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第七届中国测试学术会议

作者： Cheng Yuanqing 成元庆 Zhang Lei 张磊 Han Yinhe 韩银和 Li Xiaowei 李晓维 State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of 中国科学院计算技术研究所计算机体系结构国家重点实验室(筹) 北京100190 中国科学院研究生院北京 100190 State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of 中国科学院计算技术研究所计算机体系结构国家重点实验室(筹) 北京100190

三维集成电路是通过硅通孔将多个相同或不同工艺的晶片上下堆叠并进行垂直集成的新兴芯片集成技术。通过这种集成,芯片可获得更小的外形尺寸、更高的片上晶体管集成密度、单片上能集成更多的功能模块以及更高的互连性能等显著优点。然而... 详细信息

三维集成电路是通过硅通孔将多个相同或不同工艺的晶片上下堆叠并进行垂直集成的新兴芯片集成技术。通过这种集成,芯片可获得更小的外形尺寸、更高的片上晶体管集成密度、单片上能集成更多的功能模块以及更高的互连性能等显著优点。然而,三维集成电路也带来了诸如TSV电迁移效应等新挑战。本文提出了一种抑制TSV电迁移效应的可靠性设计方法。首先,针对镀铜气泡、绑定非对齐和绑定界面尘埃沾染等TSV缺陷,分析了制造缺陷和电迁移效应之间的关系。通过观察发现,制造缺陷在加剧电迁移效应的同时还会影响TSV的阻值。然后,本文提出了TSV-SAFE(TSV Self-healing architecture For Electro-migration)可靠性设计框架抑制电迁移效应。实验中,本文构建了一个由两层电路组成的3D芯片仿真平台。实验结果表明,采用本文所提出的技术,TSV的平均无故障时间(MTTF)平均增加了70倍,而由此带来的硬件面积开销不超过全芯片面积的1％。

关键词：三维集成电路电迁移效应可靠性设计自修复技术

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基于敏感度分析的异构众核处理器能效管理方法

基于敏感度分析的异构众核处理器能效管理方法

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第七届中国测试学术会议

作者： Jun Ma 马君 Guihai Yan 鄢贵海 Yinhe Han 韩银和 Xiaowei Li 李晓维 State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of 中国科学院计算技术研究所计算机体系结构国家重点实验室(筹) 北京100190 中国科学院研究生院北京 100190 State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of 中国科学院计算技术研究所计算机体系结构国家重点实验室(筹) 北京100190

随着集成电路设计和半导体工艺的发展，以及人们对计算需求的不断提高，处理器的结构也逐渐由单核、多核向众核演化。与此同时，单个多核处理器芯片的功耗也迅速上升。但限于片上供电及散热等因素的限制，功耗成为限制众核处理器性能提... 详细信息

随着集成电路设计和半导体工艺的发展，以及人们对计算需求的不断提高，处理器的结构也逐渐由单核、多核向众核演化。与此同时，单个多核处理器芯片的功耗也迅速上升。但限于片上供电及散热等因素的限制，功耗成为限制众核处理器性能提升的主要瓶颈。因此，如何在功耗受限的前提下充分发挥众核处理器的计算能力，即高效的能耗管理方法，是当下计算机体系结构设计的关键问题之一。现有的管理手段主要有动态电压频率调节、多线程技术及异构多核架构。但是如何有效地将这些技术有机结合来协同管理功耗及提升能效还有待研究。本文针对一种异构众核体系结构，利用敏感度分析的方法来指导能效管理。通过分析工作频率、线程数在不同异构架构上的能效边际效益，来择优激活某些处理器核，确定各个核的工作频率，以及分配并行程序的线程数量。

关键词：集成电路异构众核处理器能效管理敏感度分析边际效益

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相变存储器的可靠性设计方法

相变存储器的可靠性设计方法

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第七届中国测试学术会议

作者： Fang Yuntan 方运潭 Li Huawei 李华伟 Li Xiaowei 李晓维 State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of 中国科学院计算技术研究所计算机体系结构国家重点实验室北京100190 中国科学院研究生院北京100049 State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of 中国科学院计算技术研究所计算机体系结构国家重点实验室北京100190

相变存储器(Phase Change Memory,PCM)由于其高密度、低漏电功耗、抗辐射、非易失性、可扩展性等优势受到学术界和工业界的广泛关注,但是它面临着寿命有限、电阻漂移等可靠性问题。提高相变存储器的寿命和缓解电阻漂移对于其实用化十分... 详细信息

相变存储器(Phase Change Memory,PCM)由于其高密度、低漏电功耗、抗辐射、非易失性、可扩展性等优势受到学术界和工业界的广泛关注,但是它面临着寿命有限、电阻漂移等可靠性问题。提高相变存储器的寿命和缓解电阻漂移对于其实用化十分重要。本文介绍了相变存储器的故障模型,延长其寿命的四类主要方法以及电阻漂移缓解方法。

