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

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 1. 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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Dynamics and Resonance Fluorescence from a Superconducting Artificial Atom Doubly Driven by Quantized and Classical Fields

arXiv

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arXiv 2024年

作者： Ruan, Xinhui Wang, Jia-Heng He, Dong Song, Pengtao Li, Shengyong Zhao, Qianchuan Kuang, L.M. Tsai, Jaw-Shen Zou, Chang-Ling Zhang, Jing Zheng, Dongning Astafiev, O.V. Liu, Yu-Xi Peng, Zhihui Department of Automation Tsinghua University Beijing100084 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center of Quantum Effects and Applications Hunan Normal University Changsha410081 China School of Integrated Circuits Tsinghua University Beijing100084 China Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100190 China Center for Quantum Computing RIKEN Saitama351-0198 Japan Graduate School of Science Tokyo University of Science 1-3 Kagurazaka Shinjuku Tokyo162-0825 Japan CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei Anhui230026 China Hefei National Laboratory Hefei230088 China School of Automation Science and Engineering Xi'an Jiaotong University Xi’an710049 China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi’an710049 China Skolkovo Institute of Science and Technology Nobel str. 3 Moscow143026 Russia Moscow Institute of Physics and Technology Institutskiy Pereulok 9 Dolgoprudny141701 Russia Royal Holloway University of London Surrey EghamTW20 0EX United Kingdom

We report an experimental demonstration of resonance fluorescence in a two-level superconducting artificial atom under two driving fields coupled to a detuned cavity. One of the fields is classical and the other is varied from quantum (vacuum fluctuations) to classical one by controlling the photon number inside the cavity. The device consists of a transmon qubit strongly coupled to a one-dimensional transmission line and a coplanar waveguide resonator. We observe a sideband anti-crossing and asymmetry in the emission spectra of the system through a one-dimensional transmission line, which is fundamentally different from the weak coupling case. By changing the photon number inside the cavity, the emission spectrum of our doubly driven system approaches to the case when the atom is driven by two classical bichromatic fields. We also measure the dynamical evolution of the system through the transmission line and study the properties of the first-order correlation function, Rabi oscillations and energy relaxation in the system. The study of resonance fluorescence from an atom driven by two fields promotes understanding decoherence in superconducting quantum circuits and may find applications in superconducting quantum computing and quantum networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Electric lines

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

引用

arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related {1.. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced {1. demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader {1. of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Exploiting Two-Dimensional Bi2O2Se for Trace Oxygen Detection

引用

Angewandte Chemie 2020年第41期132卷

作者： Xu S. Fu H. Tian Y. Deng T. Cai J. Wu J. Tu T. Li T. Tan C. Liang Y. Zhang C. Liu Z. Chen Y. Jiang Y. Yan B. Peng H. Center for Nanochemistry Beijing Science and Engineering Center for Nanocarbons Beijing National Laboratory for Molecular Sciences College of Chemistry and Molecular Engineering Peking University Beijing 100871 China Department of Condensed Matter Physics Weizmann Institute of Science Rehovot 7610001 Israel International Center for Quantum Materials School of Physics Peking University Beijing 100871 China CAS Center for Excellence in Superconducting Electronics (CENSE) State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology (SIMIT) Chinese Academy of Sciences Shanghai 200050 China University of Chinese Academy of Sciences Beijing 100049 China School of Physical Science and Technology ShanghaiTech University Shanghai 201210 China Tianjin Key Lab for Rare Earth Materials and Applications Center for Rare Earth and Inorganic Functional Materials School of Materials Science and Engineering National Institute for Advanced Materials Nankai University Tianjin 300350 China Academy for Advanced Interdisciplinary Studies Peking University Beijing 100871 China ShanghaiTech Laboratory for Topological Physics ShanghaiTech University Shanghai 201210 China Collaborative Innovation Center of Quantum Matter Beijing 100084 China Department of Physics University of Oxford Oxford OX1 3PU United Kingdom Collaborative Innovation Center of Quantum Matter Beijing 100871 China CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences Beijing China

