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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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Fast sampling of satisfying assignments from random k-SAT with applications to connectivity

arXiv

引用

arXiv 2022年

作者： Chen, Zongchen Galanis, Andreas Goldberg, Leslie Ann Guo, Heng Herrera-Poyatos, Andrés Mani, Nitya Moitra, Ankur Department of Computer Science and Engineering University at Buffalo BuffaloNY14260 United States Department of Computer Science University of Oxford Wolfson Building Parks Road OxfordOX1 3QD United Kingdom School of Informatics University of Edinburgh Informatics Forum 10 Crichton Street EdinburghEH8 9AB United Kingdom University of Granada Spain Department of Mathematics Massachusetts Institute of Technology CambridgeMA20139 United States

We give a nearly linear-time algorithm to approximately sample satisfying assignments in the random k-SAT model when the density of the formula scales exponentially with k. The best previously known sampling algorithm for the random k-SAT model applies when the density α = m/n of the formula is less than 2k/300 and runs in time nexp(Θ(k)) (Galanis, Goldberg, Guo and Yang, SIAM J. Comput., 2021). Here n is the number of variables and m is the number of clauses. Our algorithm achieves a significantly faster running time of n1+ok(1) and samples satisfying assignments up to density α ≤ 20.039k. The main challenge in our setting is the presence of many variables with unbounded degree, which causes significant correlations within the formula and impedes the application of relevant Markov chain methods from the bounded-degree setting (Feng, Guo, Yin and Zhang, J. ACM, 2021;Jain, Pham and Vuong, 2021). Our main technical contribution is a ok(log n) bound of the sum of influences in the k-SAT model which turns out to be robust against the presence of high-degree variables. This allows us to apply the spectral independence framework and obtain fast mixing results of a uniform-block Glauber dynamics on a carefully selected subset of the variables. The final key ingredient in our method is to take advantage of the sparsity of logarithmic-sized connected sets and the expansion properties of the random formula, and establish relevant connectivity properties of the set of satisfying assignments that enable the fast simulation of this Glauber dynamics. Our results also allow us to conclude that, with high probability, a random k-CNF formula with density at most 20.227k has a giant component of solutions that are connected in a graph where solutions are adjacent if they have Hamming distance Ok(log n). We are also able to deduce looseness results for random k-CNFs in the same *** Codes 68W20, 68W25, 68W40, 68Q87 © 2022, CC BY.

关键词： Markov chains

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Constellation-based robotic visual servoing method for fault diagnosis of used printed circuit board assemblies

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CIRP Annals 2025年

作者： Tipary, Bence Erdős, Gábor Kemény, Zsolt Centre of Excellence in Production Informatics and Control HUN-REN Institute for Computer Science and Control Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

The mainstream of diagnostic approaches of printed circuit board assemblies is optimised for large-scale industrial production, and is less suited for repair and maintenance during later stages of the product life-cycle. Main challenges include sparse documentation, small batch sizes and high product diversity, to which existing solutions are too costly, bulky or parameter-sensitive for industrial use. The paper presents a novel approach using computer vision, visual servoing and digital twins for robotic positioning of a measuring probe head. Subject to patent application, the method has proven its feasibility and affordability in live industrial practice with 99.80% success rate. © 2025 The Author(s)

关键词： Industrial robots

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Artificial Intelligence Approach for Name Classification in Information-Centric Networking-based Internet of Things 2

Artificial Intelligence Approach for Name Classification in ...

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2nd International Conference on Sustainable engineering and Creative Computing, ICSECC 2020

作者： Safitri, Cutifa Mandala, Rila Nguyen, Quang Ngoc Sato, Takuro Faculty of Computing President University Bekasi Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Department of Communications and Computer Engineering Waseda University Tokyo Japan Faculty of Science and Engineering Waseda University Tokyo Japan

ISBN: (纸本)9781728175881

Content management has continuously been among the most challenging problems, especially on the Internet of Things (IoT), where devices are set to be "hungry" for content. In this context, Information-Centric Networking (ICN), a promising Future Internet Architecture, can facilitate the IoT requirements of continuous and long-lasting connectivity, which has burdened the IP-based network system. In ICN, a content naming structure is composed to direct content requests to the nearest content provider with the built-in in-network caching function, storing the content. For a successful implementation of ICN-based IoT, an intelligent algorithm approach for IoT-based ICN implementation aiming to improve content management is proposed in this study. We establish a hybrid ICN with the most suitable machine learning algorithms satisfying the requirements to realize a feasible IoT technology. The selected algorithms from Supervised Learning, Unsupervised Learning, and Reinforcement Learning are evaluated before being chosen as the content forwarding process. The numerical findings show the superiority of the Extended Learning Classifier System under Reinforcement Learning’s scheme compared to the other algorithms. © 2020 IEEE.

