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Recent progress of integrated circuits and optoelectronic chips

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

Multispectral Pan-sharpening via Dual-Channel Convolutional Network with Convolutional LSTM Based Hierarchical Spatial-Spectral Feature Fusion

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arXiv 2020年

作者： Wang, Dong Bai, Yunpeng Li, Ying School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xian China School of Computing and Information Systems University of Melbourne VIC3010 Australia

Multispectral pan-sharpening aims at producing a high resolution (HR) multispectral (MS) image in both spatial and spectral domains by fusing a panchromatic (PAN) image and a corresponding MS image. In this paper, we propose a novel dual-channel network (DCNet) framework for MS pan-sharpening. In our DCNet, the dual-channel backbone involves a spatial channel to capture spatial information with a 2D CNN, and a spectral channel to extract spectral information with a 3D CNN. This heterogeneous 2D/3D CNN architecture can minimize causing spectral information distortion, which typically happens in conventional 2D CNN models. In order to fully integrate the spatial and spectral features captured from different levels, we introduce a multi-level fusion strategy. Specifically, a spatial-spectral CLSTM (S2-CLSTM) module is proposed for fusing the hierarchical spatial and spectral features, which can effectively capture correlations among multi-level features. The S2-CLSTM module attaches two fusion ways: the intra-level fusion via bi-directional lateral connections and inter-level fusion via the cell state in the S2-CLSTM. Finally, the ideal HR-MS image is recovered by a reconstruction module. Extensive experiments have been conducted at both simulated lower scale and the original scale of real-world datasets. Compared with the state-of-the-art methods, the proposed DCNet achieves superior or competitive performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

Web Intelligence (WI) 3.0: in search of a better-connected world to create a future intelligent society

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Artificial Intelligence Review 2025年第9期58卷

作者： Kuai, Hongzhi Huang, Jimmy X. Tao, Xiaohui Pasi, Gabriella Yao, Yiyu Liu, Jiming Zhong, Ning School of Computer Science and Technology/School of Artificial Intelligence Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China School of Information Technology York University North YorkON Canada School of Mathematics Physics and Computing University of Southern Queensland ToowoombaQLD Australia Department of Informatics Systems and Communication University of Milano-Bicocca Milano Italy Department of Computer Science University of Regina Regina Canada Department of Computer Science Hong Kong Baptist University Kowloon Tong Hong Kong Faculty of Engineering Maebashi Institute of Technology Gunma Maebashi Japan

Over the past two decades, Web Intelligence (WI) has emerged as a key field driving the evolution of AI in the connected world, addressing the demands of a future intelligent society. This paper provides a comprehensive review of WI’s contributions since its inception in 2000, spanning three distinct phases: Wisdom World Wide Web (WI 1.0, 2000–2009), Wisdom Web of Things (WI 2.0, 2010–2017), and Wisdom Web of Everything (WI 3.0, since 2018). For each phase, we examine key advancements, challenges, and future directions from the perspectives of both intelligent machines and human experts, highlighting significant societal impacts. To advance WI research, we propose a large language model-based learning framework for topic analysis and trend prediction. Moving beyond single-perspective approaches, we emphasize the Connected Intelligence Ecosystem defined by the HIGH5 scheme comprising one goal, two twins, three fundamentals, four functions, and five services that are realized through WI 3.0. This vision serves as a bridge from localized models to a global reference framework for addressing sustainability challenges in future societies. To illustrate the real-world implications of WI 3.0, we present case studies focusing on brain-inspired research, particularly in the intersection of brain intelligence, brain health, and brainternet-fostering interdisciplinary collaboration across diverse research communities. © The Author(s) 2025.

关键词： Intelligent computing

Automatic image annotation with real-world community contributed data set

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Automatic image annotation with real-world community contrib...

作者： Tian, Feng Shen, Xukun Shang, Fuhua School of Computer and Information Technology Northeast Petroleum University DaQing163318 China School of Computing National University of Singapore Singapore119077 Singapore State Key Laboratory of Virtual Reality Technology and Systems BeiHang University Beijing100191 China

With the massive explosion of social multimedia community, social images have become very popular in our daily life. The image-associated labels are a valuable resource for automatic image annotation, but they tend to be unreliable. In this paper, we exploit the problem of image annotation from real-world community contributed images and their associated incorrect, insufficient, and personalized labels. We present SNTag, a novel semantic neighborhood learning method, on which image annotation task can be efficiently carried out in real-world scenario. First, we propose to use image-associated labels as the supervising information to guide the replenishment of training images, which enable the labels for training image not only more sufficient, but also more correct. Then, the "semantic balanced neighborhood" for image is generated, thus enabling the presence of more rare labels in image label list. Furthermore, we generate "semantic consistent neighborhood" within corresponding "semantic balanced neighborhood". The retrieved neighbor images are not only visually alike but also semantically related. Contrary to earlier work, these neighbors are retrieved from the same subspace by the integration of metric learning embedded in multiple labels and sparse reconstruction. Based on the neighbor set, we propose a novel algorithm to assign the optimal labels to the image, which is more robust to noise. We conduct extensive experiments on several standard real-world benchmark data sets downloaded from community websites. The experimental results demonstrate that it outperforms the current state-of-the-art methods. © 2017, Springer-Verlag Berlin Heidelberg.

