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Co‑packaged optics(CPO):status,challenges,and solutions

Frontiers of Optoelectronics 2023年第1期16卷 1-40页

作者： Min Tan Jiang Xu Siyang Liu Junbo Feng Hua Zhang Chaonan Yao Shixi Chen Hangyu Guo Gengshi Han Zhanhao Wen Bao Chen Yu He Xuqiang Zheng Da Ming Yaowen Tu Qiang Fu Nan Qi Dan Li Li Geng Song Wen Fenghe Yang Huimin He Fengman Liu Haiyun Xue Yuhang Wang Ciyuan Qiu Guangcan Mi Yanbo Li Tianhai Chang Mingche Lai Luo Zhang Qinfen Hao Mengyuan Qin School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong KongChina HKUST Fok Ying Tung Research Institute Guangzhou 511462China The Hong Kong University of Science and Technology(Guangzhou) Guangzhou 511462China Chongqing United Micro-Electronics Center(CUMEC) Chongqing 401332China Hisense Broadband Multimedia Technologies Co. Ltd.Qingdao 266000China Institute of Microelectronics Chinese Academy of SciencesBeijing 100029China State Key Laboratory of Superlattices and Microstructures Institute of SemiconductorsChinese Academy of SciencesBeijing 100083China School of Microelectronics Xi’an Jiaotong UniversityXi’an 710049China Zhangjiang Laboratory Shanghai 201210China The State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China Huawei Technologies Co. Ltd.Shenzhen 440307China College of Computer National University of Defense TechnologyChangsha 410073China Institute of Computing Technology Chinese Academy of SciencesBeijing 100086China

Due to the rise of 5G,IoT,AI,and high-performance computing applications,datacenter trafc has grown at a compound annual growth rate of nearly 30%.Furthermore,nearly three-fourths of the datacenter trafc resides within *** conventional pluggable optics increases at a much slower rate than that of datacenter *** gap between application requirements and the capability of conventional pluggable optics keeps increasing,a trend that is *** optics(CPO)is a disruptive approach to increasing the interconnecting bandwidth density and energy efciency by dramatically shortening the electrical link length through advanced packaging and co-optimization of electronics and *** is widely regarded as a promising solution for future datacenter interconnections,and silicon platform is the most promising platform for large-scale *** international companies(e.g.,Intel,Broadcom and IBM)have heavily investigated in CPO technology,an inter-disciplinary research feld that involves photonic devices,integrated circuits design,packaging,photonic device modeling,electronic-photonic co-simulation,applications,and *** review aims to provide the readers a comprehensive overview of the state-of-the-art progress of CPO in silicon platform,identify the key challenges,and point out the potential solutions,hoping to encourage collaboration between diferent research felds to accelerate the development of CPO technology.

关键词： Co-packaged optics Silicon photonics High-performance computing Advanced packaging External laser Optical power delivery Co-simulation Standardization Transmitter Receiver

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Non-Hermitian Thermophotonic Funneling via Nonreciprocal Surface Waves

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Physical Review Letters 2025年第19期134卷 196901-196901页

作者： Shuihua Yang Guoqiang Xu Chenglong Zhou Mengqi Liu Lei Qu Jianfeng Chen Jiaxin Li Jing Wu Zhipeng Li Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of Singapore Kent Ridge 117583 Republic of Singapore School of Electronic Science & Engineering Southeast University Nanjing 211189 China School of Energy Science and Engineering Harbin Institute of Technology Harbin 150001 China Institute of Engineering Thermophysics MOE Key Laboratory for Power Machinery and Engineering School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China Nanotech Energy and Environment Platform National University of Singapore Suzhou Research Institute Suzhou 215123 China

Non-Hermitian photonics revolutionizes the understanding and manipulation of wave propagation in open systems. However, due to the interplay of non-Hermitian light-matter interactions and complex long-range couplings, miniaturizing topological features into deep-subwavelength regimes remains a significant challenge, and state-of-the-art explorations have thus far remained on the reciprocal topological photonic states. Here, we introduce nonreciprocal surface waves into deep-subwavelength dimerized lattices to theoretically demonstrate an efficient thermophotonic funnel. The system comprises an array of silicon carbide nanoparticles coupled with a graphene substrate. Under electrical bias, the graphene substrate enables the breaking of time-reversal symmetry in a magnetic-free and compact configuration, while also providing additional channels for photonic-based heat flow. The synergy between nonreciprocal surface waves and collective thermophotonic interactions drives all eigenstates to collapse toward the truncation of the lattice, exhibiting up to a 278-fold enhancement in one-way radiative field intensity. The topological fingerprints of this funneling effect are characterized by point-gap topology and complex-eigenspectrum braiding. Our findings bridge non-Hermitian physics and nonreciprocal thermophotonics, unlocking new possibilities for topological applications.

