Boeing Company, eds. Photonic devices and algorithms for computing IV: 8-9 July, 2002, Seattle, Washington, USA. SPIE, 2002.

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Частини книг з теми "Photonic computing"

1

Binh, Le Nguyen. "Photonic Computing Processors." In Photonic Signal Processing. CRC Press, 2019. http://dx.doi.org/10.1201/9780429436994-4.

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2

Chaurasiya, Rohit, and Devanshi Arora. "Photonic Quantum Computing." In Quantum and Blockchain for Modern Computing Systems: Vision and Advancements. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04613-1_4.

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3

Easttom, Chuck. "Photonic Quantum Computing." In Hardware for Quantum Computing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-66477-9_3.

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4

Brunner, Daniel, Piotr Antonik, and Xavier Porte. "1. Introduction to novel photonic computing." In Photonic Reservoir Computing, edited by Daniel Brunner, Miguel C. Soriano, and Guy Van der Sande. De Gruyter, 2019. http://dx.doi.org/10.1515/9783110583496-001.

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5

Ortín, Silvia, Luis Pesquera, Guy Van der Sande, and Miguel C. Soriano. "5. Time delay systems for reservoir computing." In Photonic Reservoir Computing, edited by Daniel Brunner, Miguel C. Soriano, and Guy Van der Sande. De Gruyter, 2019. http://dx.doi.org/10.1515/9783110583496-005.

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Dambre, Joni. "2. Information processing and computation with photonic reservoir systems." In Photonic Reservoir Computing, edited by Daniel Brunner, Miguel C. Soriano, and Guy Van der Sande. De Gruyter, 2019. http://dx.doi.org/10.1515/9783110583496-002.

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Katumba, Andrew, Matthias Freiberger, Floris Laporte, et al. "3. Integrated on-chip reservoirs." In Photonic Reservoir Computing, edited by Daniel Brunner, Miguel C. Soriano, and Guy Van der Sande. De Gruyter, 2019. http://dx.doi.org/10.1515/9783110583496-003.

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8

Brunner, Daniel, Julian Bueno, Xavier Porte, Sheler Maktoobi, and Louis Andreoli. "4. Large scale spatiotemporal reservoirs." In Photonic Reservoir Computing, edited by Daniel Brunner, Miguel C. Soriano, and Guy Van der Sande. De Gruyter, 2019. http://dx.doi.org/10.1515/9783110583496-004.

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Larger, Laurent. "6. Ikeda delay dynamics as Reservoir processors." In Photonic Reservoir Computing, edited by Daniel Brunner, Miguel C. Soriano, and Guy Van der Sande. De Gruyter, 2019. http://dx.doi.org/10.1515/9783110583496-006.

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Van der Sande, Guy, and Miguel C. Soriano. "7. Semiconductor lasers as reservoir substrates." In Photonic Reservoir Computing, edited by Daniel Brunner, Miguel C. Soriano, and Guy Van der Sande. De Gruyter, 2019. http://dx.doi.org/10.1515/9783110583496-007.

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Тези доповідей конференцій з теми "Photonic computing"

1

Pernice, Wolfram H. P. "Probabilistic photonic computing." In AI and Optical Data Sciences VI, edited by Masaya Notomi and Tingyi Zhou. SPIE, 2025. https://doi.org/10.1117/12.3041804.

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2

Stabile, R., D. Feyisa, N. Calabretta, and B. Shi. "Photonic Switching and Computing Using InP Photonic Integration." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.w4d.6.

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Анотація:
We highlight advances, challenges and perspective for our work on InP photonic integrated switches and neural networks, in this rapidly evolving deep learning era, leveraging both a generic and an ultra-compact membrane integration technology.
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3

Shafiee, Amin, Linhong Chen, Sudeep Pasricha, Jie Yao, and Mahdi Nikdast. "Enabling Scalable Photonic Tensor Cores with Polarization-Domain Photonic Computing." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.w2a.42.

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Анотація:
We present a silicon-photonic tensor core using 2D ferroelectric materials to enable wavelength- and polarization-domain computing. Results, based on experimentally characterized material properties, show up to 83% improvement in computation accuracy compared to coherent networks.
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4

Youngblood, Nathan, Paolo Pintus, Mario Dumont, et al. "Non-reciprocal devices for in-memory photonic computing." In Frontiers in Optics. Optica Publishing Group, 2024. https://doi.org/10.1364/fio.2024.ftu1d.2.

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Анотація:
Non-reciprocal platforms can offer several key advantages for scalable and efficient photonic computing. In this talk, I will present our recent experimental work validating the use of non-reciprocal materials to implement high-endurance memory for photonic computing. Full-text article not available; see video presentation
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5

Youngblood, Nathan, Paolo Pintus, Mario Dumont, et al. "Non-Reciprocal Materials for Photonic in-Memory Computing." In Integrated Photonics Research, Silicon and Nanophotonics. Optica Publishing Group, 2024. https://doi.org/10.1364/iprsn.2024.itu2b.6.