关键词： Image annotation

A New Approach for Face Anti-Spoofing Using Handcrafted and Deep Network Features

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A New Approach for Face Anti-Spoofing Using Handcrafted and ...

2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Das, Polash Kumar Hu, Bin Liu, Chang Cui, Kaixin Ranjan, Prabhat Xiong, Gang School of Electronic and Information Engineering South China University of Technology Guangzhou China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China College of Computer Science Sichuan University Chengdu China Cloud Computing Center Chinese Academy of Science Dongguan China

ISBN: (纸本)9781728128535

In biometrics, face recognition methods are achieving momentum with recent progress in the computer vision(CV). Face recognition is widely used in the identification of an individual's identity. Unfortunately, in recent research work has revealed this face biometrics system is unprotected to spoofing attacks using by very low price instrument such as printed 2D photos attack, 3D masking attack and taking videos using smart devices (reply attack). Therefore, a Liveness Attack Detection (LAD) approach is needed to improve the high-quality security of face recognition system. Most of the earlier worked LAD methods for face anti-spoofing methods have highlight on using the handcrafted features, which are developed by expert knowledge of researcher. As example Gabor filter, Histogram of Oriented Gradients, local ternary pattern, and the Local Binary Pattern. Because of that, the extracted features consider limited factors of the problem, yielding a capture accuracy that is very low and changes with the point of presentation in attack face images. The deep learning method has developed in the computer vision research community, which is proven to be suitable for automatically training. In this article, we approach to mix or combine the handcrafted features and deep neural network features to design the discriminant face spoofing detection. The handcrafted features were based on LBP analysis. We examine the features information from the brightness and the chrominance channels using LBP descriptor. In deep features, we present an approach based on pre-trained convolutional neural network VGG-16 model using static features to recognize video and printed(2D) photo attacks. By attaching this two types of image features on our dataset and public databases, we get good results to identify real and attack images feature, called hybrid features, which has better discrimination ability to understand spoofing image feature. © 2019 IEEE.

关键词： Local binary pattern

EPICAST 2.0: A LARGE-SCALE, DEMOGRAPHICALLY DETAILED, AGENT-BASED MODEL FOR SIMULATING RESPIRATORY PATHOGEN SPREAD IN THE UNITED STATES

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arXiv 2025年

作者： Alexander, Prescott C. Harris, Thomas J. Kitson, Joy Tuccillo, Joseph V. Del Valle, Sara Y. Germann, Timothy C. Los Alamos National Laboratory Los AlamosNM United States Los Alamos National Laboratory Los AlamosNM United States School of Computing and Information Systems University of Melbourne ParkvilleVIC Australia Department of Computer Science University of Maryland College ParkMD United States Geospatial Science and Human Security Division Oak Ridge National Laboratory Oak RidgeTN United States Los Alamos National Laboratory Los AlamosNM United States

The recent history of respiratory pathogen epidemics, including those caused by influenza and SARS-CoV-2, has highlighted the urgent need for advanced modeling approaches that can accurately capture heterogeneous disease dynamics and outcomes at the national scale, thereby enhancing the effectiveness of resource allocation and decision-making. In this paper, we describe Epicast 2.0, an agent-based model that utilizes a highly detailed, synthetic population and high-performance computing techniques to simulate respiratory pathogen transmission across the entire United States. This model replicates the contact patterns of over 320 million agents as they engage in daily activities at school, work, and within their communities. Epicast 2.0 supports vaccination and an array of non-pharmaceutical interventions that can be promoted or relaxed via highly granular, user specified policies. We illustrate the model’s capabilities using a wide range of outbreak scenarios, highlighting the model’s varied dynamics as well as its extensive support for policy exploration. This model provides a robust platform for conducting what if scenario analysis and providing insights into potential strategies for mitigating the impacts of infectious diseases. Copyright © 2025, The Authors. All rights reserved.