关键词： Internet of things

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Hierarchical Blockchain radio Access Networks: Architecture, Modelling, and Performance Assessment

arXiv

引用

arXiv 2024年

作者： Kouvakis, Vasileios Trevlakis, Stylianos E. Boulogeorgos, Alexandros-Apostolos A. Liu, Hongwu Khalid, Waqas Tsiftsis, Theodoros Dobre, Octavia A. Department of Research and Development InnoCube P.C. 17is Noemvriou 79 Thessaloniki55534 Greece Department of Electrical and Computer Engineering University of Western Macedonia ZEP Area Kozani50100 Greece School of Information Science and Electrical Engineering Shandong Jiaotong University Jinan250357 China Institute of Industrial Technology Korea University Sejong30019 Korea Republic of Department of Informatics & Telecommunications University of Thessaly Lamia35100 Greece Faculty of Engineering and Applied Science Memorial University St. John’sNLA1B 3X9 Canada

Demands for secure, ubiquitous, and always-available connectivity have been identified as the pillar design parameters of the next generation radio access networks (raNs). Motivated by this, the current contribution introduces a network architecture that leverages blockchain technologies to augment security in raNs, while enabling dynamic coverage expansion through the use of intermediate commercial or private wireless nodes. To assess the efficiency and limitations of the architecture, we employ Markov chain theory in order to extract a theoretical model with increased engineering insights. Building upon this model, we quantify the latency as well as the security capabilities in terms of probability of successful attack, for three scenarios, namely fixed topology fronthaul network, advanced coverage expansion and advanced mobile node connectivity, which reveal the scalability of the blockchain-raN architecture. © 2024, CC BY.

关键词： Markov chains

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Accelerated discovery of 3D printing materials using data-driven multi-objective Optimization

arXiv

引用

arXiv 2021年

作者： Erps, Timothy Foshey, Michael Luković, Mina Konaković Shou, Wan Goetzke, Hanns Hagen Dietsch, Herve Stoll, Klaus von Vacano, Bernhard Matusik, Wojciech Electrical Engineering and Computer Science Department Massachusetts Institute of Technology CambridgeMA United States BASF SE RA OIS Carl Bosch Str 38 Ludwigshafen67056 Germany

Additive manufacturing has become one of the forefront technologies in fabrication, enabling new products impossible to manufacture before. Although many materials exist for additive manufacturing, they typically suffer from performance trade-offs preventing them from replacing traditional manufacturing techniques. Current materials are designed with inefficient human-driven intuition-based methods, leaving them short of optimal solutions. We propose a machine learning approach to accelerate the discovery of additive manufacturing materials with optimal trade-offs in mechanical performance. A multi-objective optimization algorithm automatically guides the experimental design by proposing how to mix primary formulations to create better-performing materials. The algorithm is coupled with a semi-autonomous fabrication platform to significantly reduce the number of performed experiments and overall time to solution. Without any prior knowledge of the primary formulations, the proposed methodology autonomously uncovers twelve optimal composite formulations and enlarges the discovered performance space 288 times after only 30 experimental iterations. This methodology could easily be generalized to other material formulation problems and enable completely automated discovery of a wide variety of material designs. Copyright © 2021, The Authors. All rights reserved.

关键词： 3D printers

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DeepHCS++: Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses for label-free high-content screening

引用

Medical Image Analysis 2021年 70卷 101995页

作者： Lee, Gyuhyun Oh, Jeong-Woo Her, Nam-Gu Jeong, Won-Ki Korea Republic of Department of Health Sciences and Technology Samsung Advanced Institute for Health Science and Technology Sungkyunkwan University Korea Republic of Institute for Refractory Cancer Research Samsung Medical Center Korea Republic of Department of Computer Science and Engineering College of Informatics Korea University Korea Republic of

