检索结果-内蒙古大学图书馆

Conference on Silicon photonics XIX

作者： Gorgulu, Kazim Vit, Aycan Deniz Amiri, Ali Najjar Magden, Emir Salih Koc Univ Dept Elect & Elect Engn TR-34450 Sariyer Istanbul Turkiye

ISBN: (纸本)9781510670433;9781510670426

We propose a deep photonic interferometer network architecture for designing fabrication-tolerant photonic devices. Our framework incorporates layers of variation-aware, custom-designed Mach-Zehnder interferometers and virtual wafer maps to optimize broadband power splitters under fabrication variations. Specifically, we demonstrate 50/50 splitters with below 1% deviation from the desired 50/50 ratio, even with up to 15 nm over-etch and under-etch variations. The significantly improved device performance under fabrication-induced changes demonstrates the effectiveness of the deep photonic network architecture in designing fabrication-tolerant photonic devices and showcases the potential for improving circuit performance by optimizing for expected variations in waveguide width.

关键词： photonic network fabrication-tolerant optimization silicon photonics

来源：评论

学校读者我要写书评

暂无评论

Deep photonic networks for on-chip dispersion control and optimization

Deep photonic networks for on-chip dispersion control and op...

引用

Smart photonic and Optoelectronic Integrated Circuits 2024

作者： Gorgulu, Kazim Amiri, Ali Najjar Vit, Aycan Deniz Magden, Emir Salih Department of Electrical and Electronics Engineering Koç University Sarıyer Istanbul34450 Turkey

ISBN: (纸本)9781510670402

In this study, we propose on-chip deep photonic networks with custom-designed Mach-Zehnder interferometers (MZI), allowing for the design of devices with arbitrarily defined dispersion profiles. We demonstrate a custom simulation and optimization framework to optimize dispersion and transmission profiles of these MZI-based networks. We experimentally show a proof-of-concept two-port photonic network with a highly nonintuitive, triangular dispersion profile in the C-band, while simultaneously achieving a flat band transmission with less than 0.7 dB insertion loss. We also demonstrate capabilities with multi-port photonic networks to enhance design freedom for customizable dispersion profiles, opening up new possibilities for on-chip dispersion engineering. © 2024 SPIE.

关键词： Mach-Zehnder interferometers

来源：评论

学校读者我要写书评

暂无评论

Towards universal polarization handling with silicon-based deep photonic networks 28

Towards universal polarization handling with silicon-based d...

引用

Integrated Optics: devices, Materials, and Technologies XXVIII 2024

作者： Vit, Aycan Deniz Najjar Amiri, Ali Gorgulu, Kazim Magden, Emir Salih Department of Electrical and Electronics Engineering Koc University Sariyer Istanbul34450 Turkey

ISBN: (纸本)9781510670389

We propose a novel methodology employing deep photonic networks comprising cascaded Mach-Zehnder Interferometers (MZIs) to illustrate the proficiency of on-chip polarization handling. By applying gradient-based optimization techniques to tailor specific phase profiles within successive layers of MZIs, we demonstrate the functionality of devices adept at power division in both polarization-dependent and polarization-independent modalities. In silico simulations underscore the cutting-edge performance metrics achieved, encompassing a bandwidth exceeding 120 nm centered at 1550 nm, an extinction ratio surpassing 15 dB, and transmission bands characterized by flat-top profiles. These results prove the comprehensive capabilities of our deep photonic network ecosystem in polarization management, thereby unveiling promising prospects for advanced optical applications necessitating versatile polarization handling capabilities. © 2024 SPIE.

关键词： photonic devices

来源：评论

学校读者我要写书评

暂无评论

Introduction to the photonic networks and devices (networks) Special Issue

引用

JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING 2020年第4期12卷 NET1-NET2页

作者： Furdek, Marija Caplan, David Fiorentino, Marco Chalmers Univ Technol Dept Elect Engn S-41296 Gothenburg Sweden MIT Lincoln Lab 244 Wood St Lexington MA 02421 USA Hewlett Packard Enterprise 1501 Page Mill Rd Palo Alto CA 94304 USA

This special issue comprises extended versions of some of the top-scored papers that were presented at the OSA photonic networks and devices (networks) meeting that was part of the OSA Advanced photonics Congress held in Burlingame, California, USA, July 29-August 1, 2019. Here, we highlight relevant topics from included papers relating to photonic communication network development. (C) 2020 Optical Society of America.

