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2014 3rd International Conference on Electrical and Electronics engineering(ICEEE 2014)

作者： Zheng Xie Xin'an Wang Zhibin Lian Shanshan Yong Key Lab of Integrated Micro System Science Engineering and Application Shenzhen Graduate School of Peking University

The reuse of verification code and methodology is a major factor providing significant acceleration of the overall verification efficiency. The verification system reusability is supported by a flexible user interface, environment structure, testbench design, and verification methodology. In this contribution, we proposed a novel reuse technology of extended finite state machine at the transaction level to simultaneously implement scenario generator, scenario coverage collector, and reference model. Associated with the Application Program Interface(API) function library based on Universal Verification Methodology(UVM), an extended finite state machine was recorded by the state machine component in Verification Expert system(VES) toolbox to generate specific code automatically. The APIs are encapsulated in the UVM library as a part of reuse resource that can reduce the amount of code greatly, provide clear architecture for functional debugging, and produce test cases automatically.

关键词： verification reuse extended finite state machine UVM

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Development of a Mind-controlled Android Racing Game using a Brain Computer Interface(BCI)

Development of a Mind-controlled Android Racing Game using a...

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2014 4th IEEE International Conference on Information science and Technology

作者： Guobin Wu Zheng Xie Xin'an Wang Key Lab of Integrated Micro System Science Engineering and Application Shenzhen Graduate School of Peking University

Brain computer interface(BCI) is to provide a direct communication or control channel between human's brain and external devices. Recently, with the development of BCI, several research institutions and organizations have developed some games that are interacted with BCI to enrich user's experience. However, most of these games are only used in computers and just focus on the realization of direction strategies. To meet the potential increasing Android smartphone users' needs and explore interacting game processes with people's mental states, this paper designs an Android 3D racing game controlled by mental states like attention and meditation. In this application, with the feedbacks of a Bluetooth BCI called Mindset, the racing car's speed and part of game scenarios can be in the charge of user's mental states.

关键词： BCI Development of a Mind-controlled Android Racing Game using a Brain Computer Interface

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Development of a mind-controlled Android racing game using a brain computer interface (BCI)

Development of a mind-controlled Android racing game using a...

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International Conference on Information science and Technology (ICIST)

作者： Guobin Wu Zheng Xie Xin'an Wang Key Lab of Integrated Micro System Science Engineering and Application Shenzhen Graduate School of Peking University Shenzhen China

Brain computer interface (BCI) is to provide a direct communication or control channel between human's brain and external devices. Recently, with the development of BCI, several research institutions and organizations have developed some games that are interacted with BCI to enrich user's experience. However, most of these games are only used in computers and just focus on the realization of direction strategies. To meet the potential increasing Android smartphone users' needs and explore interacting game processes with people's mental states, this paper designs an Android 3D racing game controlled by mental states like attention and meditation. In this application, with the feedbacks of a Bluetooth BCI called Mindset, the racing car's speed and part of game scenarios can be in the charge of user's mental states.

关键词： Games Bluetooth Androids Humanoid robots Smart phones Message systems Payloads

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A Method to Recognize Sleeping Position Using an CNN Model Based on Human Body Pressure Image

A Method to Recognize Sleeping Position Using an CNN Model B...

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IEEE International Conference on Power, Intelligent Computing and systems (ICPICS)

作者： Zhong Liu Xin'an Wang Mingliang SU Ken Lu School of Electronics Engineering and Computer Science Peking University China Key Laboratory of Integrated Micro-System Science and Engineering Applications Peking University Shenzhen Graduate School Shenzhen China iFutureLab Inc. USA

Sleep is an important part of human life. Recognition of sleeping position is an important part of judging the quality of sleep, warning disease and prevention of pressure sores. At present, the body pressure is being used as a commonly method to recognize sleep gesture, and most of it is based on methods of extracting features, mainly based on local signs and global features. This paper proposes a deep learning method based on CNN deep learning method to deal with recognition of body pressure image. Compared with the previous methods, the method based on CNN neural network achieves higher accuracy of recognition at a lower cost and is suitable for complex application environments, which has good practical value.