In this paper, we propose a novel microscopy image translation method for transforming a bright-field microscopy image into three different fluorescence images to observe the apoptosis, nuclei, and cytoplasm of cells, which visualize dead cells, nuclei of cells, and cytoplasm of cells, respectively. These biomarkers are commonly used in high-content drug screening to analyze drug response. The main contribution of the proposed work is the automatic generation of three fluorescence images from a conventional bright-field image;this can greatly reduce the time-consuming and laborious tissue preparation process and improve throughput of the screening process. Our proposed method uses only a single bright-field image and the corresponding fluorescence images as a set of image pairs for training an end-to-end deep convolutional neural network. By leveraging deep convolutional neural networks with a set of image pairs of bright-field and corresponding fluorescence images, our proposed method can produce synthetic fluorescence images comparable to real fluorescence microscopy images with high accuracy. Our proposed model uses multi-task learning with adversarial losses to generate more accurate and realistic microscopy images. We assess the efficacy of the proposed method using real bright-field and fluorescence microscopy image datasets from patient-driven samples of a glioblastoma, and validate the method's accuracy with various quality metrics including cell number correlation (CNC), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), cell viability correlation (CVC), error maps, and R2 correlation. © 2021 Elsevier B.V.

关键词： Fluorescence microscopy

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Matching Using Sufficient Dimension Reduction for Heterogeneity Causal Effect Estimation 24

Matching Using Sufficient Dimension Reduction for Heterogene...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhao, Haoran Zhang, Yinghao Cheng, Debo Li, Chen Feng, Zaiwen College of Science Huazhong Agricultural University Wuhan430070 China College of Computer Science and Engineering Guangxi Normal University Guilin541000 China University of South Australia Stem Australia Kyushu Institute of Technology Department of Computer Science and Systems Engineering Iizuka820-8502 Japan College of Informatics Huazhong Agricultural University Wuhan430070 China Huazhong Agricultural University Hubei Key Laboratory of Agricultural Bioinformatics Wuhan430070 China Huazhong Agricultural University Hubei Hongshan Laboratory Wuhan430070 China Huazhong Agricultural University National Key Laboratory of Crop Genetic Improvement Wuhan430070 China Wuhan University State Key Laboratory of Hybrid Rice 299 Bayi Rd Wuhan430070 China Macro Agricultural Research Institute Huazhong Agricultural University Wuhan430070 China

ISBN: (纸本)9798350319934

Causal inference plays an important role in understanding the underlying mechanisation of the data generation process across various domains. It is challenging to estimate the average causal effect and individual causal effects from observational data with high-dimensional covariates due to the curse of dimension and the problem of data sufficiency. The existing matching methods can not effectively estimate individual causal effect or solve the problem of dimension curse in causal inference. To address this challenge, in this work, we prove that the reduced set by sufficient dimension reduction (SDR) is a balance score for confounding adjustment. Under the theorem, we propose to use an SDR method to obtain a reduced representation set of the original covariates and then the reduced set is used for the matching method. In detail, a non-parametric model is used to learn such a reduced set and to avoid model specification errors. The experimental results on real-world datasets show that the proposed method outperforms the compared matching methods. Moreover, we conduct an experiment analysis and the results demonstrate that the reduced representation is enough to balance the imbalance between the treatment group and control group individuals. © 2022 IEEE.

关键词： Causal Inference Individual Causal Effect Matching Sufficient Dimension Reduction

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Automatic Detection of Cardiac Ejection rate in Left Ventricular Photography Using Deep Learning 4

Automatic Detection of Cardiac Ejection Rate in Left Ventric...

引用

4th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2022

作者： Lin, Wenyuan Hu, Hsiang-Wei Tung, Nai-Yun Huang, Ren-Syuan Lin, Wei-Ming Hu, You-Ming Huang, Sheng-Yao Kao, Shin-Yu Lee, Chun-Yi Shih, Shao-Ni Wu, Chin-Yu Chang, Wei-Ting International Academia of Biomedical Innovation Technology Taipei Taiwan Chang Gung University Department of Medical Biotechnology and Laboratory Science Taoyuan Taiwan Department of Biomedical Engineering National Cheng Kung University Tainan Taiwan National Cheng Kung University Department of Biomedical Engineering Tainan Taiwan National Central University Department of Economics Taoyuan Taiwan Hualien Tzu Chi General Hospital Department of Radiation Oncology Hualien Taiwan Taipei Medical University Depaertment of Biomedical Informatics Taipei Taiwan National Tsing Hua University Department of Computer Science Hsinchu Taiwan National Tsing Hua University Department of Chinese Literature Hsinchu Taiwan Chinese Culture University Department of Electrical Engineering Taipei Taiwan Division of Cardiology Department of Internal Medicine Chi-Mei Medical Center Taiwan Institute of Clinical Medicine College of Medicine National Cheng Kung University Taiwan Department of Biotechnology Southern Taiwan University of Science and Technology Tainan Taiwan