关键词： Silicon carbide

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In situ control and nanofocusing of extreme ultraviolet solid-state high harmonics

In situ control and nanofocusing of extreme ultraviolet soli...

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International Conference on Ultrafast Phenomena, UP 2022

作者： Korobenko, Aleksey Rashid, Sabaa Heide, Christian Naumov, Andrei Yu. Reis, David A. Berini, Pierre Corkum, Paul B. Vampa, Giulio Joint Attosecond Science Laboratory National Research Council of Canada University of Ottawa Ottawa Canada Center for Research in Photonics University of Ottawa Ottawa Canada Stanford PULSE Institute SLAC National Accelerator Laboratory Menlo ParkCA94025 United States School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada Department of Physics University of Ottawa Ottawa Canada

In this work we use helium ion beam nanolithography to structure the surface of an MgO crystal and control the emission of extreme-ultraviolet high harmonics. We demonstrate their focusing to below 300 nm in waist dia... 详细信息

ISBN: (纸本)9781957171074

关键词： Crystal structure

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Dynamic Online Modulation Recognition using Incremental Learning

arXiv

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arXiv 2023年

作者： Owfi, Ali Abbasi, Ali Afghah, Fatemeh Ashdown, Jonathan Turck, Kurt Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC United States Computer Science Department Vanderbilt University NashvilleTN United States Air Force Research Laboratory RomeNY United States

Modulation recognition is a fundamental task in communication systems as the accurate identification of modulation schemes is essential for reliable signal processing, interference mitigation for coexistent communication technologies, and network optimization. Incorporating deep learning (DL) models into modulation recognition has demonstrated promising results in various scenarios. However, conventional DL models often fall short in online dynamic contexts, particularly in class incremental scenarios where new modulation schemes are encountered during online deployment. Retraining these models on all previously seen modulation schemes is not only time-consuming but may also not be feasible due to storage limitations. On the other hand, training solely on new modulation schemes often results in catastrophic forgetting of previously learned classes. This issue renders DL-based modulation recognition models inapplicable in real-world scenarios because the dynamic nature of communication systems necessitate the effective adaptability to new modulation schemes. This paper addresses this challenge by evaluating the performance of multiple Incremental Learning (IL) algorithms in dynamic modulation recognition scenarios, comparing them against conventional DL-based modulation recognition. Our results demonstrate that modulation recognition frameworks based on IL effectively prevent catastrophic forgetting, enabling models to perform robustly in dynamic scenarios. © 2023, CC BY.

关键词： Deep learning

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Utilization of Artifact and Noise Affected Electrocardiogram for Simultaneous Heart Rate Computation and Motion Type Inference

Utilization of Artifact and Noise Affected Electrocardiogram...

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IEEE SENSORS

作者： Jihwan Kim Hyun Bin Kim Dengyang Lu Byeong Woon Lee Wooseok Kim Sang Uk Park Hee Kyu Lee Jaejun Lim Yida Wang Seunghwan Seo Sang Min Won Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea Department of Materials Science and Engineering University of Pennsylvania Philadelphia PA USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge MA USA

The specific objective of developing neural interfaces is to analyze the electrophysiological signals associated with neural activities within the body, with the aim of providing early diagnosis for diseases related to the neural system. One example is the electrocardiogram (ECG) that assesses the functionality of the heart, identifying arrhythmias and potentially pinpointing the location of any malfunction. Conventional clinical protocols require cardiac electrical signal measurements to be conducted during a stationary phase, with an emphasis on minimizing noise interference as much as possible. While this holds true in clinical scenarios, such motion artifacts can be valuable sources of information for motion tracking. In this study, we present a methodology where we demonstrate the simultaneous monitoring and computation of both heart rate and motion using a wireless ECG sensing system with a three-electrode configuration.