关键词： Sleep apnea Feature extraction Convolution Kernel Image recognition Biological neural networks Sensors

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A Study on Sleep Position Recognition of Body Pressure Image based on KPCA and SVM

A Study on Sleep Position Recognition of Body Pressure Image...

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IEEE International Conference on Power, Intelligent Computing and systems (ICPICS)

作者： Zhong Liu Xin'an Wang Yang Le Jone Sun School of Electronics Engineering and Computer Science Peking University China Key Laboratory of Integrated Micro-System Science and Engineering Applications Peking University Shenzhen Graduate School Shenzhen China iFutureLab Inc. USA

ISBN: (纸本)9781728137216

Sleep is an important part of life, and sleep position recognition is an important indicator which reflects sleep quality, warns diseases and prevents pressure sores. Actually, sleep quality is usually related to the body pressure, which affects muscle comfort and blood circulation during sleep. However, in order to regulate the body pressure, one vital step is to recognize the sleep position. Body pressure is a common indicator of the recognition of sleep position. This investigation put forward a body pressure recognition method combining with kernel principal component analysis (KPCA) and support vector machine. The feature extraction based on KPCA can well solve the nonlinear separable problems as well as dimension reduction processing of PCA, and reduce the system's complexity. Compared with the image recognition method and computer vision recognition method, it realized a higher recognition accuracy at a lower cost. Ultimately, the recognition accuracy of six common sleep positions reached 96.5%.

关键词： Sleep apnea Support vector machines Image recognition Mathematical model Feature extraction Training Principal component analysis

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Two-dimensional materials for future information technology: status and prospects

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science China(Information sciences) 2024年第6期67卷 1-147页

作者： Hao QIU Zhihao YU Tiange ZHAO Qi ZHANG Mingsheng XU Peifeng LI Taotao LI Wenzhong BAO Yang CHAI Shula CHEN Yiqi CHEN Hui-Ming CHENG Daoxin DAI Zengfeng DI Zhuo DONG Xidong DUAN Yuhan FENG Yu FU Jingshu GUO Pengwen GUO Yue HAO Jun HE Xiao HE Jingyi HU Weida HU Zehua HU Xinyue HUANG Ziyang HUANG Ali IMRAN Ziqiang KONG Jia LI Qian LI Weisheng LI Lei LIAO Bilu LIU Can LIU Chunsen LIU Guanyu LIU Kaihui LIU Liwei LIU Sheng LIU Yuan LIU Donglin LU Likuan MA Feng MIAO Zhenhua NI Jing NING Anlian PAN Tian-Ling REN Haowen SHU Litao SUN Yue SUN Quanyang TAO Zi-Ao TIAN Dong WANG Hao WANG Haomin WANG Jialong WANG Junyong WANG Wenhui WANG Xingjun WANG Yeliang WANG Yuwei WANG Zhenyu WANG Yao WE... Department of Applied Physics The Hong Kong Polytechnic University Key Laboratory for Micro-Nano Physics and Technology of Hunan Province College of Materials Science and EngineeringHunan University College of Integrated Circuits Zhejiang University Shenzhen Geim Graphene Center Shenzhen Key Laboratory of Layered Materials for Value-Added ApplicationsTsinghua-Berkeley Shenzhen Institute & Institute of Materials ResearchTsinghua Shenzhen International Graduate SchoolTsinghua University State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringInternational Research Center for Advanced PhotonicsZijingang CampusZhejiang University National Key Laboratory of Materials for Integrated Circuits Shanghai Institute of Microsystem and Information TechnologyChinese Academy of Sciences CAS Key Laboratory of Nanophotonic Materials and Devices & Key Laboratory of Nanodevices and Applications i-LabSuzhou Institute of Nano-Tech and Nano-Bionics (SINANO)Chinese Academy of Sciences College of Chemistry and Chemical Engineering Hunan University Key Laboratory of Quantum State Construction and Manipulation (Ministry of Education) Department of PhysicsRenmin University of China School of Integrated Circuits Beijing National Research Center for Information Science and Technology (BNRist)Tsinghua University School of Electronic Science and Engineering Nanjing University The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology Shaanxi Joint Key Laboratory of GrapheneSchool of MicroelectronicsXidian University Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education School of Physics and TechnologyWuhan University School of Integrated Circuits Huazhong University of Science and Technology Center for Nanochemistry (CNC) Beijing Science and Engineering Center for NanocarbonsBeijing National Laboratory for Molecular SciencesCollege of Chemistry and Molecular EngineeringPeking University State Key Laboratory for Artificial Microstruct

