检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Liu, Changzhu He, Ruisi Niu, Yong Han, Zhu Ai, Bo Gao, Meilin Ma, Zhangfeng Wang, Gongpu Zhong, Zhangdui The State Key Laboratory of Advanced Rail Autonomous Operation The School of Electronics and Information Engineering The Frontiers Science Center for Smart High-speed Railway System The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The Tsinghua Space Center the Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100091 China The College of Information Engineering Shaoyang University Shaoyang422000 China The Beijing Key Lab of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

Reconfigurable intelligent surface (RIS) emerges as maximization problem is formulated to facilitate RIS discrete an efficient and promising technology for the next wireless phase design and RIS placement optimization, which is subject generation networks and has attracted a lot of attention owing to coverage probability constraint. Simulation results validate to the capability of extending wireless coverage by reflecting that better coverage performance and higher travel distance signals toward targeted receivers. In this paper, we consider can be achieved with deployment of RIS. The impacts of some a RIS-assisted high-speed train (HST) communication system key system parameters including transmission power, signal-to-to enhance wireless coverage and improve coverage probability. noise ratio threshold, number of RIS elements, number of RIS First, coverage performance of the downlink single-input-single- quantization bits, horizontal distance between base station and output system is investigated, and the closed-form expression RIS, and speed of HST on system performance are investigated. of coverage probability is derived. Moreover, travel distance In addition, it is found that RIS can well improve coverage probability with limited power consumption for HST communications. Copyright © 2023, The Authors. All rights reserved.

关键词： Railroads

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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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Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0 Tesla K-Space and Spatial Dataset

arXiv

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arXiv 2024年

作者： Wang, Shanshan Yu, Shoujun Cheng, Jian Jia, Sen Tie, Changjun Zhu, Jiayu Peng, Haohao Dong, Yijing He, Jianzhong Zhang, Fan Xing, Yaowen Jia, Xiuqin Yang, Qi Tian, Qiyuan Guo, Hua Li, Guobin Zheng, Hairong Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Beihang University Beijing China United-Imaging Healthcare Shanghai China School of Biomedical Engineering Tsinghua University Beijing China Department of Radiology Beijing Chaoyang Hospital Capital Medical University Key Lab of Medical Engineering for Cardiovascular Disease Ministry of Education Beijing China Institute of Information Processing and Automation College of Information Engineering Zhejiang University of Technology China University of Electronic Science and Technology of China Chengdu China

Diffusion magnetic resonance imaging (dMRI) provides critical insights into the microstructural and connectional organization of the human brain. However, the availability of high-field, open-access datasets that include raw k-space data for advanced research remains limited. To address this gap, we introduce Diff5T, a first comprehensive 5.0 Tesla diffusion MRI dataset focusing on the human brain. This dataset includes raw k-space data and reconstructed diffusion images, acquired using a variety of imaging protocols. Diff5T is designed to support the development and benchmarking of innovative methods in artifact correction, image reconstruction, image preprocessing, diffusion modelling and tractography. The dataset features a wide range of diffusion parameters, including multiple b-values and gradient directions, allowing extensive research applications in studying human brain microstructure and connectivity. With its emphasis on open accessibility and detailed benchmarks, Diff5T serves as a valuable resource for advancing human brain mapping research using diffusion MRI, fostering reproducibility, and enabling collaboration across the neuroscience and medical imaging communities. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation

A Semi-Supervised Approach with Error Reflection for Echocar...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoxiang Han Yiman Liu Jiang Shang Qingli Li Jiangang Chen Menghan Hu Qi Zhang Yuqi Zhang Yan Wang School of Communication and Information Engineering Shanghai University Shanghai P.R.China The SMART (Smart Medicine and AI-based Radiology Technology) Lab Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai P.R.China Department of Pediatric Cardiology Shanghai Children’s Medical Center School of Medicine Shanghai Jiao Tong University Shanghai P.R.China Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP) Shanghai P.R.China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai P.R.China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Segmenting internal structure from echocardiography is essential for the diagnosis and treatment of various heart diseases. Semi-supervised learning shows its ability in alleviating annotations scarcity. While existing semi-supervised methods have been successful in image segmentation across various medical imaging modalities, few have attempted to design methods specifically addressing the challenges posed by the poor contrast, blurred edge details and noise of echocardiography. These characteristics pose challenges to the generation of high-quality pseudo-labels in semi-supervised segmentation based on Mean Teacher. Inspired by human reflection on erroneous practices, we devise an error reflection strategy for echocardiography semi-supervised segmentation architecture. The process triggers the model to reflect on inaccuracies in unlabeled image segmentation, thereby enhancing the robustness of pseudo-label generation. Specifically, the strategy is divided into two steps. The first step is called reconstruction reflection. The network is tasked with reconstructing authentic proxy images from the semantic masks of unlabeled images and their auxiliary sketches, while maximizing the structural similarity between the original inputs and the proxies. The second step is called guidance correction. Reconstruction error maps decouple unreliable segmentation regions. Then, reliable data that are more likely to occur near high-density areas are leveraged to guide the optimization of unreliable data potentially located around decision boundaries. Additionally, we introduce an effective data augmentation strategy, termed as multi-scale mixing up strategy, to minimize the empirical distribution gap between labeled and unlabeled images and perceive diverse scales of cardiac anatomical structures. Extensive experiments on a public echocardiography dataset CAMUS, and a private clinical echocardiography dataset demonstrate the competitiveness of the proposed method.

