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The Roadmap of 2D Materials and Devices Toward Chips

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Nano-Micro Letters 2024年第6期16卷 343-438页

作者： Anhan Liu Xiaowei Zhang Ziyu Liu Yuning Li Xueyang Peng Xin Li Yue Qin Chen Hu Yanqing Qiu Han Jiang Yang Wang Yifan Li Jun Tang Jun Liu Hao Guo Tao Deng Songang Peng He Tian Tian‑Ling Ren School of Integrated Circuits and Beijing National Research Center for Information Science and technology(BNRist) Tsinghua UniversityBeijing 100049People’s Republic of China School of Microelectronics Fudan UniversityShanghai 200433People’s Republic of China State Key laboratory of Dynamic Measurement technology Shanxi Province Key Laboratory of Quantum Sensing and Precision MeasurementNorth University of ChinaTaiyuan 030051People’s Republic of China High‑Frequency High‑Voltage Device and Integrated Circuits R&D Center Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029People’s Republic of China School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044People’s Republic of China School of Integrated Circuits University of Chinese Academy of SciencesBeijing 100049People’s Republic of China IMECAS-HKUST-joint laboratory of Microelectronics Beijing 100029People’s Republic of China

Due to the constraints imposed by physical effects and performance degra certain limitations in sustaining the advancement of Moore’s ***-dimensional(2D)materials have emerged as highly promising candidates for the post-Moore era,offering significant potential in domains such as integrated circuits and next-generation ***,in this review,the progress of 2D semiconductors in process engineering and various electronic applications are summarized.A careful introduction of material synthesis,transistor engineering focused on device configuration,dielectric engineering,contact engineering,and material integration are given *** 2D transistors for certain electronic applications including digital and analog circuits,heterogeneous integration chips,and sensing circuits are ***,several promising applications(artificial intelligence chips and quantum chips)based on specific mechanism devices are ***,the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed,and potential development pathways or roadmaps are further speculated and outlooked.

关键词： Two-dimensional materials Roadmap Integrated circuits Post-Moore era

Visual attention network

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Computational Visual Media 2023年第4期9卷 733-752页

作者： Meng-Hao Guo Cheng-Ze Lu Zheng-Ning Liu Ming-Ming Cheng Shi-Min Hu Department of Computer Science Tsinghua UniversityBeijingChina Nankai University TianjinChina Fitten Tech BeijingChina

While originally designed for natural language processing tasks,the self-attention mechanism has recently taken various computer vision areas by ***,the 2D nature of images brings three challenges for applying self-attention in computer vision:(1)treating images as 1D sequences neglects their 2D structures;(2)the quadratic complexity is too expensive for high-resolution images;(3)it only captures spatial adaptability but ignores channel *** this paper,we propose a novel linear attention named large kernel attention(LKA)to enable self-adaptive and long-range correlations in self-attention while avoiding its ***,we present a neural network based on LKA,namely Visual Attention Network(VAN).While extremely simple,VAN achieves comparable results with similar size convolutional neural networks(CNNs)and vision transformers(ViTs)in various tasks,including image classification,object detection,semantic segmentation,panoptic segmentation,pose estimation,*** example,VAN-B6 achieves 87.8%accuracy on ImageNet benchmark,and sets new state-of-the-art performance(58.2%PQ)for panoptic ***,VAN-B2 surpasses Swin-T 4%mloU(50.1%vs.46.1%)for semantic segmentation on ADE20K benchmark,2.6%AP(48.8%vs.46.2%)for object detection on COCO *** provides a novel method and a simple yet strong baseline for the *** code is available at https://***/Visual-Attention-Network.