We exploit a high-performing resistive-type trace oxygen sensor based on 2D high-mobility semiconducting Bi2O2Se nanoplates. Scanning tunneling microscopy combined with first-principle calculations confirms an amorphous Se atomic layer formed on the surface of 2D Bi2O2Se exposed to oxygen, which contributes to larger specific surface area and abundant active adsorption sites. Such 2D Bi2O2Se oxygen sensors have remarkable oxygen-adsorption induced variations of carrier density/mobility, and exhibit an ultrahigh sensitivity featuring minimum detection limit of 0.25 ppm, long-term stability, high durativity, and wide-range response to concentration up to 400 ppm at room temperature. 2D Bi2O2Se arrayed sensors integrated in parallel form are found to possess an oxygen detection minimum of sub-0.25 ppm ascribed to an enhanced signal-to-noise ratio. These advanced sensor characteristics involving ease integration show 2D Bi2O2Se is an ideal candidate for trace oxygen detection. © 2020 Wiley-VCH GmbH

关键词： amorphous layers Bi2O2Se integration oxygen sensors two-dimensional materials

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Correction to: 2021.Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales

引用

Archives of virology 2021年第12期166卷 3567-3579页

作者： Jens H Kuhn Scott Adkins Bernard R Agwanda Rim Al Kubrusli Sergey V Alkhovsky Gaya K Amarasinghe Tatjana Avšič-Županc María A Ayllón Justin Bahl Anne Balkema-Buschmann Matthew J Ballinger Christopher F Basler Sina Bavari Martin Beer Nicolas Bejerman Andrew J Bennett Dennis A Bente Éric Bergeron Brian H Bird Carol D Blair Kim R Blasdell Dag-Ragnar Blystad Jamie Bojko Wayne B Borth Steven Bradfute Rachel Breyta Thomas Briese Paul A Brown Judith K Brown Ursula J Buchholz Michael J Buchmeier Alexander Bukreyev Felicity Burt Carmen Büttner Charles H Calisher Mengji Cao Inmaculada Casas Kartik Chandran Rémi N Charrel Qi Cheng Yuya Chiaki Marco Chiapello Il-Ryong Choi Marina Ciuffo J Christopher S Clegg Ian Crozier Elena Dal Bó Juan Carlos de la Torre Xavier de Lamballerie Rik L de Swart Humberto Debat Nolwenn M Dheilly Emiliano Di Cicco Nicholas Di Paola Francesco Di Serio Ralf G Dietzgen Michele Digiaro Olga Dolnik Michael A Drebot J Felix Drexler William G Dundon W Paul Duprex Ralf Dürrwald John M Dye Andrew J Easton Hideki Ebihara Toufic Elbeaino Koray Ergünay Hugh W Ferguson Anthony R Fooks Marco Forgia Pierre B H Formenty Jana Fránová Juliana Freitas-Astúa Jingjing Fu Stephanie Fürl Selma Gago-Zachert George Fú Gāo María Laura García Adolfo García-Sastre Aura R Garrison Thomas Gaskin Jean-Paul J Gonzalez Anthony Griffiths Tony L Goldberg Martin H