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Ferroelectric transistors based on shear-transformation-mediated rhombohedral-stacked molybdenum disulfide

Nature Electronics 2024年第1期7卷 29-38页

作者： Yang, Tilo H. Liang, Bor-Wei Hu, Hsiang-Chi Chen, Fu-Xiang Ho, Sheng-Zhu Chang, Wen-Hao Yang, Liu Lo, Han-Chieh Kuo, Tzu-Hao Chen, Jyun-Hong Lin, Po-Yen Simbulan, Kristan Bryan Luo, Zhao-Feng Chang, Alice Chinghsuan Kuo, Yi-Hao Ku, Yu-Seng Chen, Yi-Cheng Huang, You-Jia Chang, Yu-Chen Chiang, Yu-Fan Lu, Ting-Hua Lee, Min-Hung Li, Kai-Shin Wu, Menghao Chen, Yi-Chun Lin, Chun-Liang Lan, Yann-Wen Department of Physics National Taiwan Normal University Taipei Taiwan Taiwan Semiconductor Research Institute National Applied Research Laboratories Hsinchu Taiwan Department of Electrophysics National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Physics National Cheng Kung University Tainan Taiwan School of Physics Huazhong University of Science and Technology Wuhan China Institute of Cellular and Organismic Biology Academia Sinica Taipei Taiwan Department of Mathematics and Physics University of Santo Tomas Manila Philippines Program for Semiconductor Devices Materials and Hetero-integration Graduate School of Advanced Technology National Taiwan University Taipei Taiwan Hsinchu Taiwan Department of Physics National Taiwan University Taipei Taiwan Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States

To develop low-power, non-volatile computing-in-memory device using ferroelectric transistor technologies, ferroelectric channel materials with scaled thicknesses are required. Two-dimensional semiconductors, such as molybdenum disulfide (MoS2), equipped with sliding ferroelectricity could provide an answer. However, achieving switchable electric polarization in epitaxial MoS2 remains challenging due to the absence of mobile domain boundaries. Here we show that polarity-switchable epitaxial rhombohedral-stacked (3R) MoS2 can be used as a ferroelectric channel in ferroelectric memory transistors. We show that a shear transformation can spontaneously occur in 3R MoS2 epilayers, producing heterostructures with stable ferroelectric domains embedded in a highly dislocated and unstable non-ferroelectric matrix. This diffusionless phase transformation process produces mobile screw dislocations that enable collective polarity control of 3R MoS2 via an electric field. Polarization–electric-field measurements reveal a switching field of 0.036 V nm−1 for shear-transformed 3R MoS2. Our sliding ferroelectric transistors are non-volatile memory units with thicknesses of only two atomic layers and exhibit an average memory window of 7 V with an applied voltage of 10 V, retention times greater than 104 seconds and endurance greater than 104 cycles. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

关键词： Molybdenum disulfide

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Language-specific Characteristic Assistance for Code-switching Speech Recognition

arXiv

引用

arXiv 2022年

作者： Song, Tongtong Xu, Qiang Ge, Meng Wang, Longbiao Shi, Hao Lv, Yongjie Lin, Yuqin Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Graduate School of Informatics Kyoto University Sakyo-ku Kyoto Japan Japan Advanced Institute of Science and Technology Ishikawa Japan

Dual-encoder structure successfully utilizes two language-specific encoders (LSEs) for code-switching speech recognition. Because LSEs are initialized by two pre-trained language-specific models (LSMs), the dual-encoder structure can exploit sufficient monolingual data and capture the individual language attributes. However, most existing methods have no language constraints on LSEs and underutilize language-specific knowledge of LSMs. In this paper, we propose a language-specific characteristic assistance (LSCA) method to mitigate the above problems. Specifically, during training, we introduce two language-specific losses as language constraints and generate corresponding language-specific targets for them. During decoding, we take the decoding abilities of LSMs into account by combining the output probabilities of two LSMs and the mixture model to obtain the final predictions. Experiments show that either the training or decoding method of LSCA can improve the model's performance. Furthermore, the best result can obtain up to 15.4% relative error reduction on the code-switching test set by combining the training and decoding methods of LSCA. Moreover, the system can process code-switching speech recognition tasks well without extra shared parameters or even retraining based on two pre-trained LSMs by using our method. Copyright © 2022, The Authors. All rights reserved.