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Deep Subspace Encoders for Nonlinear System Identification

arXiv

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arXiv 2022年

作者： Beintema, Gerben I. Schoukens, Maarten Toth, Roland Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Using Artificial Neural Networks (ANN) for nonlinear system identification has proven to be a promising approach, but despite of all recent research efforts, many practical and theoretical problems still remain open. Specifically, noise handling and models, issues of consistency and reliable estimation under minimisation of the prediction error are the most severe problems. The latter comes with numerous practical challenges such as explosion of the computational cost in terms of the number of data samples and the occurrence of instabilities during optimization. In this paper, we aim to overcome these issues by proposing a method which uses a truncated prediction loss and a subspace encoder for state estimation. The truncated prediction loss is computed by selecting multiple truncated subsections from the time series and computing the average prediction loss. To obtain a computationally efficient estimation method that minimizes the truncated prediction loss, a subspace encoder represented by an artificial neural network is introduced. This encoder aims to approximate the state reconstructability map of the estimated model to provide an initial state for each truncated subsection given past inputs and outputs. By theoretical analysis, we show that, under mild conditions, the proposed method is locally consistent, increases optimization stability, and achieves increased data efficiency by allowing for overlap between the subsections. Lastly, we provide practical insights and user guidelines employing a numerical example and state-of-the-art benchmark results. © 2022, CC BY.

关键词： Forecasting

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High-frequency terahertz waves disrupt Alzheimer’s β-amyloid fibril formation

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eLight 2023年第1期3卷 85-93页

作者： Wenyu Peng Zhi Zhu Jing Lou Kun Chen Yuanming Wu Chao Chang Department of Biochemistry and Molecular Biology Shaanxi Provincial Key Laboratory of Clinic GeneticsSchool of Basic MedicineAir Force Medical UniversityXi’an 710032China Innovation Laboratory of Terahertz Biophysics National Innovation Institute of Defense TechnologyBeijing 100071China Key Laboratory of Optical Technology and Instrument for Medicine Ministry of EducationCollege of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Department of Anatomy Histology and Embryology and K.K.Leung Brain Research CentreSchool of Basic MedicineAir Force Medical UniversityXi’an 710032China School of Physics Peking UniversityBeijing 100871China

The accumulation and deposition of amyloid can cause a variety of neurodegenerative diseases,including Alzheimer’s and Parkinson’s *** degradation or clearance of this accumulation is currently the most widely accepted therapeutic strategy for intervention in these *** study on amyloid-β(Aβ)oligomers in vitro revealed that high-frequency terahertz(THz)waves at a specific frequency of 34.88 THz could serve as a physical,efficient,non-thermal denaturation technique to delay the fibrotic process by 80%,as monitored by a thioflavine T(ThT)binding assay and Fourier transform infrared(FTIR)***,THz waves of this frequency have been shown to have no side effects on normal cells,as confirmed by cell viability and mitochondrial membrane potential ***,molecular dynamic(MD)simulations revealed that the THz waves could resonate with Aβ fibrils,disrupting the dense conformation by breaking the β-sheet structure and promoting the formation of abundant coil and bend *** study uses the amyloid of Aβ as an example,and the results will further guide interventions for the accumulation of other amyloids,which may provide new ideas for the remission of related diseases.

关键词： amyloid Alzheimer Aβ

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Enhancing Real-World Active Speaker Detection with Multi-Modal Extraction Pre-Training

arXiv

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arXiv 2024年

作者： Tao, Ruijie Qian, Xinyuan Das, Rohan Kumar Gao, Xiaoxue Wang, Jiadong Li, Haizhou The Department of Electrical and Computer Engineering National University of Singapore Singapore The Department of Computer Science and Technology University of Science and Technology Beijing Beijing100083 China Fortemedia Singapore138637 Singapore Institute for Infocomm Research A*STAR Singapore The Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big data Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore The University of Bremen 28359 Germany

Audio-visual active speaker detection (AV-ASD) aims to identify which visible face is speaking in a scene with one or more persons. Most existing AV-ASD methods prioritize capturing speech-lip correspondence. However, there is a noticeable gap in addressing the challenges from real-world AV-ASD scenarios. Due to the presence of low-quality noisy videos in such cases, AV-ASD systems without a selective listening ability are short of effectively filtering out disruptive voice components from mixed audio inputs. In this paper, we propose a Multi-modal Speaker Extraction-to-Detection framework named ‘MuSED’, which is pre-trained with audio-visual target speaker extraction to learn the denoising ability, then it is fine-tuned with the AV-ASD task. Meanwhile, to better capture the multi-modal information and deal with real-world problems such as missing modality, MuSED is modelled on the time domain directly and integrates the multi-modal plus-and-minus augmentation strategy. Our experiments demonstrate that MuSED substantially outperforms the state-of-the-art AV-ASD methods and achieves 95.6% mAP on the AVA-ActiveSpeaker dataset, 98.3% AP on the ASW dataset, and 97.9% F1 on the Columbia AV-ASD dataset, respectively. We will publicly release the code in due course. © 2024, CC BY.

