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Recent progress of integrated circuits and optoelectronic chips

Science China(Information Sciences) 2021年第10期64卷 103-135页

作者： Yue HAO Shuiying XIANG Genquan HAN Jincheng ZHANG Xiaohua MA Zhangming ZHU Xingxing GUO Yahui ZHANG Yanan HAN Ziwei SONG Yan LIU Ling YANG Hong ZHOU Jiangyi SHI Wei ZHANG Min XU Weisheng ZHAO Biao PAN Yangqi HUANG Qi LIU Yimao CAI Jian ZHU Xin OU Tiangui YOU Huaqiang WU Bin GAO Zhiyong ZHANG Guoping GUO Yonghua CHEN Yong LIU Xiangfei CHEN Chunlai XUE Xingjun WANG Lixia ZHAO Xihua ZOU Lianshan YAN Ming LI School of Microelectronics Xidian University State Key Laboratory of Integrated Service Networks Xidian University National Integrated Circuit Innovation Center School of Microelectronics Fudan University School of Integrated Circuit Science and Engineering Beihang University China Center for Information Industry Development Frontier Institute of Chip and System Fudan University Institute of Microelectronics Peking University Nanjing Electronic Devices Institute State Key Laboratory of Functional Material for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Institute of Microelectronics Tsinghua University Center for Carbon-based Electronics Department of Electronics Peking University CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Origin Quantum Computing Company Limited Key Laboratory of Flexible Electronics & Institute of Advanced Materials Nanjing Tech University State Key Laboratory of Electronic Thin Films and Integrated Devices School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China National Laboratory of Solid State Microstructures & College of Engineering and Applied Sciences Nanjing University State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University School of Electrical and Electronic Engineering Tiangong University Center for Information Photonics and Communications School of Information Science and TechnologySouthwest Jiaotong University School of Electronic Electrical and Communication Engineering

Integrated circuits(ICs) and optoelectronic chips are the foundation stones of the modern information society. The IC industry has been driven by the so-called "Moore's law" in the past 60 years, and now has entered the post Moore's law era. In this paper, we review the recent progress of ICs and optoelectronic chips. The research status, technical challenges and development trend of devices, chips and integrated technologies of typical IC and optoelectronic chips are focused on. The main contents include the development law of IC and optoelectronic chip technology, the IC design and processing technology, emerging memory and chip architecture, brain-like chip structure and its mechanism, heterogeneous integration, quantum chip technology, silicon photonics chip technology, integrated microwave photonic chip, and optoelectronic hybrid integrated chip.

关键词： integrated circuit photonic neural computing semiconductor science and technology optoelectronic device and chip photonic integrated circuit wide bandgap semiconductors silicon photonics hybrid integration quantum chip integrated microwave photonic

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Enhancement of sensitivity near exceptional points in dissipative qubit-resonator systems

arXiv

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arXiv 2025年

作者： Han, Pei-Rong Wu, Fan Huang, Xin-Jie Wu, Huai-Zhi Zou, Chang-Ling Yi, Wei Zhang, Mengzhen Li, Hekang Xu, Kai Zheng, Dongning Fan, Heng Wen, Jianming Yang, Zhen-Biao Zheng, Shi-Biao School of Physics and Mechanical and Electrical Engineering Longyan University Longyan364012 China Fujian Key Laboratory of Quantum Information and Quantum Optics College of Physics and Information Engineering Fuzhou University Fuzhou350108 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Institute of Physics Chinese Academy of Sciences Beijing100190 China CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering Binghamton University BinghamtonNY13902 United States Department of Physics Kennesaw State University MariettaGA30060 United States