关键词： Speech recognition

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Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon Coupling in Hybrid Organic-Inorganic Perovskites

Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon...

引用

Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Dasom Kim Jin Hou Geon Lee Ayush Agrawal Sunghwan Kim Hao Zhang Di Bao Andrey Baydin Wenjing WU Fuyang Tay Shengxi Huang Daivd Hagenmüller Elbert E. M. Chia Dai-Sik Kim Minah Seo Aditya D. Mohite Junichiro Kono Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Department of Electrical and Computer Engineering Rice University Houston TX USA Department of Materials Science and NanoEngineering Rice University Houston TX USA Sensor System Research Center Korea Institute of Science and Technology Seoul Republic of Korea Department of Physics and Astronomy Seoul National University Seoul Republic of Korea Department of Chemical and Biomolecular Engineering Rice University Houston TX USA Department of Physics Long-wavelength Nanotechnology Laboratory Quantum Photonics Institute Ulsan National Institute of Science and Technology (UNIST) Ulsan Republic of Korea Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Smalley-Curl institute Rice University Houston TX USA CESQ-ISIS (UMR 7006) Universite de Strasbourg and CNRS Strasbourg France KU-KIST Graduate School of Converging Science and Technology Korea University Seoul Republic of Korea Department of Physics and Astronomy Rice University Houston TX USA

Methods for harnessing vibrational states are desired for phonon-based technologies. We realized ultrastrong coupling of two phonon modes in perovskite materials induced by ultrastrong coupling with a common photonic ... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Couplings Phonons Laser modes Perovskites Electro-optical waveguides Photonics

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Observation of room-temperature ferroelectricity in elemental Te nanowires

arXiv

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arXiv 2022年

作者： Zhang, Jinlei Zhang, Jiayong Qi, Yaping Gong, Shuainan Zhao, Run Yang, Hongbin Wu, Zhenping Cui, Dapeng Wang, Lin Ma, Chunlan Gao, Ju Chen, Yong P. Jiang, Yucheng Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Av. Wai Long 999078 China Institute of Materials Science & Devices Suzhou University of Science and Technology Suzhou215009 China State Key Laboratory of Information Photonics and Optical Communications School of Science Beijing University of Posts and Telecommunications Beijing100876 China Department of Physics Faculty of Science National University of Singapore Singapore117551 Singapore School of Materials Science and Engineering Shanghai University Shanghai200444 China School for Optoelectronic Engineering Zaozhuang University Shandong277160 China Department of Physics and Astronomy Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Institute of Physics and Astronomy Villum Center for Hybrid Quantum Materials and Devices Aarhus University Aarhus-C8000 Denmark

Ferroelectrics are essential in low-dimensional memory devices for multi-bit storage and high-density integration. A polar structure is a necessary premise for ferroelectricity, mainly existing in compounds. However, it is usually rare in elemental materials, causing a lack of spontaneous electric polarization. Here, we report an unexpected room-temperature ferroelectricity in few-chain Te nanowires. Out-of-plane ferroelectric loops and domain reversal are observed by piezoresponse force microscopy. Through density functional theory, we attribute the ferroelectricity to the ion-displacement created by the interlayer interaction between lone pair electrons. Ferroelectric polarization can induce a strong field effect on the transport along the Te chain, supporting a self-gated field-effect transistor. It enables a nonvolatile memory with high in-plane mobility, zero supply voltage, multilevel resistive states, and a high on/off ratio. Our work provides new opportunities for elemental ferroelectrics with polar structures and paves a way towards applications such as low-power dissipation electronics and computing-in-memory devices. © 2022, CC BY.