Groschup Stephan Günther Roy A Hall John Hammond Tong Han Jussi Hepojoki Roger Hewson Jiang Hong Ni Hong Seiji Hongo Masayuki Horie John S Hu Tao Hu Holly R Hughes Florian Hüttner Timothy H Hyndman M Ilyas Risto Jalkanen Dàohóng Jiāng Gilda B Jonson Sandra Junglen Fujio Kadono Karia H Kaukinen Michael Kawate Boris Klempa Jonas Klingström Gary Kobinger Igor Koloniuk Hideki Kondō Eugene V Koonin Mart Krupovic Kenji Kubota Gael Kurath Lies Laenen Amy J Lambert Stanley L Langevin Benhur Lee Elliot J Lefkowitz Eric M Leroy Shaorong Li Longhui Li Jiànróng Lǐ Huazhen Liu Igor S Lukashevich Piet Maes William Marciel de Souza Marco Marklewitz Sergio H Marshall Shin-Yi L Marzano Se Integrated Research Facility at Fort Detrick National Institute of Allergy and Infectious Diseases National Institutes of Health Frederick MD USA. kuhnjens@mail.nih.gov. United States Department of Agriculture Agricultural Research Service US Horticultural Research Laboratory Fort Pierce FL USA. Zoology Department National Museums of Kenya Nairobi Kenya. Jomo Kenyatta University of Agriculture & Technology Nairobi Kenya. Division Phytomedicine Faculty of Life Sciences Humboldt-Universität zu Berlin Berlin Germany. D.I. Ivanovsky Institute of Virology of N.F. Gamaleya National Center on Epidemiology and Microbiology of Ministry of Health of Russian Federation Moscow Russia. Department of Pathology and Immunology Washington University School of Medicine St. Louis MO USA. Faculty of Medicine University of Ljubljana Ljubljana Slovenia. Centro de Biotecnología y Genómica de Plantas Universidad Politécnica de Madrid-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria Campus de Montegancedo Pozuelo de Alarcón Madrid Spain. Departamento de Biotecnología-Biología Vegetal Escuela Técnica Superior de Ingeniería Agronómica Alimentaria y de Biosistemas Universidad Politécnica de Madrid Madrid Spain. Center for Ecology of Infectious Diseases Department of Infectious Diseases Department of Epidemiology and Biostatistics Insitute of Bioinformatics University of Georgia Athens GA USA. Institute of Novel and Emerging Infectious Diseases Friedrich-Loeffler-Institut Federal Research Institute for Animal Health Greifswald-Insel Riems Germany. Department of Biological Sciences Mississippi State University Mississippi State MS USA. Center for Microbial Pathogenesis Institute for Biomedical Sciences Georgia State University Atlanta GA USA. Edge BioInnovation Consulting and Mgt Frederick MD USA. Institute of Diagnostic Virology Friedrich-Loeffler-Institut Greifswald-Insel Riems Germany. UFyMA INTA-CONICET Cordoba Argentina. Genomics and Bioinformatics D

