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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 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

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Aerial Active STAR-RIS-assisted Satellite-Terrestrial Covert Communications

arXiv

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arXiv 2025年

作者： Zhang, Chuang Sun, Geng Li, Jiahui Wang, Jiacheng Zhang, Ruichen Niyato, Dusit Mao, Shiwen Quek, Tony Q.S. Computer Science and Technology Jilin University Changchun130012 China Singapore University of Technology and Design Singapore487372 Singapore College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States Information System Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore

An integration of satellites and terrestrial networks is crucial for enhancing performance of next generation communication systems. However, the networks are hindered by the long-distance path loss and security risks in dense urban environments. In this work, we propose a satellite-terrestrial covert communication system assisted by the aerial active simultaneous transmitting and reflecting reconfigurable intelligent surface (AASTAR-RIS) to improve the channel capacity while ensuring the transmission covertness. Specifically, we first derive the minimal detection error probability (DEP) under the worst condition that the Warden has perfect channel state information (CSI). Then, we formulate an AASTAR-RIS-assisted satellite-terrestrial covert communication optimization problem (ASCCOP) to maximize the sum of the fair channel capacity for all ground users while meeting the strict covert constraint, by jointly optimizing the trajectory and active beamforming of the AASTAR-RIS. Due to the challenges posed by the complex and high-dimensional state-action spaces as well as the need for efficient exploration in dynamic environments, we propose a generative deterministic policy gradient (GDPG) algorithm, which is a generative deep reinforcement learning (DRL) method to solve the ASCCOP. Concretely, the generative diffusion model (GDM) is utilized as the policy representation of the algorithm to enhance the exploration process by generating diverse and high-quality samples through a series of denoising steps. Moreover, we incorporate an action gradient mechanism to accomplish the policy improvement of the algorithm, which refines the better state-action pairs through the gradient ascent. Simulation results demonstrate that the proposed approach significantly outperforms important benchmarks, and also validate the performance and practicality under different algorithm parameters and environment settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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A Parameter-Centric Approach for Enhancing Misalignment Tolerance in Wireless Power Transfer systems

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IEEE Transactions on Power Electronics 2025年

作者： Yuan, Zhaoyang Yang, Qingxin Cai, Changsong Zhang, Pengcheng Wang, Ran Ma, Xianjie Lin, Hongjian Saeedifard, Maryam Wuhan University Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy School of Electrical Engineering and Automation Wuhan430072 China Tianjin University of Technology Tianjin300384 China Tsinghua University Department of Electrical Engineering Beijing100084 China Heze Power Supply Company State Grid Shandong Electric Power Company Heze274000 China Laixi Power Supply Company State Grid Shandong Electric Power Company Qingdao266600 China University of Alberta Department of Electrical and Computer Engineering ABT6G 2R3 Canada Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States

This paper introduces a misalignment-tolerant design method for wireless power transfer (WPT) systems by optimization of compensation network parameters. Leveraging the derivation of two-port impedance parameters and adjoint sensitivity analysis, the coupling misalignment characteristics of the system are assessed. Subsequently, a parameter-centric misalignment tolerant approach, independent of the coil structure, is established through a linear fitting method. The proposed approach ensures zero-voltage switching (ZVS) and facilitates efficient stable constant current (CC) output. Experimental validation is performed on a scaled-down 500-W rated SS-compensated WPT prototype equipped with non-specialized square-shaped coils, demonstrating the effectiveness of the proposed approach. The proposed method enhances the SS-compensated system's ability to tolerate both horizontal and vertical misalignments, surpassing the performance of conventional SS topologies. Specifically, the system achieves over 85% efficiency within the misalignment tolerance range, with a peak efficiency of 93.2%. Additionally, the load current variation remains within 5.8% for 100 mm x-misalignment and -15 to +50 mm z-misalignment. © 1986-2012 IEEE.

关键词： Inductive power transmission

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Erratum to: Large circuit models: opportunities and challenges

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SCIENCE CHINA Information Sciences 2024年第11期67卷 1-1页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y. YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah's Ark Lab Hong Kong 999077 China School of Integrated Circuits Peking University Beijing 100871 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo 315211 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong 999077 China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong 999077 China School of Integrated Circuits Southeast University Nanjing 210096 China Huawei HiSilicon Shenzhen 518129 China Peng Cheng Laboratory Shenzhen 518052 China School of Computer Science Peking University Beijing 100871 China School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University Shanghai 200433 China School of Artificial Intelligence Shanghai Jiao Tong University Shanghai 200240 China

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Continuous-time digital twin with analog memristive neural ordinary differential equation solver

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Science advances 2025年第22期11卷 eadr7571页