关键词： Ferroelectricity

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User-Centric Conversational Recommendation with Multi-Aspect User Modeling

arXiv

引用

arXiv 2022年

作者： Li, Shuokai Xie, Ruobing Zhu, Yongchun Ao, Xiang Zhuang, Fuzhen He, Qing Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China WeChat Search Application Department Tencent China Institute of Artificial Intelligence Beihang Unversity Beijing100191 China SKLSDE School of Computer Science Beihang University Beijing100191 China Institute of Intelligent Computing Technology Suzhou China

Conversational recommender systems (CRS) aim to provide high-quality recommendations in conversations. However, most conventional CRS models mainly focus on the dialogue understanding of the current session, ignoring other rich multi-aspect information of the central subjects (i.e., users) in recommendation. In this work, we highlight that the user's historical dialogue sessions and look-alike users are essential sources of user preferences besides the current dialogue session in CRS. To systematically model the multi-aspect information, we propose a User-Centric Conversational Recommendation (UCCR) model, which returns to the essence of user preference learning in CRS tasks. Specifically, we propose a historical session learner to capture users' multi-view preferences from knowledge, semantic, and consuming views as supplements to the current preference signals. A multi-view preference mapper is conducted to learn the intrinsic correlations among different views in current and historical sessions via self-supervised objectives. We also design a temporal look-alike user selector to understand users via their similar users. The learned multi-aspect multi-view user preferences are then used for the recommendation and dialogue generation. In experiments, we conduct comprehensive evaluations on both Chinese and English CRS datasets. The significant improvements over competitive models in both recommendation and dialogue generation verify the superiority of UCCR. The source code will be available on https://***/lisk123/UCCR. Copyright © 2022, The Authors. All rights reserved.

关键词： Recommender systems

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Automatic Sleep Spindle Detection and Analysis in Patients with Sleep Disorders 1

引用

2nd International Workshop on Human Brain and Artificial Intelligence, HBAI 2020 held in Conjunction with IJCAI-PRICAI 2020

作者： Chen, Chao Zhu, Xuequan Belkacem, Abdelkader Nasreddine Lu, Lin Hao, Long You, Jia Shin, Duk Tan, Wenjun Huang, Zhaoyang Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin300072 China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300384 China Department of Computer and Network Engineering College of Information Technology UAE University Al Ain15551 United Arab Emirates Zhonghuan Information College Tianjin University of Technology Tianjin300380 China Department of Electronics and Mechatronics Tokyo Polytechnic University Atsugi243-0297 Japan School of Computer Science and Engineering Northeastern University Shenyang110189 China Department of Neurology Xuanwu Hospital Capital Medical University Beijing100088 China

ISBN: (数字)9789811612886

ISBN: (纸本)9789811612879

Nowadays, Sleep disorder is a common disease, and spindle spindles are important features of the second stage non-rapid eye movement (NREM) sleep. In this paper, we propose an improved automatic detection method of spindles based on wavelet transform. The spindles automatic detector is mainly composed of wavelet transform and clustering. We collected the electroencephalography (EEG) signals of six patients with sleep disorders all night for ten hours, and then preprocessed the data and other operations, and then used our improved method to detect the sleep EEG signals by spindles. By comparing with the previous automatic detection method not improved and another automatic detection method, the results show that the accuracy of sleep spindles detection can be effectively improved. The accuracy of the improved detector is 5.19% higher than before, and 9.7% higher than that of another method based on amplitude threshold. Finally, we made a simple comparison between people with sleep disorders and normal people. We found that there were significant differences in spindle density between people with sleep disorders and people without sleep disorders. The average spindle density in the normal population averaged 2.59 spindles per minute. People with sleep disorders had an average spindle density of 1.32 spindles per minute. In future research, our research direction is to improve the accuracy of spindles automatic detection by improving the spindles detector and study the difference of spindles between patients with sleep disorders and normal people in a large number of samples, so that the difference of spindles can be used as the basis for the diagnosis of sleep disorders. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Wavelet transforms

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Three-dimensional C-scan-based generation adversarial network with synthetic input to improve optical coherence tomography angiography