关键词： Image segmentation Echocardiography Noise Semantics Anatomical structure Semisupervised learning Data augmentation Reflection Robustness Image reconstruction

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Tailoring the Anomalous Hall Effect in Van Der Waals Ferromagnet Fe3gete2

SSRN

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SSRN 2024年

作者： Wu, Kaile Wang, Qin Wang, Junshan Jiang, Jiaolin Zhao, Tian Zhao, Dong Han, Dan Quan, Zhiyong Ma, Zongmin Wang, Haomin Sang, Shengbo Ge, Yang Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Electronic Information and Optical Engineering Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System The Ministry of Education Taiyuan University of Technology Taiyuan030024 China College of Materials Science and Engineering Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education School of Chemistry and Materials Science Shanxi Normal University Taiyuan030031 China State Key Laboratory of Dynamic Measurement Technology North University of China Shanxi Taiyuan030051 China School of Semiconductor and Physics North University of China Shanxi Taiyuan030051 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan030024 China

Two-dimensional van der Waals magnetic materials have shown fascinating natural ferromagnetic properties at the atomic level, which has great potential in fields of low power spintronics, quantum computing and optical communication. Herein, the ferromagnetism and magnetic anisotropy of bulk Fe3GeTe2 (FGT) were investigated, and the testing results indicate a pronounced vertical magnetic anisotropy below the Curie temperature (Tc=165 K) of FGT. In addition, the anomalous Hall effect of FGT nanoflakes was studied at various temperatures and thicknesses by standard Hall bar device in detail. The results show the anomalous Hall resistance (0.764 Ω at 20 K decreased to 0.332 Ω at 140 K) and the obvious rectangular magnetic hysteresis rings below Tc (165 K). Furthermore, we calculated the carrier concentration (2.5×1022 cm-3 at 20K) and mobility (13.3 cm2/(v·s) at 20K) under large magnetic field (-6 T~6 T). Particularly, we demonstrated that the strong tailoring ability of the anomalous Hall effect for FGT-based magnetoelectric transport devices comes form the regulation of magnetic moment by sample thickness and magnetic field angle. The above experimental results verify that FGT is expected to be a prime candidate material for ultrathin 2D spintronic devices to the next generation. © 2024, The Authors. All rights reserved.

关键词： Germanium alloys

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Gan/Moo3 N-N Heterojunction Based Anti-Humidity Gas Sensor for No2 Real-Time Monitoring

SSRN

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SSRN 2024年

作者： Hong, Yutao Han, Dan Duan, Qi Li, Donghui He, Xiuli Li, Bing Liang, Hua Li, Hongwei Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Integrated Circuits Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China State Key Laboratory of Transducer Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials Shanxi Medical University School Hospital of Stomatology Shanxi Taiyuan030001 China Shanxi Province Cancer Hospital Shanxi Hospital Affiliated to Cancer Hospital Chinese Academy of Medical Sciences Cancer Hospital Affiliated to Shanxi Medical University Taiyuan030000 China University of Chicago United States

