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A Valuable and Low-Budget Process Scheme of Equivalized 1 nm technology Node Based on 2D Materials

Nano-Micro Letters 2025年第8期17卷 294-305页

作者： Yang Shen Zhejia Zhang Zhujun Yao Mengge Jin Jintian Gao Yuhan Zhao Wenzhong Bao Yabin Sun He Tian College of Integrated Circuit Science and Engineering Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghai 200241People’s Republic of China Institute of Microelectronics and Beijing National Research Center for Information Science and Technology(BNRist) Tsinghua UniversityBeijing 100084People’s Republic of China State Key Laboratory of ASIC and System School of MicroelectronicsFudan UniversityShanghai 200433People’s Republic of China Shaoxin Laboratory Shaoxing 312000People’s Republic of China

Emerging two-dimensional(2D)semiconductors are among the most promising materials for ultra-scaled transistors due to their intrinsic atomic-level *** the stacking process advances,the complexity and cost of nanosheet field-effect transistors(NSFETs)and complementary FET(CFET)continue to *** 1 nm technology node is going to be based on Si-CFET process according to international roadmap for devices and systems(IRDS)(2022,https://***/),but not publicly confirmed,indicating that more possibilities still *** miniaturization advantage of 2D semiconductors motivates us to explore their potential for reducing process costs while matching the performance of next-generation nodes in terms of area,power consumption and *** this study,a comprehensive framework is built.A set of MoS2 NSFETs were designed and fabricated to extract the key parameters and *** then for benchmarking,the sizes of 2D-NSFET are scaled to a extent that both of the Si-CFET and 2D-NSFET have the same average device *** these conditions,the frequency of ultra-scaled 2D-NSFET is found to improve by 36%at a fixed power *** work verifies the feasibility of replacing silicon-based CFETs of 1 nm node with 2D-NSFETs and proposes a 2D technology solution for 1 nm nodes,i.e.,“2D eq 1 nm”*** the same time,thanks to the lower characteristic length of 2D semiconductors,the miniaturized 2D-NSFET achieves a 28%frequency increase at a fixed power ***,developing a standard cell library,these devices obtain a similar trend in 16-bit RISC-V *** work quantifies and highlights the advantages of 2D semiconductors in advanced nodes,offering new possibilities for the application of 2D semiconductors in high-speed and low-power integrated circuits.

关键词： Two-dimensional semiconductors 1 nm technology node Nanosheet field-effect transistors Complementary field-effect transistors Horizontal scaling

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Listen With Seeing: Cross-Modal Contrastive Learning for Audio-Visual Event Localization

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IEEE Transactions on Multimedia 2025年 27卷 2650-2665页

作者： Sun, Chao Chen, Min Zhu, Chuanbo Zhang, Sheng Lu, Ping Chen, Jincai Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science and Technology Wuhan430074 China South China University of Technology School of Computer Science and Engineering Guangzhou510640 China Pazhou Laboratory Guangzhou510640 China

In real-world physiological and psychological scenarios, there often exists a robust complementary correlation between audio and visual signals. Audio-Visual Event Localization (AVEL) aims to identify segments with Audio-Visual Events (AVEs) that contain both audio and visual tracks in unconstrained videos. Prior studies have predominantly focused on audio-visual cross-modal fusion methods, overlooking the fine-grained exploration of the cross-modal information fusion mechanism. Moreover, due to the inherent heterogeneity of multi-modal data, inevitable new noise is introduced during the audio-visual fusion process. To address these challenges, we propose a novel Cross-modal Contrastive Learning network (CCLN) for AVEL, comprising a backbone network and a branch network. In the backbone network, drawing inspiration from physiological theories of sensory integration, we elucidate the process of audio-visual information fusion, interaction, and integration from an information-flow perspective. Notably, the Self-constrained Bi-modal Interaction (SBI) module is a bi-modal attention structure integrated with audio-visual fusion information, and through gated processing of the audio-visual correlation matrix, it effectively captures inter-modal correlation. The Foreground Event Enhancement (FEE) module emphasizes the significance of event-level boundaries by elongating the distance between scene events during training through adaptive weights. Furthermore, we introduce weak video-level labels to constrain the cross-modal semantic alignment of audio-visual events and design a weakly supervised cross-modal contrastive learning loss (WCCL Loss) function, which enhances the quality of fusion representation in the dual-branch contrastive learning framework. Extensive experiments conducted on the AVE dataset for both fully supervised and weakly supervised event localization, as well as Cross-Modal Localization (CML) tasks, demonstrate the superior performance of our model compa