引用

Journal of Biomedical Optics 2025年第5期30卷 056006页

作者： Xu, Jingjiang Feng, Zhongwu Qiu, Haixia Tang, Peijun Gao, Kai Huang, Yanping Lan, Gongpu Qin, Jia An, Lin Jia, Gangyong Wu, Qing Foshan University Guangdong-Hong Kong-Macao Intelligent Micro-Nano Optoelectronic Technology Joint Laboratory Foshan China Guangdong Weiren Meditech Co. Ltd Innovation and Entrepreneurship Teams Project of Guangdong Pearl River Talents Program Foshan China Foshan University School of Mechatronic Engineering and Automation Foshan China Chinese PLA General Hospital The First Medical Centre Department of Laser Medicine Beijing China South China Normal University College of Biophotonics Guangzhou China Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science State Key Laboratory of Ophthalmology Guangdong Provincial Clinical Research Center for Ocular Diseases Zhongshan Ophthalmic Center Guangzhou China Hangzhou Dianzi University School of Computer Science Hangzhou China

Significance: Optical coherence tomography angiography (OCTA) usually suffers from the inherent random fluctuations of noise and speckles in the imaging system. Previous deep learning methods have mainly focused on improving the quality of B-scan blood flow images or en face projection images. We propose a deep learning method to reconstruct high-quality 3D vasculature, which fully utilizes the volumetric OCTA data and the topological features of the vascular network. Aim: We propose a deep learning method called the three-dimensional C-scanbased generation adversarial network (3DCS-GAN) to improve vascular visualization for volumetric OCTA data. Approach: To train the network, we superimposed the single-shot en face OCTA images on avascular noisy C-scan images to synthesize the input data and used the multiple averaged en face OCTA images as the reference labels. The deep learning algorithm is based on Pix2Pix architecture and consists of a generator model and a discriminator model. A perceptual loss function was utilized by combining content loss and adversarial loss. The proposed algorithm is applied to the C-scan images depth-by-depth to suppress the background noise and enhance vascular visualization in the 3D OCTA data. Results: The proposed method has improved the contrast-to-noise ratio of crosssectional OCTA images by ∼2 times. It greatly enhances the visualization of blood vessels in the deep layer and offers much clearer blood vessel topology in the 3D volume-rendering of OCTA. 3DCS-GAN has exhibited superior image enhancement compared with alternative methods. It has been used to enhance the OCTA images of port wine stain disease for clinical investigation. Conclusions: It demonstrates that the proposed 3DCS-GAN can greatly improve vascular visualization in the deep layer, provide better image quality than the multiple averaged OCTA images, and achieve superior image enhancement for volumetric OCTA data. © 2025 SPIE. All rights reserved.

关键词： Image enhancement

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An overview on the application of graph neural networks in wireless networks

arXiv

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arXiv 2021年

作者： He, Shiwen Xiong, Shaowen Ou, Yeyu Zhang, Jian Wang, Jiaheng Huang, Yongming Zhang, Yaoxue School of Computer Central South University Changsha410083 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing210096 China National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

In recent years, with the rapid enhancement of computing power, deep learning methods have been widely applied in wireless networks and achieved impressive performance. To effectively exploit the information of graph-structured data as well as contextual information, graph neural networks (GNNs) have been introduced to address a series of optimization problems of wireless networks. In this overview, we first illustrate the construction method of wireless communication graph for various wireless networks and simply introduce the progress of several classical paradigms of GNNs. Then, several applications of GNNs in wireless networks such as resource allocation and several emerging fields, are discussed in detail. Finally, some research trends about the applications of GNNs in wireless communication systems are discussed. © 2021, CC BY.