ISBN: (数字)9781728195797

ISBN: (纸本)9781728195797

Left ventricular angiography is an assessment of cardiac structure and function that is often performed during cardiac catheterization. In addition to obtaining the index of left ventricular ejection rate (LVEF), left ventricular angiography also assists in the interpretation of cardiac hypertrophy, valve regurgitation, and regional myocardial systolic dysfunction. This also assists the clinician in the treatment and the judgment of opening the blood vessels. Although left ventricular angiography has been a well-established and internationally recognized detection method, it is not easy to train cardiac catheterization professionals, as image analysis requires sufficient experience but it is time-consuming. In this study, the deep learning models are introduced to reduce clinical interpretation errors and provide information on cardiac function and local cardiac systolic abnormalities in a short period. Then, clinicians can easily make medical decisions and judgments. The models in this study are expected to reduce 90% of operation time and improve the accuracy from 50% to 99%. It also helps detect myocardial hypoxia and determine whether to perform coronary surgery. Automatic and accurate detection of cardiac ejection rate, cardiac hypertrophy1, and abnormal local contraction is enabled to immediately warn and feedback to clinicians to make the most accurate treatment decisions. © 2022 IEEE.

关键词： Angiography

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QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge

arXiv

引用

arXiv 2024年

作者： Li, Hongwei Bran Navarro, Fernando Ezhov, Ivan Bayat, Amirhossein Das, Dhritiman Kofler, Florian Shit, Suprosanna Waldmannstetter, Diana Paetzold, Johannes C. Hu, Xiaobin Wiestler, Benedikt Zimmer, Lucas Amiranashvili, Tamaz Prabhakar, Chinmay Berger, Christoph Weidner, Jonas Alonso-Basanta, Michelle rashid, Arif Baid, Ujjwal Adel, Wesam Alis, Deniz Baheti, Bhakti Bai, Yingbin Bhat, Ishaan Cetindag, Sabri Can Chen, Wenting Cheng, Li Dutande, Prasad Dular, Lara Elattar, Mustafa A. Feng, Ming Gao, Shengbo Huisman, Henkjan Hu, Weifeng Innani, Shubham Ji, Wei Karimi, Davood Kuijf, Hugo J. Kwak, Jin Tae Le, Hoang Long Li, Xiang Lin, Huiyan Liu, Tongliang Ma, Jun Ma, Kai Ma, Ting Oksuz, Ilkay Holland, Robbie Oliveira, Arlindo L. Pal, Jimut Bahan Pei, Xuan Qiao, Maoying Saha, Anindo Selvan, raghavendra Shen, Linlin Silva, Joao Lourenco Spiclin, Ziga Talbar, Sanjay Wang, Dadong Wang, Wei Wang, Xiong Wang, Yin Xi, Ruiling Xu, Kele Yang, Yanwu Yergin, Mert Yu, Shuang Zeng, Lingxi Zhang, YingLin Zhao, Jiachen Zheng, Yefeng Zukovec, Martin Do, Richard Becker, Anton Simpson, Amber Konukoglu, Ender Jakab, Andras Bakas, Spyridon Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Quantitative Biomedicine University of Zurich Switzerland University Children’s Hospital Zurich University of Zurich Switzerland Department of Radioncology and Radiation Theraphy Klinikum rechts der Isar Technical University of Munich Germany Department of Information Technology and Electrical Engineering ETH-Zurich Switzerland Department of Radiology Memorial Sloan Kettering Cancer Center New York City United States Department of Biomedical and Molecular Sciences Queen’s University Canada TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany McGovern Institute Massachusetts Institute of Technology United States Institute for Diagnostic and Interventional Radiology Unveristy Zurich Hospital Switzerland BioMedIA Imperial College London United Kingdom Department of Radiation Oncology University of Pennsylvania PA United States University of Pennsylvania PA United States Department of Radiation Oncology Winship Cancer Institute of Emory University Georgia United States Nile University Cairo Egypt Department of Medical Sciences Acibadem University Istanbul Turkey Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Trustworthy Machine Learning Lab University of Sydney Australia Image Sciences Institute University Medical Center Utrecht Netherlands Computer Engineering Department Istanbul Technical University Istanbul Turkey School of Computer Science Shenzhen University Shenzhen China University of Alberta United States University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Tongji University Shanghai China OPPO Research Institute Shanghai China School of Biological and Medical Engineering Beihang University Beijing China Harvard Medical School Boston

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT;various organs such as the brain, prostate, kidney, and pancreas;and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks. © 2024, CC BY-NC-ND.

关键词： Image segmentation

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