Dissipation usually plays a negative role in quantum metrological technologies, which aim to improve measurement precision by leveraging quantum effects that are vulnerable to environment-induced decoherence. Recently, it has been demonstrated that dissipation can actually be used as a favorable resource for enhancing the susceptibility of signal detection. However, demonstrations of such enhancement for detecting physical quantities in open quantum systems are still lacking. Here we propose and demonstrate a protocol for realizing such non-Hermitian quantum sensors for probing the coupling between a qubit and a resonator subjecting to energy dissipations. The excitation-number conversion associated with the no-jump evolution trajectory enables removal of the noisy outcomes with quantum jumps, implementing the exceptional point (EP), where the Rabi splitting exhibits a divergent behavior in response to a tiny variation of the effective coupling. The sensitivity enhancement near the EP is confirmed by both theoretical calculation and experimental measurement. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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A novel deep learning method for textual sentiment analysis

arXiv

引用

arXiv 2021年

作者： Sadr, Hossein Solimandarabi, Mozhdeh Nazari Pedram, Mir Mohsen Teshnehlab, Mohammad Department of Computer Engineering Rasht Branch Islamic Azad University Rasht Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Industrial Control Center of Excellence Faculty of Electrical and Computer Engineering K. N. Toosi University Tehran Iran

Sentiment analysis is known as one of the most crucial tasks in the field of natural language processing and Convolutional Neural Network (CNN) is one of those prominent models that is commonly used for this aim. Although convolutional neural networks have obtained remarkable results in recent years, they are still confronted with some limitations. Firstly, they consider that all words in a sentence have equal contributions in the sentence meaning representation and are not able to extract informative words. Secondly, they require a large number of training data to obtain considerable results while they have many parameters that must be accurately adjusted. To this end, a convolutional neural network integrated with a hierarchical attention layer is proposed which is able to extract informative words and assign them higher weight. Moreover, the effect of transfer learning that transfers knowledge learned in the source domain to the target domain with the aim of improving the performance is also explored. Based on the empirical results, the proposed model not only has higher classification accuracy and can extract informative words but also applying incremental transfer learning can significantly enhance the classification performance. © 2021, CC BY.

关键词： Sentiment analysis

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Ensuring Privacy and Correlation Awareness in Multi-Dimensional Service Quality Prediction and Recommendation for Iot

SSRN

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SSRN 2024年

作者： Zhong, Weiyi Fang, Wei Zhao, Yifan Wang, Sifeng Yan, Chao Jiang, Rong Khan, Maqbool Yang, Xuan Rafique, Wajid Qufu Normal University School of Engineering Rizhao276800 China Qufu Normal University School of Computer Science Rizhao276800 China Weifang University of Science and Technology Weifang262700 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650000 China Software Competence Center Hagenberg Upper Austria 4232 Austria Angeles University Foundation Pampanga2009 Philippines University of Calgary Department of Electrical and Software Engineering CalgaryT2N 1N4 Canada

Edge computing, with its advantages in terms of lightweight data transmission between users and cloud platforms, has become a promising solution for alleviating the heavy burden of timely data processing in many IoT scenarios, such as smart commerce and wise medicine. While several challenges are raised when we fuse the multiple-source IoT data recorded by different edge servers. First of all, data repetition within each edge server can greatly reduce the efficiency of various edge-based smart applications. Besides, IoT data fusion associated with multiple distributed edge servers can compromise user privacy. In addition, the multi-dimensional and interrelated nature of IoT data complicates precise data mining and analysis. To tackle these issues, a novel edge data fusion method (named TLTM) for cross-platform service recommendation is brought forth, which considers data dimensions, data correlation, and data privacy simultaneously. At last, to validate the effectiveness and efficiency of the TLTM method, we have designed massive experiments on the popular WS-DREAM dataset. The reported experimental results show that our TLTM method is superior to other related methods in terms of popular performance metrics including MAE, RMSE, Precision, Recall, F1-Score, and Time cost. © 2024, The Authors. All rights reserved.