关键词： vision backbone deep learning ConvNets attention

Smoothness preserving layout for dynamic labels by hybrid optimization

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Computational Visual Media 2022年第1期8卷 149-163页

作者： Yu He Guo-Dong Zhao Song-Hai Zhang Department of Computer Science and technology Tsinghua UniversityBeijingChina Department of Computer Science and technology Tianjin University of TechnologyTianjinChina

Stable label movement and smooth label trajectory are critical for effective information *** label changes cannot be avoided by whatever forced directed methods due to the unreliability of resultant force or global optimization methods due to the complex trade-off on the different *** solve this problem,we proposed a hybrid optimization method by taking advantages of the merits of both *** first detect the spatial-temporal intersection regions from whole trajectories of the features,and initialize the layout by optimization in decreasing order by the number of the involved *** label movements between the spatial-temporal intersection regions are determined by force directed *** cope with some features with high speed relative to neighbors,we introduced a force from future,called temporal force,so that the labels of related features can elude ahead of time and retain smooth *** also proposed a strategy by optimizing the label layout to predict the trajectories of features so that such global optimization method can be applied to streaming data.

关键词： label layout smoothness preserving dynamic label forced based static optimization hybrid optimization

Visualization of COVID-19 spread based on spread and extinction indexes

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Science China(Information Sciences) 2020年第6期63卷 220-222页

作者： Song-Hai ZHANG Yun CAI Jian LI Department of Computer Science and technology Tsinghua University Beijing National Research Center for Information Science and technology(BNRist)

From December 2019, Coronavirus disease 2019(COVID-19) rapidly spread from the city of Wuhan (Hubei Province) to all of China, which was exacerbated by mass population movements associated with the Chinese Spring Festival. To date, more than 80000 people have been infected,and more than 3000 have died. In addition,COVID-19 has already spread to 142 countries,

关键词： Visualization of COVID-19 spread based on spread and extinction indexes

SpinNet: Spinning convolutional network for lane boundary detection

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Computational Visual Media 2019年第4期5卷 417-428页

作者： Ruochen Fan Xuanrun Wang Qibin Hou Hanchao Liu Tai-Jiang Mu tsinghua University Beijing100084China Nankai University Tianjin300350China

In this paper,we propose a simple but effective framework for lane boundary detection,called Spin *** that cars or pedestrians often occlude lane boundaries and that the local features of lane boundaries are not distinctive,therefore,analyzing and collecting global context information is crucial for lane boundary *** this end,we design a novel spinning convolution layer and a brand-new lane parameterization branch in our network to detect lane boundaries from a global *** extract features in narrow strip-shaped fields,we adopt stripshaped convolutions with kernels which have 1×n or n×1 shape in the spinning convolution *** tackle the problem of that straight strip-shaped convolutions are only able to extract features in vertical or horizontal directions,we introduce the concept of feature map rotation to allow the convolutions to be applied in multiple directions so that more information can be collected concerning a whole lane ***,unlike most existing lane boundary detectors,which extract lane boundaries from segmentation masks,our lane boundary parameterization branch predicts a curve expression for the lane boundary for each pixel in the output feature *** the network utilizes this information to predict the weights of the curve,to better form the final lane *** framework is easy to implement and end-to-end *** show that our proposed Spin Net outperforms state-of-the-art methods.

关键词： object detection lane boundary detection autonomous driving deep learning

Detecting human-object interaction with multi-level pairwise feature network

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Computational Visual Media 2021年第2期7卷 229-239页

作者： Hanchao Liu Tai-Jiang Mu Xiaolei Huan Key laboratory of Pervasive Computing Ministry of EducationBNRistDepartment of Computer Science and TechnologyTsinghua UniversityBeijing 100084China College of Information Sciences and technology Pennsylvania State UniversityUniversity ParkPA 16802USA.

Human–object interaction(HOI)detection is crucial for human-centric image understanding which aims to infer human,action,object triplets within an *** studies often exploit visual features and the spatial configuration of a human–object pair in order to learn the action linking the human and object in the *** argue that such a paradigm of pairwise feature extraction and action inference can be applied not only at the whole human and object instance level,but also at the part level at which a body part interacts with an object,and at the semantic level by considering the semantic label of an object along with human appearance and human–object spatial configuration,to infer the *** thus propose a multi-level pairwise feature network(PFNet)for detecting human–object *** network consists of three parallel streams to characterize HOI utilizing pairwise features at the above three levels;the three streams are finally fused to give the action *** experiments show that our proposed PFNet outperforms other state-of-the-art methods on the VCOCO dataset and achieves comparable results to the state-of-the-art on the HICO-DET dataset.