关键词： Extraction

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Traffic Signal Optimization by Integrating Reinforcement Learning and Digital Twins

Traffic Signal Optimization by Integrating Reinforcement Lea...

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Smart World Congress (SWC), IEEE

作者： Vijayalakshmi K Kumarasamy Abhilasha Jairam Saroj Yu Liang Dalei Wu Michael P. Hunter Angshuman Guin Mina Sartipi Department of Computer Science and Engineering University of Tennessee at Chattanooga USA Applied Research for Mobility Systems Oak Ridge National Laboratory USA School of Civil and Environmental Engineering Georgia Institute of Technology USA

Machine learning (ML) methods, especially reinforcement learning (RL), have been widely considered for traffic signal optimization in intelligent transportation systems. Most of these ML methods are centralized, lacking in scalability and adaptability in large traffic networks. Further, it is challenging to train such ML models due to the lack of training platforms and/or the cost of deploying and training in a real traffic networks. This paper presents an approach for the integration of decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin (DT) to optimize traffic signals for the reduction of traffic congestion and network-wide fuel consumption related to stopping. Specifically, the DGMARL agents learn traffic state patterns and make decisions regarding traffic signal control with assistance from a Digital Twin module, which simulates and replicates the traffic behaviors of a real traffic network. The proposed approach was evaluated using PTV-Vissim [1], a microscopic traffic simulation platform. PTV-Vissim is also the simulation engine of the DT, enabling emulation and optimization of the traffic signals on the MLK Smart Corridor in Chattanooga, Tennessee. Compared to an actuated signal control baseline approach, experiment results show that Eco_PI, a developed performance measure capturing the impact of stops on fuel consumption, was reduced by 44.27% in a 24-hour and an average of 29.88% in a PM-peak-hour scenario.

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Modeling the Self-Capacitance of Individual Plates in a Multi-Conductor System using PEEC 5

Modeling the Self-Capacitance of Individual Plates in a Mult...

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5th IEEE research and Applications of Photonics in Defense Conference, RAPID 2022

作者： Wolf, Henry Striker, Ryan Cleveland, Jerika Gieser, Anna Braaten, Benjamin D. Enochson, Avery Ewert, Daniel Mitra, Dipankar Allen, Jeffery Allen, Monica Vitola, Sabina North Dakota State University Department of Electrical and Computer Engineering Fargo United States Program of Minnesota State University Department of Integrated Engineering at Iron Range Engineering MankatoMN United States University of Wisconsin - la Crosse Department of Computer Science and Computer Engineering La CrosseWI United States Eglin Air Force Base Eglin Afb Air Force Research Laboratory Munitions Directorate FL32542 United States Minnesota State University Bell Program Department of Integrated Engineering MankatoMN United States

ISBN: (数字)9781665402231

ISBN: (纸本)9781665402231

In many areas of electronics design, it is necessary to understand the different aspects of capacitance associated with various conducting surfaces in a particular layout. This is because as operating frequencies increase and dimensions decrease, capacitive coupling can become the dominant means in which noise is induced in a design. Therefore, having the ability to extract an equivalent circuit for modeling capacitive coupling and understanding the coupling can be very important, and challenging. This paper presents insight on capacitive coupling by evaluating the capacitance between two parallel plates, with particular attention paid to the self-capacitance (with a reference at infinity) of each of the individual plates. More specifically, the Partial Element Equivalent Circuit (PEEC) method is used to compute the self-capacitance of each individual plate in the parallel plate capacitor problem, and results are verified by comparison to values from James C. Maxwell's original works and the electrostatic solver ANSYS Maxwell 3D. Overall, it is shown how the self-capacitance of each individual plate changes as a function of distance between the parallel plates. © 2022 IEEE.

关键词： Capacitance

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