关键词： Quality of service

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Open-Vocabulary Affordance Detection in 3D Point Clouds

arXiv

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arXiv 2023年

作者： Nguyen, Toan Vu, Minh Nhat Vuong, An Nguyen, Dzung Vo, Thieu Le, Ngan Nguyen, Anh FPT Software AI Center Viet Nam VNUHCM-University of Science Ho Chi Minh City Viet Nam TU Wien Vienna Austria Vienna Austria Faculty of Mathematics and Statistics Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Science & Computer Engineering University of Arkansas United States Department of Computer Science University of Liverpool United Kingdom

Affordance detection is a challenging problem with a wide variety of robotic applications. Traditional affordance detection methods are limited to a predefined set of affordance labels, hence potentially restricting the adaptability of intelligent robots in complex and dynamic environments. In this paper, we present the Open-Vocabulary Affordance Detection (OpenAD) method, which is capable of detecting an unbounded number of affordances in 3D point clouds. By simultaneously learning the affordance text and the point feature, OpenAD successfully exploits the semantic relationships between affordances. Therefore, our proposed method enables zero-shot detection and can be able to detect previously unseen affordances without a single annotation example. Intensive experimental results show that OpenAD works effectively on a wide range of affordance detection setups and outperforms other baselines by a large margin. Additionally, we demonstrate the practicality of the proposed OpenAD in real-world robotic applications with a fast inference speed. Our project is available at https://***. © 2023, CC0.

关键词： Intelligent robots

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Contrastive Learning Based Human Activity Recognition Using Body Sensors

Contrastive Learning Based Human Activity Recognition Using ...

引用

International Conference on Big Data Computing and Communications (BIGCOM)

作者： Chuanshi Xie Yu Zhou Dandan Yu Shilong Sun Xiao Zhang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Information Center The First Affiliated Hospital of Dalian Medical University Dalian China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Department of Computer Science South-Central Minzu University State Ethnic Affairs Commission Key Laboratory of Cyber-Physical Fusion Intelligent Computing South-Central Minzu University Wuhan China

Pervasive sensing and wearable sensor techniques have been increasingly employed to monitor and recognize human activities through body sensors in areas of smart healthcare and manufacturing. However, conventional machine learning or deep learning based human activity recognition (HAR) requires a large amount of labeled data, which is cost-expensive in real-world scenarios. To tackle this issue, we propose a contrastive learning framework for HAR (CL-HAR), which utilizes the generated unlabeled data as the input and explores the supervised features of the unlabeled data under the principle of self-supervised learning. A simple yet effective backbone network as a feature extractor for subsequent activity recognition is proposed. By using a small portion of the labeled samples as the training set which is fed into our learned feature extractor, we build a classifier and use the rest of the data to verify the feasibility and effectiveness of our CL method. Extensive experiments on three benchamark datasets and one real-world dataset demonstrate that CL-HAR can achieve better classification accuracy than compared supervised and semi-supervised methods with less labelled samples, which is of practical use.

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Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities

arXiv

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arXiv 2024年

作者： Wehbi, Osama Arisdakessian, Sarhad Guizani, Mohsen Wahab, Omar Abdel Mourad, Azzam Otrok, Hadi Khzaimi, Hoda Al Ouni, Bassem Mohammad Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Computer and Software Engineering Polytechnique Montréal MontrealQC Canada Artificial Intelligence & Cyber Systems Research Center Department of CSM Lebanese American University Lebanon KU 6G Research Center Department of CS Khalifa University United Arab Emirates Khalifa University Abu Dhabi United Arab Emirates Division of Engineering New York University Abu Dhabi United Arab Emirates Abu Dhabi United Arab Emirates

Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities. Nevertheless, the inherent heterogeneity of these urban environments presents a significant challenge in selecting trustworthy clients for collaborative model training. The usage of traditional approaches, such as the random client selection technique, poses several threats to the system’s integrity due to the possibility of malicious client selection. Primarily, the existing literature focuses on assessing the trustworthiness of clients, neglecting the crucial aspect of trust in federated servers. To bridge this gap, in this work, we propose a novel framework that addresses the mutual trustworthiness in federated learning by considering the trust needs of both the client and the server. Our approach entails: (1) Creating preference functions for servers and clients, allowing them to rank each other based on trust scores, (2) Establishing a reputation-based recommendation system leveraging multiple clients to assess newly connected servers, (3) Assigning credibility scores to recommending devices for better server trustworthiness measurement, (4) Developing a trust assessment mechanism for smart devices using a statistical Interquartile Range (IQR) method, (5) Designing intelligent matching algorithms considering the preferences of both parties. Based on simulation and experimental results, our approach outperforms baseline methods by increasing trust levels, global model accuracy, and reducing non-trustworthy clients in the system. © 2024, CC BY-NC-SA.