Over the past 70 years, the semiconductor industry has undergone transformative changes,largely driven by the miniaturization of devices and the integration of innovative structures and materials. Two-dimensional(2D) materials like transition metal dichalcogenides(TMDs) and graphene are pivotal in overcoming the limitations of silicon-based technologies, offering innovative approaches in transistor design and functionality, enabling atomic-thin channel transistors and monolithic 3D integration. We review the important progress in the application of 2D materials in future information technology, focusing in particular on microelectronics and optoelectronics. We comprehensively summarize the key advancements across material production, characterization metrology, electronic devices, optoelectronic devices, and heterogeneous integration on silicon. A strategic roadmap and key challenges for the transition of 2D materials from basic research to industrial development are outlined. To facilitate such a transition, key technologies and tools dedicated to 2D materials must be developed to meet industrial standards, and the employment of AI in material growth, characterizations, and circuit design will be essential. It is time for academia to actively engage with industry to drive the next 10 years of 2D material research.

关键词： two-dimensional (2D) materials information technology production technology standardization of characterization microelectronic devices optoelectronic devices multi-functional integration

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A Universal Framework of Dual-Use Model for Both Performance and Functionality Based on the Abstract State Machine

A Universal Framework of Dual-Use Model for Both Performance...

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International Conference on ASIC

作者： Zheng Xie Xin'an Wang Zhibin Lian Qiuping Li Shanshan Yong Key Lab of Integrated Micro System Science Engineering and Application Shenzhen Graduate School of Peking University Shenzhen China Be One Lab Inc. Toronto Ontario Canada

Confronted with ever increasing design complexity, modeling system architectures for early performance evaluation and fast exploration of the design space will be a necessary process. Meanwhile, function verification has been a bottleneck for time to market. The sharing of the two processes will greatly enhance the efficiency of system development. This paper presents a universal framework of dual-use model for both performance and functionality based on the abstract state machine, to reuse the performance model in building reference model to reduce the cycle of verification. The Application Program Interface (API) function library based on Universal Verification Methodology (UVM), corresponding to the dual-use model, is provided simultaneously. Associated with the function library, the abstract state machine is recorded by the state machine component in Verification Expert system (VES) toolbox to generate specific code automatically.

关键词： Abstract State Machine VES UVM

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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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Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial applications

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ACS Nano 2023年第12期17卷 11087-11219页