关键词： human–object interaction detection pairwise feature network deep learning multi-level object instance

A new dataset of dog breed images and a benchmark for fine-grained classification

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Computational Visual Media 2020年第4期6卷 477-487页

作者： Ding-Nan Zou Song-Hai Zhang Tai-Jiang Mu Min Zhang Department of Computer Science and technology BNRistTsinghua UniversityBeijing 100084China NaJiu Company Hunan 410022China Harvard Medical School Brigham and Women’s HospitalBostonMA 02115USA

In this paper, we introduce an image dataset for fine-grained classification of dog breeds: the tsinghua Dogs Dataset. It is currently the largest dataset for fine-grained classification of dogs, including 130 dog breeds and 70,428 real-world images. It has only one dog in each image and provides annotated bounding boxes for the whole body and head. In comparison to previous similar datasets, it contains more breeds and more carefully chosen images for each breed. The diversity within each breed is greater,with between 200 and 7000+ images for each *** of the whole body and head makes the dataset not only suitable for the improvement of finegrained image classification models based on overall features, but also for those locating local informative parts. We show that dataset provides a tough challenge by benchmarking several state-of-the-art deep neural models. The dataset is available for academic purposes at https://***/ThuDogs/.

关键词： fine-grained classification dog dataset benchmark

Traffic signal detection and classification in street views using an attention model

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Computational Visual Media 2018年第3期4卷 253-266页

作者： Yifan Lu Jiaming Lu Songhai Zhang Peter Hall TNList Tsinghua University Department of Computer Science University of Bath

Detecting small objects is a challenging *** focus on a special case:the detection and classification of traffic signals in street *** present a novel framework that utilizes a visual attention model to make detection more efficient,without loss of accuracy,and which *** attention model is designed to generate a small set of candidate regions at a suitable scale so that small targets can be better located and *** order to evaluate our method in the context of traffic signal detection,we have built a traffic light benchmark with over 15,000 traffic light instances,based on tencent street view *** have tested our method both on the dataset we have built and the tsinghua–tencent 100K(TT100K)traffic sign *** show that our method has superior detection performance and is quicker than the general faster RCNN object detection framework on both *** is competitive with state-of-theart specialist traffic sign detectors on TT100K,but is an order of magnitude *** show generality,we tested it on the LISA dataset without tuning,and obtained an average precision in excess of 90%.

关键词：交通信号检测交通灯计算机技术交通管理

A Large Chinese Text Dataset in the Wild

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Journal of Computer Science & technology 2019年第3期34卷 509-521页

作者： Tai-Ling Yuan Zhe Zhu Kun Xu Cheng-Jun Li Tai-Jiang Mu Shi-Min Hu Department of Computer Science and technology Tsinghua UniversityBeijing 100084China Department of Radiology Duke UniversityNorth Carolina 27708U.S.A. tencent technology(Beijing)Co.Ltd. Beijing 100080China

In this paper,we introduce a very large Chinese text dataset,in the *** optical character recognition(OCR)in document images is well studied and many commercial tools are available,the detection and recognition of text in natural images is still a challenging problem,especially for some more complicated character sets such as Chinese *** of training data has always been a problem,especially for deep learning methods which require massive training *** this paper,we provide details of a newly created dataset of Chinese text with about 1 million Chinese characters from 3850 unique ones annotated by experts in over 30 000 street view *** is a challenging dataset with good diversity containing planar text,raised text,text under poor illumination,distant text,partially occluded text,*** each character,the annotation includes its underlying character,bounding box,and six *** attributes indicate the charactcr's background complexity,appearance,style,*** the dataset,we give baseline results using state-of-the-art methods for tliree tasks:character recognition(top-1 accuracy of 80.5%),character detection(AP of 70.9%),and text line detection(AED of 22.1).The dataset,source code,and trained models are publicly available.

关键词： Chinese text dataset Chinese text detection Chinese text recognition