作者： Hegan Chen Jichang Yang Jia Chen Songqi Wang Shaocong Wang Dingchen Wang Xinyu Tian Yifei Yu Xi Chen Yinan Lin Qifan Zhu Yangu He Xiaoshan Wu Yi Li Xinyuan Zhang Ning Lin Meng Xu Xumeng Zhang Xiaojuan Qi Zhongrui Wang Han Wang Dashan Shang Qi Liu Kwang-Ting Cheng Ming Liu Department of Electrical and Electronic Engineering the University of Hong Kong Hong Kong China. School of Microelectronics Southern University of Science and Technology Shenzhen 518055 China. Center for Advanced Semiconductors and Integrated Circuits the University of Hong Kong Hong Kong China. Department of Electronic and Computer Engineering the Hong Kong University of Science and Technology Hong Kong China. School of Integrated Circuits Hubei Key Laboratory for Advanced Memories Huazhong University of Science and Technology Wuhan 430074 China. State Key Laboratory of Integrated Chips and Systems Frontier Institute of Chip and System Fudan University Shanghai 200433 China. State Key Lab of Fabrication Technologies for Integrated Circuits and Key Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing 100029 China. University of Chinese Academy of Sciences Beijing 100049 China.

Digital twins, which replicate real-world entities through computational models, are transforming manufacturing and automation. While recent advances in machine learning have enabled data-driven digital twin development using discrete-time data and finite-depth models on digital hardware, these approaches face significant limitations. They struggle to capture continuous-time dynamics and model complex systems, and suffer from substantial time and energy overheads due to physically separated storage and processing as well as frequent analog-digital (A/D) conversions. Here, we propose a memristive neural ordinary differential equation (ODE) solver for digital twins. Our approach is intrinsically time-continuous using infinite-depth neural networks to model complex dynamics. Fully analog memristor arrays collocate storage and computation, addressing the von Neumann bottleneck and reducing A/D conversion requirements. We experimentally validate our solver on digital twins of HP variable-resistor model and Lorenz96 dynamics, demonstrating a 166.5-fold/369.3-fold speedup and a 499.0-fold/673.9-fold improvement in energy efficiency, respectively. This work paves the way to future digital twins for Industry 4.0.

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Achieving High-Yield Conversion of Janus Transition Metal Dichalcogenides on Diverse Substrates

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ACS nano 2025年第22期19卷 2025 Jun 2页

作者： Xueqiu Zheng Kunyan Zhang Xiantuo Zhao Jian Zhou Hongzhi Shen Jing Kong Yunfan Guo Key Laboratory of Excited-State Materials of Zhejiang Province Department of Chemistry State Key Laboratory of Silicon and Advanced Semiconductor Materials Zhejiang University Hangzhou 310058 China. Molecular Biophysics and Integrated Bioimaging Division Lawrence Berkeley National Laboratory Berkeley California 94720 United States. Department of Chemistry University of California Berkeley California 94720 United States. Center for Alloy Innovation and Design State Key Laboratory of Mechanical Behavior of Materials Xi'an Jiaotong University Xi'an 710049 China. Research Center for Industries of the Future and School of Engineering Westlake University Hangzhou 310030 China. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 United States.

Janus transition metal dichalcogenides (TMDCs) with intrinsic broken mirror symmetry and vertical dipole moment provide an additional degree of freedom to manipulate material symmetry down to atomic-layer thickness. However, despite advances in synthesis strategies, fundamental understanding of this atomic substitution process remains limited, which has impeded their implementation in advanced devices. Here, by using a room-temperature atomic-layer substitution (RT-ALS) strategy, we systematically investigate the critical factors facilitating the high-yield conversion of Janus TMDCs on diverse substrates. Combining Raman spectroscopy probes, X-ray photoelectron spectroscopy (XPS) measurements, and density functional theory (DFT) calculations, we demonstrate that substrates with enhanced electron doping or larger surface polarity substantially benefit the conversion of Janus TMDCs reaching a near-unity yield. Intriguingly, the strong affinity between Janus TMDCs and substrates (e.g., Au) brings about abnormal Raman spectroscopic phenomena. These findings highlight the significance of substrates in achieving the reliable synthesis of Janus two-dimensional materials with improved homogeneity on various substrates. In addition, this takes us one step closer to utilizing Janus TMDCs as a versatile platform in next-generation optoelectronic devices, sensors, and quantum technologies.