关键词：相变存储器可靠性设计故障模型电阻漂移

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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The Design and Implementation of a Cleaning system Prototype

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IOP Conference Series: Earth and Environmental Science 2019年第3期252卷

作者： He Yang Weiwei Liu Xiaohui Wang He Liu Bin Yu Hongwei Zhou Artificial Intelligence on Electric Power System State Grid Corporation Joint Laboratory Global Energy Interconnection Research Institute co.Ltd Beijing 102209 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data College of Computer Science North China University of Technology Beijing 100144 China State Grid Jiangsu Economic Research Institute Jiangsu 210000 China

As we all know, data is one of the most valuable assets, however, raw data is often problematic, not conducive to the training of algorithm models. To cope with this, we can process the dirty data with cleaning systems [1] to obtain standard clean data for data statistics, data mininig and other use. Instead of manually modifying data, writing SQLs or other cumbersome methods which are popular present ways to clean data, the article proposes an approach by making use of the Hadoop big data platform to support massive data and support the cleaning of multiple heterogeneous data sources. Moreover, our system prototype supports custom rules and algorithms, can export results to a specified database, greatly simplifying the workload of data cleaning personnel. Based on the system design and theoretical verification presented in this paper, the author implemented a big data cleaning tool based on big data platform. The typical data cleaning process shows that the data cleaning can be achieved and user operations can be simplified on the basis of the theory proposed in this paper.

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War city profiles drawn from satellite images

Nature Cities

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Nature Cities 2024年 359-369页

作者： Zhengyang Hou Ying Qu Liqiang Zhang Qiwei Yu Ziyue Chen Hong Tang Yuebin Wang Xingang Li Yang Li Shuwen Peng Xin Yao Xichen Meng Suhong Liu Jun Liu Faqiang Wang An Zeng Yuanyuan Zhao Jing Ran Chenghu Zhou State Key Laboratory of Remote Sensing Science Faculty of Geographical Science Beijing Normal University Beijing China School of Mathematical Sciences Beijing Normal University Beijing China School of Systems Science Beijing Normal University Beijing China College of Land Science and Technology China Agricultural University Beijing China School of Architecture and Planning Hunan University Changsha China State Key Laboratory of Resources and Environment Information System Institute of Geographical Science and Natural Resources Chinese Academy of Sciences Beijing China

The extent of war-induced destruction in urban areas is critical information for international relief efforts, impact assessments and restoration decisions. However, precise geotargeting of zones with severe destruction is still a great challenge. Here we present a novel temporal-knowledge-guided detection scheme (TKDS) with a pixel-based transformer network (PtNet) for monitoring urban destruction using satellite imagery, applied to conflict zones in the Syrian civil war and the Russia–Ukraine conflict. Compared with state-of-the-art methods, the TKDS-PtNet model enhances war damage identification by 44.0 (72.5 versus 28.5) in the F1 score for six Syrian cities and 34.2 (83.5 versus 49.3) for four Ukrainian cities. The identified damaged buildings are further utilized to estimate the affected population and damage to critical infrastructures such as hospitals and schools in these areas. Our results demonstrate the high potential of a repeatable and relatively low-cost scheme for the near real-time monitoring of damage in urban areas resulting from wars, earthquakes or extreme weather events. Our findings underscore the crucial importance of taking action to stop the conflict and developing mechanisms to prevent present and future urban-related damage from military actions.

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Bubble Passage: Underwater Superaerophobicity/Superaerophilicity and Unidirectional Bubble Passage Based on the Femtosecond Laser‐Structured Stainless Steel Mesh (Adv. Mater. Interfaces 14/2020)

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Advanced Materials Interfaces 2020年第14期7卷

作者： Jinglan Huo Qing Yang Jiale Yong Peixun Fan Yong feng Lu Xun Hou Feng Chen State Key Laboratory for Manufacturing System Engineering and Shaanxi Key Laboratory of Photonics Technology for Information School of Electronics & Information Engineering Xi'an Jiaotong University Xi'an 710049 P. R. China School of Mechanical Engineering Xi'an Jiaotong University Xi'an 710049 P. R. China Department of Electrical and Computer Engineering University of Nebraska‐Lincoln Lincoln NE 68588 USA