Effective and accurate detection of NO2 gas is crucial for protecting the environment and public health. In this work, balsam pear-like GaN/MoO3 porous nanosheets (GaN/MoO3) composites were synthesized by in situ assembling, the hydrothermal method, and the nitridation process to detect NO2. A series of characterization methods were used to manifest the successful synthesis and to analyze the morphology and elemental composition features of GaN/MoO3 composites. The results of the gas-sensing experiments show that, at the operating temperature of 225℃, the gas sensor based on the optimal ratio of GaN/MoO3 composites exhibits a 25-fold lower limit of detection for NO2 than that of MoO3, with a reduction of baseline resistance drift. Further, the response value of the sensor based on GaN/MoO3 composites to 200 ppm NO2 (44.74) is 4.36 times higher than that of the sensor based on pure GaN, with an improvement in stability. Meanwhile, the sensor possessed excellent repeatability, selectivity, stability, and splendid anti-humidity ability at 15-70% RH. The improved gas-sensing performance is ascribed to the unique microstructure of both GaN and MoO3 and the heterojunction effect between them. In addition, we constructed a real-time NO2 monitoring device based on the gas-sensing characteristic of GaN/MoO3, which realizes the alarm of excessive concentrations of NO2. Overall, this work provides a way to improve the ability of NO2 monitoring for the sensors of MoO3-based composites. © 2024, The Authors. All rights reserved.

关键词： III-V semiconductors

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Gan/Moo3 N-N Heterojunction Based Anti-Humidity Gas Sensor for No2 Real-Time Monitoring

SSRN

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SSRN 2024年

作者： Hong, Yutao Han, Dan Duan, Qi Li, Donghui He, Xiuli Li, Bing Liang, Hua Li, Hongwei Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Integrated Circuits Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of Technology Taiyuan030024 China State Key Laboratory of Transducer Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials Shanxi Medical University School and Hospital of Stomatology Shanxi Taiyuan030001 China Shanxi Province Cancer Hospital Shanxi Hospital Affiliated to Cancer Hospital Chinese Academy of Medical Sciences Cancer Hospital Affiliated to Shanxi Medical University Taiyuan030000 China University of Chicago United States

Effective and accurate detection of NO2 gas is crucial for protecting the environment and public health. In this work, balsam pear-like GaN/MoO3 porous nanosheets (GaN/MoO3) composites were synthesized by in situ assembling, the hydrothermal method, and the nitridation process to detect NO2. A series of characterization methods were used to manifest the successful synthesis and to analyze the morphology and elemental composition features of GaN/MoO3 composites. The results of the gas-sensing experiments show that, at the operating temperature of 225°C, the gas sensor based on the optimal ratio of GaN/MoO3 composites exhibits a 25-fold lower limit of detection for NO2 than that of MoO3, with a reduction of baseline resistance drift. Further, the response value of the sensor based on GaN/MoO3 composites to 200 ppm NO2 (44.74) is 4.36 times higher than that of the sensor based on pure GaN, with an improvement in stability. Meanwhile, the sensor possessed excellent repeatability, selectivity, stability, and splendid anti-humidity ability at 15-70% RH. The improved gas-sensing performance is ascribed to the unique microstructure of both GaN and MoO3 and the heterojunction effect between them. In addition, we constructed a real-time NO2 monitoring device based on the gas-sensing characteristic of GaN/MoO3, which realizes the alarm of excessive concentrations of NO2. Overall, this work provides a way to improve the ability of NO2 monitoring for the sensors of MoO3-based composites. © 2024, The Authors. All rights reserved.

关键词： Gallium nitride

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Addendum: Solution epitaxy of polarization-gradient ferroelectric oxide films with colossal photovoltaic current

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Nature communications 2024年第1期15卷 3878-3878页

作者： Lin, Chen Zhang, Zijun Dai, Zhenbang Wu, Mengjiao Liu, Shi Chen, Jialu Hua, Chenqiang Lu, Yunhao Zhang, Fei Lou, Hongbo Dong, Hongliang Zeng, Qiaoshi Ma, Jing Pi, Xiaodong Zhou, Dikui Wu, Yongjun Tian, He Rappe, Andrew M. Ren, Zhaohui Han, Gaorong State Key Laboratory of Silicon and Advanced Semiconductor Materials School of Materials Science and Engineering Zhejiang University Hangzhou 310027 China Center of Electron Microscope School of Materials Science and Engineering Zhejiang University Hangzhou 310027 China Department of Chemistry University of Pennsylvania Philadelphia PA United States Oden Institute for Computational Engineering and Sciences University of Texas at Austin Austin 78712 TX United States Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Hangzhou 310024 China Zhejiang Province Key Laboratory of Quantum Technology and Device Department of physics Zhejiang University Hangzhou 310027 China Center for High Pressure Science and Technology Advanced Research 201203 Shanghai China State Key Lab of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University 100091 Beijing China Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou 311215 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou 311100 China State Key Laboratory of Silicon and Advanced Semiconductor Materials School of Materials Science and Engineering Zhejiang University Hangzhou 310027 China

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