关键词： information fusion

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Iterative Bounded Distance Decoding With Random Flipping for Product-Like Codes

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IEEE Transactions on Communications 2025年第5期73卷 2864-2875页

作者： Li, Guorong Wang, Shiguo Zhao, Shancheng Jinan University National and Local Joint Research Center of Network Security Detection and Protection Technology College of Information Science and Technology Guangzhou510632 China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou Key Laboratory of Data Security and Privacy Preserving Guangzhou510632 China Changsha University of Science and Technology Computer and Communication Engineering Institute Changsha410114 China

Product-like codes are widely used in high-speed communication systems since they can be decoded with low-complexity hard decision decoders (HDDs). To meet the growing demand of data rates, enhanced HDDs are required. In this paper, we propose a novel soft-aided HDD (SA-HDD), termed iterative bounded distance decoding with random flipping (iBDD-RF), for product-like codes. In iBDD-RF, the soft reliability of a bit is a weighted sum of the output of bounded distance decoder (BDD) and the channel log-likelihood ratio (LLR). When the amplitude of the soft reliability of a bit is less than a given threshold, it is flipped with a given probability. This random flipping may make the decoder escape from the local optimum. To optimize the threshold and the flipping probability, we derive the density evolution (DE) equations of iBDD-RF for product codes (PCs) and staircase codes (SCs). Our extensive numerical results show that iBDD-RF outperforms iBDD with scaled reliability (iBDD-SR) over the binary-input additive white Gaussian noise (Bi-AWGN) channel. Particularly, for a PC with (255,239,2) Bose-Chaudhuri-Hocquenghem (BCH) code and an SC with (254,230,3) BCH code, iBDD-RF performs about 0.25 dB and 0.28 dB better than iBDD-SR, respectively. © 1972-2012 IEEE.

关键词： Optical fiber communication

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Temporal feature-based memory neural network for probabilistic-shaping polarization-division multiplexed ultrahigh-order QAM coherent optical transmission

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Chinese Optics Letters 2025年第05期 28-32页

作者：黄雪晶高明义范佳敏戈懿凡由骁迪沈纲祥 Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network Suzhou Key Laboratory of Advanced Optical Communication Network TechnologySchool of Electronic and Information EngineeringSoochow University

High-speed single-carrier transmission can be achieved by increasing the modulation format cardinality for higher spectral ***,ultrahigh-order QAM signals are usually more susceptible to various ***,we propose a temporal feature-based memory (TFM) neural network (NN) equalizer to effectively mitigate signals'impairments in ultrahigh-order *** temporal convolutional network is utilized as a feature extraction layer to significantly improve the performance of the bidirectional long short-term memory *** TFM-NN equalizer was experimentally validated in a probabilistically shaped polarization-division multiplexed 1024/4096-QAM coherent optical transmission system,and raw spectral efficiencies of 16.190 and 21.188 bit/s/Hz have been achieved at normalized generalized mutual information thresholds.

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Effects of visual rotation axis and clockwise/counterclockwise directions on circular vection in virtual reality 24

Effects of visual rotation axis and clockwise/counterclockwi...

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2024 International Conference on Virtual Reality, Image and Signal Processing, ICVISP 2024

作者： Xu, Xiaotian Song, Zhe Zhang, Kexin Xu, Yan Zhang, Yuankun Fan, Xiaoya Pu, Fang Key Laboratory of Human Motion Analysis and Rehabilitation Technology of the Ministry of Civil Affairs Beihang University Beijing China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian University of Technology Liaoning Dalian China State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing China