关键词： Recommender systems

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Fault Analysis of Microgrids with Inverter Interfaced Resources in Grid-Connected and Islanded Modes

Fault Analysis of Microgrids with Inverter Interfaced Resour...

引用

On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Jigyesh Sharma Kushan Tharuka Lulbadda Anindita Golder Tarlochan Sidhu Sheldon S. Williamson Department of Electrical and Computer Engineering Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech-Automotive Center of Excellence (ACE) Faculty of Engineering and Applied Science Smart Transportation Electrification and Energy Research (STEER) Group Ontario Tech University Oshawa ON Canada

The increasing penetration of inverter-based generation such as solar, wind and battery energy storage systems (BESS) has an impact on the fault currents in a microgrid. Also, if the microgrid is grid-connected or islanded affects the fault current. As such, it is important to conduct fault analysis of microgrids in grid-connected and islanded modes with different penetration levels of inverter-based generation sources to understand the impact of increasing levels of renewable energy on the fault currents. Different types of faults in a microgrid with inverter-based resources are simulated and the results are discussed and analyzed in this paper.

关键词： Wind energy generation Industrial electronics Renewable energy sources Wind energy Microgrids Inverters Batteries

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Scalable Stability of Nonlinear Interconnected Systems in case of amplifying Perturbations

Scalable Stability of Nonlinear Interconnected Systems in ca...

引用

American Control Conference (ACC)

作者： Marco Mirabilio Alessio Iovine Department of Information Engineering Computer Science and Mathematics Center of Excellence DEWS University of L’Aquila Laboratory of Signal and Systems (L2S) Centre National de la Recherche Scientifique (CNRS) CentraleSupélec Paris-Saclay University Gif-sur-Yvette France

This paper investigates the stability of large-scale systems (LSSs) in the presence of subsystems that amplify the perturbations propagated by their neighborhood, possibly leading to undesired behaviors of the overall interconnected system. Then, sufficient conditions ensuring the system trajectories boundedness and the subsequent LSS asymptotic stability in the sense of scalable Mesh Stability are proven to exist. The theoretical results show that there exists a dependence between the stability and the topology of the interconnected system. The obtained framework is then exploited for the stability analysis of a network of electrical microgrids, showing the effectiveness of the theoretical results.

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Contextualized Semantic Distance between Highly Overlapped Texts

arXiv

引用

arXiv 2021年

作者： Peng, Letian Li, Zuchao Zhao, Hai Department of Computer Science and Engineering University of California San Diego United States National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Department of Computer Science and Engineering Shanghai Jiao Tong University China

Overlapping frequently occurs in paired texts in natural language processing tasks like text editing and semantic similarity evaluation. Better evaluation of the semantic distance between the overlapped sentences benefits the language system’s understanding and guides the generation. Since conventional semantic metrics are based on word representations, they are vulnerable to the disturbance of overlapped components with similar representations. This paper aims to address the issue with a mask-and-predict strategy. We take the words in the longest common sequence (LCS) as neighboring words and use masked language modeling (MLM) from pre-trained language models (PLMs) to predict the distributions in their positions. Our metric, Neighboring Distribution Divergence (NDD), represents the semantic distance by calculating the divergence between distributions in the overlapped parts. Experiments on Semantic Textual Similarity show NDD to be more sensitive to various semantic differences, especially on highly overlapped paired texts. Based on the discovery, we further implement an unsupervised and training-free method for text compression, leading to a significant improvement on the previous perplexity-based method. The high compression rate controlling ability of our method even enables NDD to outperform the supervised state-of-the-art in domain adaption by a huge margin. Further experiments on syntax and semantics analyses verify the awareness of internal sentence structures, indicating the high potential of NDD for further studies.1 Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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