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Entrepreneurial Knowledge and the Influence of Entrepreneurship Education on Students' Entrepreneurial AbilitiesEntrepreneurial Knowledge and the Influence of Entrepreneurship Education on Students' Entrepreneurial Abilities

Entrepreneurial Knowledge and the Influence of Entrepreneurs...

引用

The 9th International Conference on Management and Service science (MASS 201.)

作者： Changqing Lai Wenjing lv Yuning Jiang School of Economics and Management Tsinghua University Key Lab of Integrated Microsystem Science & Engineering Applications Shenzhen Graduate SchoolPeking University

This paper studies the influence of entrepreneurship education on students' entrepreneurial abil-ities from the perspective of social network. In four college students' entrepreneurship case stu-dies from one university,we adopted methodological triangulation and multiple methods to guar-antee the reliability and validity of the research. We proposed that courses and projects in entre-preneurship education formed social network among mentors,monitors,and students,in which knowledge could flow frequently. We then discussed the influence of projects pattern,mentor pattern,and monitors pattern on college students' entrepreneurial abilities respectively,by ana-lyzing the structure and ties of the social network. This research contributed to social network theory and entrepreneurship education both theoretically and practically.

关键词： Social Network Entrepreneurship Education Entrepreneurial Abilities Entrepreneurial Knowledge

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A multi-objective optimal water strategy using time series analysis and improved genetic algorithm

引用

Journal of Information and Computational science 2015年第6期12卷 2229-2239页

作者： Lai, Changqing Xu, Zhengchao Jiang, Yuning School of Economics and Management Tsinghua University Beijing China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Key Lab. of Integrated Microsystem Science & Engineering Applications Shenzhen Graduate School Peking University Beijing China

Time series analysis and genetic algorithm have a good application in many fields. In present paper, we utilized graph theory, and established a multi-objective optimal water distribution model according to current situation in China. Then, we utilized time series analysis to predict water supply and demand situation, and put forward an improved genetic algorithm solution for optimal water resources strategy. Furthermore, sensitivity and effectivity of the improved genetic algorithm is fit to resolve the model. This method can be extended to other various resources-distribution fields. Copyright © 201. Binary Information Press.

关键词： Graph theory

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Theory and verification of operator design methodology

引用

science China(Information sciences) 2012年第2期55卷 480-490页

作者： HU ZiYi1.ZHAO Yong1.WANG XinAn1.HUANG Ru2,WANG Teng1.& ZHANG Xing2 1.0} {1. of integrated microsystem science and engineering applications,Peking University Shenzhen Graduate School,Shenzhen 51.055,China 2Institute of Microelectronics,Peking University,Beijing 1.0871.China 1. Key Lab of Integrated Microsystem Science and Engineering Applications Peking University Shenzhen Graduate School Shenzhen 518055 China2. Institute of Microelectronics Peking University Beijing 100871 China

Non-recurring-engineering(NRE)and time-to-market play an increasingly important role in the field of IC ***,with the continuous development of IC manufacturing technology,it is necessary to propose a novel design methodology to shorten design cycle and enhance design *** this paper,operator design methodology(ODM)is presented and an H.264 encoder is implemented as a *** to the flow of ODM,the register transfer level(RTL)design of H.264 encoder has been accomplished with 1. man-months,which is lower than the average 1. man-months in the field of traditional application specific {1. circuit(ASIC)***,with the advantage of operator design library,the design has a comparable performance with other ASIC *** obtained design can support a real-time video encoding of 720p at 60 frames per second or 1.80p at 30 frames per second,working at 1.7 MHz with SMIC 0.1. μm CMOS *** results provide good evidence for the practicability and efficiency of ODM.

关键词： operator design methodology H.264 encoder integrated circuit hardware

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Semantic classification based clothing image retrieval

Semantic classification based clothing image retrieval

引用

201. 3rd IEEE/IET International Conference on Audio, Language and Image Processing, ICALIP 201.

作者： Lu, Zhiyuan Shen, Ling Yuan, Yule Zhao, Yong Key Lab. of Integrated Microsystem Science and Engineering Applications Peking University Shenzhen Graduate School China

ISBN: (纸本)9781467301718

Performance of content-based image retrieval technology used in clothing image retrieval cannot meet user's requirement due to the 'semantic gap' between low-level visual features and the richness of human semantics and it's time consuming to searching images in a growing number on the internet. In order to reduce the 'semantic gap' and improve the searching speed, this paper introduces a novel clothing image retrieval method based on color semantic classification. In this method, database images are classified into different categories with color semantic features and indexed by their visual features. In the retrieval process, images with the same semantic category are selected as candidates and further ranked by their visual features. The sematic information can also be used integrated with text information {1.eled on the internet. Experimental results demonstrate our proposed method provides promising retrieval performance in less time than previous methods. © 201. IEEE.

关键词： Semantics

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Semantic Classification Based Clothing Image Retrieval

Semantic Classification Based Clothing Image Retrieval

引用

International Conference on Audio, Language and Image Processing

作者： Zhiyuan Lu Ling Shen Yule Yuan Yong Zhao Key Lab of Integrated Microsystem Science and Engineering Applications Peking University Shenzhen Graduate School

ISBN: (纸本)9781467301732

关键词： image retrieval Semantics content-based image retrieval Clothing Visualization Internets User needs

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