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Advancing global aerosol forecasting with artificial intelligence

arXiv

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arXiv 2024年

作者： Gui, Ke Zhang, Xutao Che, Huizheng Li, Lei Zheng, Yu An, Linchang Miao, Yucong Zhao, Hujia Dubovik, Oleg Holben, Brent Wang, Jun Gupta, Pawan Lind, Elena S. Toledano, Carlos Wang, Hong Wang, Zhili Wang, Yaqiang Huang, Xiaomeng Dai, Kan Xia, Xiangao Xu, Xiaofeng Zhang, Xiaoye State Key Laboratory of Severe Weather Key Laboratory of Atmospheric Chemistry of CMA Chinese Academy of Meteorological Sciences Beijing China National Meteorological Center CMA Beijing China Institute of Atmospheric Environment China Meteorological Administration Shenyang China Université de Lille CNRS UMR 8518–Laboratoire d’Optique Atmosphérique Lille France NASA Goddard Space Flight Center GreenbeltMD United States Department of Chemical and Biochemical Engineering The University of Iowa Iowa CityIA United States HuntsvilleAL United States NASA Marshall Space Flight Center HuntsvilleAL United States Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States Grupo de Optica Atmosférica Universidad de Valladolid Paseo Prado de la Magdalena Valladolid Spain Institute of Artificial Intelligence for Meteorological Chinese Academy of Meteorological Sciences Beijing China Department of Earth System Science Tsinghua University Beijing China Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China China Meteorological Administration Beijing China

Aerosol forecasting is essential for air quality warnings, health risk assessment, and climate change mitigation. However, it is more complex than weather forecasting due to the intricate interactions between aerosol physicochemical processes and atmospheric dynamics, resulting in significant uncertainty and high computational costs. Here, we develop an artificial intelligence-driven global aerosol-meteorology forecasting system (AI-GAMFS), which provides reliable 5-day, 3-hourly forecasts of aerosol optical components and surface concentrations at a 0.5° × 0.625° resolution. AI-GAMFS combines Vision Transformer and U-Net in a backbone network, robustly capturing the complex aerosol-meteorology interactions via global attention and spatiotemporal encoding. Trained on 42 years of advanced aerosol reanalysis data and initialized with GEOS Forward Processing (GEOS-FP) analyses, AI-GAMFS delivers operational 5-day forecasts in one minute. It outperforms the Copernicus Atmosphere Monitoring Service (CAMS) global forecasting system, GEOS-FP forecasts, and several regional dust forecasting systems in forecasting most aerosol variables including aerosol optical depth and dust components. Our results mark a significant step forward in leveraging AI to refine physics-based aerosol forecasting, facilitating more accurate global warnings for aerosol pollution events, such as dust storms and wildfires. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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Wireless-Friendly Window Position Optimization for RIS-Aided Outdoor-to-Indoor Networks based on Multi-Modal Large Language Model

arXiv

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arXiv 2024年

作者： Hou, Jinbo Qiu, Kehai Zhang, Zitian Yu, Yong Wang, Kezhi Capolongo, Stefano Zhang, Jiliang Li, Zeyang Zhang, Jie The Department of Electronic and Electrical Engineering the University of Sheffield Sheffield United Kingdom The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FD United Kingdom Brunel University London United Kingdom The School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China The Design & Health Lab Department of Architecture Built Environment and Construction Engineering Politecnico di Milano Italy The Department of Computer Science Brunel University London Middlesex UxbridgeUB8 3PH United Kingdom The State Key Laboratory of Synthetical Automation for Process Industries The College of Information Science and Engineering Northeastern University Shenyang China The Centre for Wireless Innovation Queen’s University of Belfast BelfastBT3 9DT United Kingdom

This paper aims to simultaneously optimize indoor wireless and daylight performance by adjusting the positions of windows and the beam directions of window-deployed reconfigurable intelligent surfaces (RISs) for RIS-aided outdoor-to-indoor (O2I) networks utilizing large language models (LLM) as optimizers. Firstly, we illustrate the wireless and daylight system models of RIS-aided O2I networks and formulate a joint optimization problem to enhance both wireless traffic sum rate and daylight illumination performance. Then, we present a multi-modal LLM-based window optimization (LMWO) framework, accompanied by a prompt construction template to optimize the overall performance in a zero-shot fashion, functioning as both an architect and a wireless network planner. Finally, we analyze the optimization performance of the LMWO framework and the impact of the number of windows, room size, number of RIS units, and daylight factor. Numerical results demonstrate that our proposed LMWO framework can achieve outstanding optimization performance in terms of initial performance, convergence speed, final outcomes, and time complexity, compared with classic optimization methods. The building’s wireless performance can be significantly enhanced while ensuring indoor daylight performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Daylighting

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Self-Propulsion: Superfast Near-Infrared Light-Driven Polymer Multilayer Rockets (Small 5/2016)

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Small 2016年第5期12卷

作者： Zhiguang Wu Tieyan Si Wei Gao Xiankun Lin Joseph Wang Qiang He State Key Laboratory of Robotics and System (HIT) Micro/Nanotechnology Research Center Harbin Institute of Technology Harbin 150080 China Department of Nanoengineering University of California San Diego La Jolla CA 92093 USA Electrical Engineering and Computer Sciences University of California Berkeley CA 94720 USA

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