关键词： Extinction ratios Modulation techniques Passive optical networks Quantum key distribution Radio over fiber Space division multiplexing

来源：评论

学校读者我要写书评

暂无评论

Multiobjective and categorical global optimization of photonic structures based on ResNet generative neural networks

引用

NANOphotonicS 2021年第1期10卷 361-369页

作者： Jiang, Jiaqi Fan, Jonathan A. Stanford Univ Dept Elect Engn 348 Via Pueblo Stanford CA 94305 USA

We show that deep generative neural networks, based on global optimization networks (GLOnets), can be configured to perform the multiobjective and categorical global optimization of photonic devices. A residual network scheme enables GLOnets to evolve from a deep architecture, which is required to properly search the full design space early in the optimization process, to a shallow network that generates a narrow distribution of globally optimal devices. As a proof-of-concept demonstration, we adapt our method to design thin-film stacks consisting of multiple material types. Benchmarks with known globally optimized antireflection structures indicate that GLOnets can find the global optimum with orders of magnitude faster speeds compared to conventional algorithms. We also demonstrate the utility of our method in complex design tasks with its application to incandescent light filters. These results indicate that advanced concepts in deep learning can push the capabilities of inverse design algorithms for photonics.

关键词： categorical optimization global optimization multiobjective optimization neural networks thin-film stack

来源：评论

学校读者我要写书评

暂无评论

Towards silicon photonic neural networks for artificial intelligence

引用

Science China(Information Sciences) 2020年第6期63卷 105-118页

作者： Bowen BAI Haowen SHU Xingjun WANG Weiwen ZOU State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Engineering and Computer Science Peking University Frontiers Science Center for Nano-optoelectronics Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks Intelligent Microwave Lightwave Integration Innovation Center(iMLic) Department of Electronic Engineering Shanghai Jiao Tong University

Brain-inspired photonic neural networks for artificial intelligence have attracted renewed interest. For many computational tasks, such as image recognition, speech processing and deep learning, photonic neural networks have the potential to increase the computing speed and energy efficiency on the orders of magnitude compared with digital electronics. Silicon photonics, which combines the advantages of electronics and photonics, brings hope for the large-scale photonic neural network integration. This paper walks through the basic concept of artificial neural networks and focuses on the key devices which construct the silicon photonic neuromorphic systems. We review some recent important progress in silicon photonic neural networks, which include multilayer artificial neural networks and brain-like neuromorphic systems, for artificial intelligence. A prototype of silicon photonic artificial intelligence processor for ultra-fast neural network computing is also proposed. We hope this paper gives a detailed overview and a deeper understanding of this emerging field.

关键词： photonic neural networks artificial intelligence deep learning photonic computing accelerator silicon photonics

来源：评论

学校读者我要写书评

暂无评论

photonic-aware Neural networks for Packet Classification in beyond 5G networks 13

Photonic-aware Neural Networks for Packet Classification in ...

引用

13th International Conference on the Network of the Future, NoF 2022

作者： Paolini, Emilio Civerchia, Federico De Marinis, Lorenzo Valcarenghi, Luca Maggiani, Luca Andriolli, Nicola Scuola Superiore sant'Anna Pisa56124 Italy TeCIP Institute Departments of Excellence Robotics & AI Italy National Research Council of Italy CNR-IEIIT Pisa56122 Italy Sma-RTy Italiat SRL Carugate20061 Italy

ISBN: (数字)9781665472548

ISBN: (纸本)9781665472548

The benefits introduced by novel network technologies such as 5G and beyond, including low latency and support for billions of devices, have the potential to transform the lives of people. However, the features promised by these new technologies have also attracted malicious actors, with various motivations for attacking the network infrastructure, from cybercrime-based frauds to political goals. Thus, to enable the full potential of the emerging network technologies, it is necessary to take into accounts these attacks and develop tailored countermeasures. One future direction in mitigating the risks of potential attacks is the automatic classification of malicious packets, with the possibility to drop them if classified in the attack category. Hence, in this context, we propose a solution based on Neural networks (NNs) to automatically classify packets into two classes, i.e., benign and attack, directly in the Radio Access Network (RAN), specifically inspecting packets when they are relayed at the next generation eNB (gNB)-Central Unit (CU) level. Since NNs can be computationally intensive algorithms, potentially increasing the latency of the network, we decide to leverage photonic-Aware Neural Network (PANN), photonic accelerators able to perform NN computations in the analog optical domain and with time-of-flight latency. We devised two different PANN architectures, considering different photonic constraints. The classification performance of the two architectures has been assessed on the CICIDS-2017 dataset and compared with electronic counterparts. Results proved that the F1-score loss due to underlying hardware constraints is negligible, paving the way for PANN applications in next generation networks. © 2022 IEEE.