作者： Choi, Dongwhi Lee, Younghoon Lin, Zong-Hong Cho, Sumin Kim, Miso Ao, Chi Kit Soh, Siowling Sohn, Changwan Jeong, Chang Kyu Lee, Jeongwan Lee, Minbaek Lee, Seungah Ryu, Jungho Parashar, Parag Cho, Yujang Ahn, Jaewan Kim, Il-Doo Jiang, Feng Lee, Pooi See Khandelwal, Gaurav Kim, Sang-Jae Kim, Hyun Soo Song, Hyun-Cheol Kim, Minje Nah, Junghyo Kim, Wook Menge, Habtamu Gebeyehu Park, Yong Tae Xu, Wei Hao, Jianhua Park, Hyosik Lee, Ju-Hyuck Lee, Dong-Min Kim, Sang-Woo Park, Ji Young Zhang, Haixia Zi, Yunlong Guo, Ru Cheng, Jia Yang, Ze Xie, Yannan Lee, Sangmin Chung, Jihoon Oh, Il-Kwon Kim, Ji-Seok Cheng, Tinghai Gao, Qi Cheng, Gang Gu, Guangqin Shim, Minseob Jung, Jeehoon Yun, Changwoo Zhang, Chi Liu, Guoxu Chen, Yufeng Kim, Suhan Chen, Xiangyu Hu, Jun Pu, Xiong Guo, Zi Hao Wang, Xudong Chen, Jun Xiao, Xiao Xie, Xing Jarin, Mourin Zhang, Hulin Lai, Ying-Chih He, Tianyiyi Kim, Hakjeong Park, Inkyu Ahn, Junseong Huynh, Nghia Dinh Yang, Ya Wang, Zhong Lin Baik, Jeong Min Choi, Dukhyun Kyung Hee University Gyeonggi Yongin17104 Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Mechanical Engineering Soft Robotics Research Center Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan School of Advanced Materials Science & Engineering Sungkyunkwan University Suwon16419 Korea Republic of Sungkyunkwan University 2066 Seobu-ro Gyeonggi Suwon16419 Korea Republic of Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 117585 Singapore Division of Advanced Materials Engineering Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Department of Physics Inha University 100 Inha-ro Incheon22212 Korea Republic of School of Materials Science & Engineering Yeungnam University Gyeongbuk Gyeongsan38541 Korea Republic of 291 Daehak-ro Daejeon34141 Korea Republic of School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Institute of Flexible Electronics Technology of Tsinghua Zhejiang Jiaxing314000 China Nanomaterials and System Lab Major of Mechatronics Engineering Faculty of Applied Energy System Jeju National University Jeju632-43 Korea Republic of School of Engineering University of Glasgow GlasgowG128QQ United Kingdom Seoul02792 Korea Republic of Department of Physics Inha University Incheon22212 Korea Republic of Suwon16419 Korea Republic of Department of

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade. © 2023 The Authors. Published by American Chemical Society

关键词： Nanogenerators

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Room-temperature waveguide integrated quantum register in a semiconductor photonic platform

arXiv

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arXiv 2023年

作者： Hu, Haibo Zhou, Yu Yi, Ailun Bao, Tongyuan Liu, Chengying Luo, Qi Zhang, Yao Wang, Zi Li, Qiang Lu, Dawei Liu, Zhengtong Xiao, Shumin Ou, Xin Song, Qinghai Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems Harbin Institute of Technology Shenzhen518055 China Pengcheng Laboratory Shenzhen518055 China Shenzhen518045 China National Key Laboratory of Materials for Integrated Circuits Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China The Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China Institute of Advanced Semiconductors Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang Hangzhou311200 China State Key Laboratory of Silicon Materials and Advanced Semiconductors School of Materials Science and Engineering Zhejiang University Hangzhou310027 China Shenzhen Institute for Quantum Science and Engineering Department of Physics Southern University of Science and Technology Shenzhen518055 China Collaborative Innovation Center of Extreme Optics Shanxi University Shanxi Taiyuan030006 China

Quantum photonic integrated circuits are reshaping quantum networks and sensing by providing compact, efficient platforms for practical quantum applications. Despite continuous breakthroughs, integrating entangled registers into photonic devices on a CMOS-compatible platform presents significant challenges. Herein, we present single electron-nuclear spin entanglement and its integration into a silicon-carbide-on-insulator (SiCOI) waveguide. We demonstrate the successful generation of single divacancy electron spins and near-unity spin initialization of single 13C nuclear spins. Both single nuclear and electron spin can be coherently controlled and a maximally entangled state with a fidelity of 0.89 has been prepared under ambient conditions. Based on the nanoscale positioning techniques, the entangled quantum register has been further integrated into SiC photonic waveguides for the first time. We find that the intrinsic optical and spin characteristics of the register are well preserved and the fidelity of the entangled state remains as high as 0.88. Our findings highlight the promising prospects of the SiCOI platform as a compelling candidate for future scalable quantum photonic applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Silicon on insulator technology

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