关键词： Deep learning

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UAE: Universal Anatomical Embedding on Multi-modality Medical Images

arXiv

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arXiv 2023年

作者： Bai, Xiaoyu Bai, Fan Huo, Xiaofei Ge, Jia Lu, Jingjing Ye, Xianghua Yan, Ke Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Beijing United Family Hospital China The First Affiliated Hospital Zhejiang University Hangzhou China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Peking Union Medical College Hospital Beijing China

Identifying specific anatomical structures (e.g., lesions or landmarks) in medical images plays a fundamental role in medical image analysis. Exemplar-based landmark detection methods are receiving increasing attention since they can detect arbitrary anatomical points in inference while do not need landmark annotations in training. They use self-supervised learning to acquire a discriminative embedding for each voxel within the image. These approaches can identify corresponding landmarks through nearest neighbor matching and has demonstrated promising results across various tasks. However, current methods still face challenges in: (1) differentiating voxels with similar appearance but different semantic meanings (e.g., two adjacent structures without clear borders);(2) matching voxels with similar semantics but markedly different appearance (e.g., the same vessel before and after contrast injection);and (3) cross-modality matching (e.g., CT-MRI landmark-based registration). To overcome these challenges, we propose universal anatomical embedding (UAE), which is a unified framework designed to learn appearance, semantic, and cross-modality anatomical embeddings. Specifically, UAE incorporates three key innovations: (1) semantic embedding learning with prototypical contrastive loss;(2) a fixed-point-based matching strategy;and (3) an iterative approach for cross-modality embedding learning. We thoroughly evaluated UAE across intra- and inter-modality tasks, including one-shot landmark detection, lesion tracking on longitudinal CT scans, and CT-MRI affine/rigid registration with varying field of view. Our results suggest that UAE outperforms state-of-the-art methods, offering a robust and versatile approach for landmark based medical image analysis tasks. Code and trained models are available at: https://***/bgsB3. © 2023, CC BY-NC-ND.

关键词： Semantics

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Face Mask Integrated with Flexible and Wearable Manganite Oxide Respiration Sensor

SSRN

引用

SSRN 2023年

作者： Ye, Lianxu Wu, Fan Xu, Ruixing Zhang, Di Lu, Juanjuan Wang, Chuanlong Dong, Anjiang Xu, Sichen Xue, Lejun Fan, Zixin Xu, Longjie Li, Kaifeng Li, Dong Kursumovic, Ahmed Zhao, Run Tang, Rujun Qiu, Lei Wang, Haiyan MacManus-Driscoll, Judith L. Jing, Qingshen Li, Weiwei Yang, Hao College of Physics MIIT Key Laboratory of Aerospace Information Materials and Physics Nanjing University of Aeronautics and Astronautics Nanjing211106 China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China School of Microelectronics & Data Science Anhui University of Technology Maanshan243002 China School of Materials Engineering School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Jiangsu Key Laboratory of Thin Films School of Physical Science and Technology Soochow University Suzhou215006 China Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China Department of Materials Science & Metallurgy University of Cambridge 27 Charles Babbage Road CambridgeCB3 0FS United Kingdom James Watt School of Engineering University of Glasgow GlasgowG12 8LT United Kingdom State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Nanjing211106 China

Face masks are key personal protective equipment for reducing exposure to viruses and other environmental hazards such as air pollution. Integrating flexible and wearable sensors into face masks can provide valuable insights into personal and public health. The advantages that a breath-monitoring face mask requires, including multi-functional sensing ability and continuous, long-term dynamic breathing process monitoring, have been underdeveloped to date. Here, we design an effective human breath monitoring face mask based on a flexible La0.7Sr0.3MnO3 (LSMO)/Mica respiration sensor. The sensor’s capabilities and systematic measurements are investigated under two application scenes, namely clinical monitoring mode and daily monitoring mode, to monitor, recognise, and analyse different human breath status, i.e., cough, normal breath, and deep breath. This sensing system exhibits super-stability and multi-modal capabilities in continuous and long-time monitoring of the human breath. We determine that during monitoring human breath, thermal diffusion in LSMO is responsible for the change of resistance in flexible LSMO/Mica sensor. Both simulated and experimental results demonstrate good discernibility of the flexible LSMO/Mica sensor operating at different breath status. Our work opens a route for the design of novel flexible and wearable electronic devices. © 2023, The Authors. All rights reserved.

关键词： Mica

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