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Анотація:
Non-reciprocal platforms can offer several key advantages for scalable and efficient photonic computing. In this talk, I will present our recent experimental work validating the use of non-reciprocal materials to implement high-endurance memory for photonic computing. Full-text article not available; see video presentation
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6

De Marinis, L., P. S. Kincaid, G. Contestabile, S. Gupta, and N. Andriolli. "Photonic Technologies for Analog Neuromorphic Computing." In 2024 IEEE Photonics Society Summer Topicals Meeting Series (SUM). IEEE, 2024. http://dx.doi.org/10.1109/sum60964.2024.10614512.

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Castro, Bernard J. Giron, Christophe Peucheret, and Francesco Da Ros. "Microring Resonator-based Photonic Reservoir Computing." In 2024 24th International Conference on Transparent Optical Networks (ICTON). IEEE, 2024. http://dx.doi.org/10.1109/icton62926.2024.10648245.

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8

Engheta, Nader. "Metamaterial Photonic Processing and Computing Machines." In 2024 IEEE INC-USNC-URSI Radio Science Meeting (Joint with AP-S Symposium). IEEE, 2024. http://dx.doi.org/10.23919/inc-usnc-ursi61303.2024.10632286.

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9

Chen, Zaijun. "Hypermultiplexed computing with photonic integrated circuits." In AI and Optical Data Sciences VI, edited by Masaya Notomi and Tingyi Zhou. SPIE, 2025. https://doi.org/10.1117/12.3052655.

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10

Xiao, Zian, Zhihao Ren, Yangyang Zhuge, et al. "Edge-Computing Enabled Si Photonics Multimodal Sensor with Integrated Photonic Convolutional Processor." In 2025 IEEE 38th International Conference on Micro Electro Mechanical Systems (MEMS). IEEE, 2025. https://doi.org/10.1109/mems61431.2025.10918045.

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Звіти організацій з теми "Photonic computing"

1

Hendry, Gilbert, Eric Robinson, Vitaliy Gleyzer, et al. Circuit-Switched Memory Access in Photonic Interconnection Networks for High-Performance Embedded Computing. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada532933.

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2

Pasupuleti, Murali Krishna. 2D Quantum Materials for Next-Gen Semiconductor Innovation. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi425.

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Анотація:
Abstract The emergence of two-dimensional (2D) quantum materials is revolutionizing next-generation semiconductor technology, offering superior electronic, optical, and quantum properties compared to traditional silicon-based materials. 2D materials, such as graphene, transition metal dichalcogenides (TMDs), hexagonal boron nitride (hBN), and black phosphorus, exhibit high carrier mobility, tunable bandgaps, exceptional mechanical flexibility, and strong light-matter interactions, making them ideal candidates for ultra-fast transistors, spintronics, optoelectronic devices, and quantum computin
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3

Hogle, Craig, Megan Ivory, Daniel Lobser, Brandon Ruzic, and Christopher DeRose. Three-Photon Optical Pumping for Trapped Ion Quantum Computing. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1854752.

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4

Bossler, Kerry. Coupled Electron-Photon Monte Carlo Radiation Transport for Next-Generation Computing Systems. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1474024.

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5

Pasupuleti, Murali Krishna. Scalable Quantum Networks: Entanglement-Driven Secure Communication. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi525.

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Анотація:
Abstract: Scalable quantum networks, powered by entanglement-driven secure communication, are poised to revolutionize global information exchange, cybersecurity, and quantum computing infrastructures. Unlike classical communication systems, quantum networks leverage quantum entanglement and superposition to enable ultra-secure data transmission, quantum key distribution (QKD), and instantaneous information sharing across large-scale networks. This research explores the fundamental principles of entanglement-based communication, the role of quantum repeaters, quantum memory, and multi-nodal ent
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6

Hemmer, Philip, and Robert Armstrong. Fractal-Enhancement of Photon Band-Gap Cavities for Quantum Computing and Other Applications. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada444845.

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7

Guha, Supratik, H. S. Philip Wong, Jean Anne Incorvia, and Srabanti Chowdhury. Future Directions Workshop: Materials, Processes, and R&D Challenges in Microelectronics. Defense Technical Information Center, 2022. http://dx.doi.org/10.21236/ad1188476.

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Анотація:
Microelectronics is a complex field with ever-evolving technologies and business needs, fueled by decades of continued fundamental materials science and engineering advancement. Decades of dimensional scaling have led to the point where even the name microelectronics inadequately describes the field, as most modern devices operate on the nanometer scale. As we reach physical limits and seek more efficient ways for computing, research in new materials may offer alternative design approaches that involve much more than electron transport e.g. photonics, spintronics, topological materials, and a
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8

Quinn, Jarus W. Optical Computing. Organization of the 1993 Photonics Science Topical Meetings Held in Palm Springs, California on March 16 - 19, 1993. Technical Digest Series, Volume 7. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada269025.

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