关键词： Lung cancer

Hyperchaos on the dynamics of memristive Tabu learning neuron model under influence of electromagnetic radiation: Application in biomedical data privacy

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Franklin Open

Franklin Open 2025年 10卷

作者： Boya, Bertrand Frederick Boui A Kengne, Jacques Nanfak, Arnaud Muni, Sishu Shankar Nkapkop, Jean de Dieu Kenmoe, Germaine Djuidje Babenko, Lyudmila Klimentyevna Unité de Recherche d'Automatique et d'Informatique Appliquée (UR-AIA) IUT-FV Bandjoun University of Dschang Bandjoun P.O. Box 134 Cameroon Laboratory of Energy-Electric and Electronic Systems Department of Physics Faculty of Science University of Yaoundé I Yaoundé P.O. Box 812 Cameroon Department of Physics Faculty of Science University of Yaoundé 1 Yaoundé Cameroon Institute of Computer Technology and Information Security Southern Federal University P.O. Box 347922 Taganrog Russian Federation Laboratory for electronics electrical engineering automation and telecommunications National Higher Polytechnic School of Douala University of Douala Douala PO Box 2701 Cameroon School of Digital Sciences Digital University Kerala Technocity campus Kerala Mangalapuram India Indian Institute of Information Technology and Management Kerala Technopark road Kerala India Department of Electrical Engineering and Industrial Computing University Institute of Technology Douala P.O. Box 8698 Cameroon

This paper presents a novel biological neural networks based on memristive Tabu learning neuron (MTLN) model influenced by electromagnetic radiation. Despite the model having an unstable equilibrium plane, numerical investigation without the influence of electromagnetic radiation reveal that when increasing the memristor strength induces complex phenomena, including the coexistence of infinite bubbles of bifurcations, offset plane coexistence of various attractors. Furthermore, electromagnetic radiation can significantly impact the system's behavior, giving rise to hyperchaotic dynamics. A decent understanding is seen between the mathematical outcomes, approved through trial execution on an Arduino microcontroller-based. The insights gained from neural dynamics could pave the way for applications in neuromorphic computing, engineering, and brain-machine interfaces. Based on the complex dynamics of the model, a data privacy of biomedical data has been build and reveals good results, and enable to resist to robust attacks. © 2024 The Author(s)

关键词： Data privacy Electromagnetic radiation Hyperchaos Microcontroller design MTLN model Offset plane coexisting behaviors

Quantum computing: A taxonomy, systematic review and future directions

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arXiv 2020年

作者： Gill, Sukhpal Singh Kumar, Adarsh Singh, Harvinder Singh, Manmeet Kaur, Kamalpreet Usman, Muhammad Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Systemics School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Virtualization School of Computer Science University of Petroleum and Energy Studies Dehradun India Jackson School of Geosciences University of Texas at Austin United States Pune India Seneca International Academy Seneca Toronto Canada School of Computing and Information Systems The University of Melbourne ParkvilleVIC Australia Laboratory School of Computing and Information Systems The University of Melbourne Australia

Quantum computing is an emerging paradigm with the potential to offer significant computational advantage over conventional classical computing by exploiting quantum-mechanical principles such as entanglement and superposition. It is anticipated that this computational advantage of quantum computing will help to solve many complex and computationally intractable problems in several application domains such as drug design, data science, clean energy, finance, industrial chemical development, secure communications, and quantum chemistry. In recent years, tremendous progress in both quantum hardware development and quantum software/algorithm have brought quantum computing much closer to reality. Indeed, the demonstration of quantum supremacy marks a significant milestone in the Noisy Intermediate Scale Quantum (NISQ) era - the next logical step being the quantum advantage whereby quantum computers solve a real-world problem much more efficiently than classical computing. As the quantum devices are expected to steadily scale up in the next few years, quantum decoherence and qubit interconnectivity are two of the major challenges to achieve quantum advantage in the NISQ era. Quantum computing is a highly topical and fast-moving field of research with significant ongoing progress in all facets. A systematic review of the existing literature on quantum computing will be invaluable to understand the state-of-the-art of this emerging field and identify open challenges for the quantum computing community to address in the coming years. This article presents a comprehensive review of quantum computing literature and proposes taxonomy of quantum computing. The proposed taxonomy is used to map various related studies to identify the research gaps. A detailed overview of quantum software tools and technologies, post-quantum cryptography and quantum computer hardware development captures the current state-of-the-art in the respective areas. The article identifies and highlights va

关键词： Taxonomies

Cramér-rao bound optimization for joint radar-communication design

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arXiv 2021年

作者： Liu, Fan Liu, Ya-Feng Li, Ang Masouros, Christos Eldar, Yonina C. The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematicsand Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The School of Information and Communications Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’anShaanxi710049 China The Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom The Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel

In this paper, we propose multi-input multi-output (MIMO) beamforming designs towards joint radar sensing and multi-user communications. We employ the Cramér-Rao bound (CRB) as a performance metric of target estimation, under both point and extended target scenarios. We then propose minimizing the CRB of radar sensing while guaranteeing a pre-defined level of signal-to-interference-plus-noise ratio (SINR) for each communication user. For the single-user scenario, we derive a closed form for the optimal solution for both cases of point and extended targets. For the multi-user scenario, we show that both problems can be relaxed into semidefinite programming by using the semidefinite relaxation approach, and prove that the global optimum can always be obtained. Finally, we demonstrate numerically that the globally optimal solutions are reachable via the proposed methods, which provide significant gains in target estimation performance over state-of-the-art benchmarks. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal to noise ratio