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

Tackling multimodal device distributions in inverse photonic design using invertible neural networks

引用

MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2023年第2期4卷 02LT02页

作者： Frising, Michel Bravo-Abad, Jorge Prins, Ferry Autonomous Univ Madrid Condensed Matter Phys Ctr IFIMAC Madrid 28049 Spain Autonomous Univ Madrid Dept Condensed Matter Phys Madrid 28049 Spain Autonomous Univ Madrid Dept Theoret Condensed Matter Phys Madrid 28049 Spain

We show how conditional generative neural networks can be used to efficiently find nanophotonic devices with desired properties, also known as inverse photonic design. Machine learning has emerged as a promising approach to overcome limitations imposed by the dimensionality and topology of the parameter space. Importantly, traditional optimization routines assume an invertible mapping between the design parameters and response. However, different designs may have comparable or even identical performance confusing the optimization algorithm when performing inverse design. Our generative modeling approach provides the full distribution of possible solutions to the inverse design problem, including multiple solutions. We compare a commonly used conditional variational autoencoder (cVAE) and a conditional invertible neural network (cINN) on a proof-of-principle nanophotonic problem, consisting in tailoring the transmission spectrum trough a metallic film milled by subwavelength indentations. We show how cINNs have superior flexibility compared to cVAEs when dealing with multimodal device distributions.

关键词： nanotechnology inverse design multimodality machine learning invertible neural networks nanophotonics generative modeling

来源：评论

学校读者我要写书评

暂无评论

Heterogeneous integration of single InAs/InP quantum dots with the SOI chip using direct bonding

引用

OPTICS EXPRESS 2024年第7期32卷 10874-10886页

作者： Burakowski, Marek Holewa, Pawel Mrowinski, Pawel Sakanas, Aurimas Musial, Anna Sek, Grzegorz Yvind, Kresten Semenova, Elizaveta Syperek, Marcin Wroclaw Univ Sci & Technol Fac Fundamental Problems Technol Dept Expt Phys Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland Tech Univ Denmark DTU Electro DK-2800 Lyngby Denmark Tech Univ Denmark NanoPhoton Ctr Nanophoton DK-2800 Lyngby Denmark

Quantum information processing with photons in small -footprint and highly integrated silicon -based photonic chips requires incorporating non -classical light sources. In this respect, self -assembled III -V semiconductor quantum dots (QDs) are an attractive solution, however, they must be combined with the silicon platform. Here, by utilizing the large -area direct bonding technique, we demonstrate the hybridization of InP and SOI chips, which allows for coupling single photons to the SOI chip interior, offering cost-effective scalability in setting up a multi -source environment for quantum photonic chips. We fabricate devices consisting of self -assembled InAs QDs embedded in the tapered InP waveguide (WG) positioned over the SOI-defined Si WG. Focusing on devices generating light in the telecom C -band compatible with the low -loss optical fiber networks, we demonstrate the light coupling between InP and SOI platforms by observing photons outcoupled at the InP-made circular Bragg grating outcoupler fabricated at the end of an 80 pm -long Si WG, and at the cleaved edge of the Si WG. Finally, for a device with suppressed multi -photon generation events exhibiting 80 % single photon generation purity, we measure the photon number outcoupled at the cleaved facet of the Si WG. We estimate the directional on -chip photon coupling between the source and the Si WG to 5.1 %.

关键词： Fiber networks Optical fibers Optical networks Quantum dots Quantum light sources Wavelength division multiplexing

来源：评论

学校读者我要写书评

暂无评论

Opportunities for integrated photonic neural networks

引用

NANOphotonicS 2020年第13期9卷 4221-4232页

作者： Stark, Pascal Horst, Folkert Dangel, Roger Weiss, Jonas Offrein, Bert Jan IBM Res Zurich Saumerstr 4 CH-8803 Ruschlikon Switzerland

photonics 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 worldoads. 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.

关键词： integrated optics optical signal processing photonic neural networks photonic reservoir computing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：