关键词： Janus transition metal dichalcogenide controlled synthesis diverse substrates doping polarity strain

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Echo state graph neural networks with analogue random resistor arrays

arXiv

引用

arXiv 2021年

作者： Wang, Shaocong Li, Yi Wang, Dingchen Zhang, Woyu Chen, Xi Dong, Danian Wang, Songqi Zhang, Xumeng Lin, Peng Gallicchio, Claudio Xu, Xiaoxin Liu, Qi Cheng, Kwang-Ting Wang, Zhongrui Shang, Dashan Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Key Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China ACCESS - AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong University of Chinese Academy of Sciences Beijing100049 China Frontier Institute of Chip and System Fudan University Shanghai 200433 China College of Computer Science and Technology Zhejiang University Zhejiang310027 China Department of Computer Science University of Pisa Largo B. Pontecorvo 3 Pisa56127 Italy Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Recent years have witnessed an unprecedented surge of interest, from social networks to drug discovery, in learning representations of graph-structured data. However, graph neural networks, the machine learning models for handling graph-structured data, face significant challenges when running on conventional digital hardware, including von Neumann bottleneck incurred by physically separated memory and processing units, slowdown of Moore's law due to transistor scaling limit, and expensive training cost. Here we present a novel hardware-software co-design, the random resistor array-based echo state graph neural network, which addresses these challenges. The random resistor arrays not only harness low-cost, nanoscale and stackable resistors for highly efficient in-memory computing using simple physical laws, but also leverage the intrinsic stochasticity of dielectric breakdown to implement random projections in hardware for an echo state network that effectively minimizes the training cost thanks to its fixed and random weights. The system demonstrates state-of-the-art performance on both graph classification using the MUTAG and COLLAB datasets and node classification using the CORA dataset, achieving 34.2×, 93.2×, and 570.4× improvement of energy efficiency and 98.27%, 99.46%, and 95.12% reduction of training cost compared to conventional graph learning on digital hardware, respectively, which may pave the way for the next generation AI system for graph learning. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

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Non-Linear 3d Reconstruction For Compressive X-Ray Tomosynthesis

Non-Linear 3d Reconstruction For Compressive X-Ray Tomosynth...

引用

IEEE International Conference on Image Processing

作者： Qile Zhao Xu Ma Angela Cuadros Gonzalo R. Arce Rui Chen Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China School of Optics and Photonics Beijing Institute of Technology Beijing China Department of Electrical and Computer Engineering University of Delaware USA Integrated Circuit Advanced Process Center Institute of Microelectronics of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Compressive X-ray tomosynthesis is an emerging technology that can be used for medical diagnosis, safety inspection and industrial non-destructive testing. Traditional compressive X-ray tomosynthesis uses sequential illumination to interrogate the object of interest, which generates non-overlapping projection measurements. However, when multiple X-ray sources emit simultaneously, the projections corresponding to different sources overlap creating multiplexed measurements. Such measurement strategy leads to a non-linear reconstruction problem. The reconstruction of the three-dimensional (3D) object from multiplexed projections is an important problem. This paper proposes a non-linear 3D reconstruction approach for compressive X-ray tomosynthesis, where a set of coding masks are used to generate structured illumination and reduce radiation dose. The effectiveness of the method is verified by simulation experiments. It is shown that the proposed approach can reconstruct high-quality images with just a few snapshots.

关键词： Image reconstruction X-ray imaging Detectors Biomedical imaging Three-dimensional displays Encoding

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Order-Preserving Wasserstein Discriminant Analysis

Order-Preserving Wasserstein Discriminant Analysis

引用

International Conference on computer Vision (ICCV)

作者： Bing Su Jiahuan Zhou Ying Wu Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering Northwestern University Evanston IL

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Supervised dimensionality reduction for sequence data projects the observations in sequences onto a low-dimensional subspace to better separate different sequence classes. It is typically more challenging than conventional dimensionality reduction for static data, because measuring the separability of sequences involves non-linear procedures to manipulate the temporal structures. This paper presents a linear method, namely Order-preserving Wasserstein Discriminant Analysis (OWDA), which learns the projection by maximizing the inter-class distance and minimizing the intra-class scatter. For each class, OWDA extracts the order-preserving Wasserstein barycenter and constructs the intra-class scatter as the dispersion of the training sequences around the barycenter. The inter-class distance is measured as the order-preserving Wasserstein distance between the corresponding barycenters. OWDA is able to concentrate on the distinctive differences among classes by lifting the geometric relations with temporal constraints. Experiments show that OWDA achieves competitive results on three 3D action recognition datasets.

关键词： Training Dimensionality reduction Dispersion Complexity theory Distortion Legged locomotion Prototypes

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Learning low-dimensional temporal representations supplementary material 35

Learning low-dimensional temporal representations supplement...

引用

35th International Conference on Machine Learning, ICML 2018

作者： Su, Bing Wu, Ying Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China Department of Electrical Engineering and Computer Science Northwestern University EvanstonIL United States

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