ISBN: (纸本)9798400710926

Vection, an illusion of self-motion induced by visual stimulus frequently experienced in virtual reality, significantly impacts user experiences in gaming, driving simulation, and rehabilitation training. User often experience circular vection, an illusion of self-rotation. However, the effects of visual rotation axes and rotation directions on circular vection remain elusive. We employ a 3 (rotation axis: sagittal, coronal, vertical) × 2 (rotation direction: clockwise, counterclockwise) repeated-measures design. Twenty participants were recruited. They were exposed to a virtual visual rotating drum at 60 °/s to induce circular vection. The real-time estimation of perceived self-motion rotation velocity (PSRV) was obtained. Results indicated that during the 30-second constant rotation, the group-level PSRV showed an initial rapid increase followed by a slower rise. A significant interaction effect between rotation axis and direction was revealed. The rotation direction showed no significant effect on PSRV under sagittal and vertical conditions. However, under coronal condition, the PSRV was significantly higher when rotating counterclockwise, compared to rotation clockwise. In addition, when rotating counterclockwise, the highest PSRV appeared under sagittal condition, with the lowest under the coronal condition and the middle under the vertical condition. These findings provide valuable insights for designing VR scenarios with better user experience by vection manipulation. © 2024 Copyright held by the owner/author(s).

关键词： Virtual reality

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BOTDA temperature extraction based on coordinate attention residual network 4

BOTDA temperature extraction based on coordinate attention r...

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4th International Computational Imaging Conference, CITA 2024

作者： Zhang, Zhihao Qian, Yuhao Wang, Liang Ma, Xiaole Yu, Kuanglu Beijing Key Laboratory of Advanced Information Science and Network Technology Institute of Information Science Beijing Jiao tong University Beijing100044 China Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781510688834

We have proposed and demonstrated a coordinate-attention-based residual network (CARN) that is capable of accurately extracting temperature distributions in Brillouin gain spectra with low signal-to-noise ratio (SNR). The performance of the CARN network is statistically analyzed experimentally and compare it with the conventional Lorentz curve fitting method (LCF) and the BFSCNN network, which show that CARN has higher temperature extraction accuracy and stronger noise tolerance and robustness over a wide range of temperature and SNR. At a relatively low 6.5dB SNR, the average temperature RMSE error extracted by CARN at the end of a 9.6km sensing fiber is only 1.08°C, which is an improvement of about 54% and 29%, respectively. The average SD is only 0.75℃, an improvement of about 64% and 38%, respectively. © 2025 SPIE.

关键词： Signal to noise ratio Temperature distribution Signal detection Temperature metrology Convolution Data processing Optical fibers Signal processing Statistical analysis

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Comparison of gap-filling methods for generating landsat-like land surface temperatures under all-weather conditions

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ISPRS Journal of Photogrammetry and Remote Sensing 2025年 225卷 113-130页

作者： Guo, Jiali Quan, Jinling Zhan, Wenfeng Wen, Zhongguan State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research CAS Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100190 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System Science Nanjing University Jiangsu Nanjing210046 China College of Environmental Sciences and Engineering Peking University Beijing100871 China

Thermal infrared remote sensors provide cost-effective and widespread land surface temperatures (LSTs) but often with spatiotemporal gaps due to discrete sampling and synoptic disturbance, greatly limiting their reliability and application. Current gap-filling methods have been primarily developed and validated for medium- to low-resolution LSTs;however, with rising demand for spatiotemporally continuous, high-resolution (tens of meters like Landsat) LSTs across disciplines, there is an urgent need to assess these methods’ applicability and uncertainty at higher spatial resolutions under a unified framework. In this study, we apply eight typical and hybrid methods, including temporal interpolation, spatiotemporal interpolation, weight-based fusion, learning-based fusion, and four standard annual temperature cycle (ATC)-based hybrid reconstructions, to fill gaps in irregularly spaced Landsat series over Weishan, Huairou, and Yulin, China. These sites represent cropland in a sub-humid plain, forest in a sub-humid mountain region, and grassland in the semi-arid Loess Plateau. We evaluate their performance in terms of spatiotemporal pattern, statistical accuracy, sensitivity to input data quality and distribution, and adaptability to different synoptic and surface conditions based on cloudy Landsat data and in-situ measurements. Results reveal that the enhanced ATC (EATC) method is optimal among these methods, capturing all-weather spatiotemporal dynamics at the Landsat scale with superior accuracy and robustness under various input, cloud, and ground conditions. In addition, the ATC-based hybrid methods do not necessarily improve the statistical accuracy over their respective typical ones. This comprehensive evaluation provides valuable insights into the selection of appropriate gap-filling methods for generating Landsat-like LSTs under all-weather conditions and highlights the need for further advancements especially in addressing abrupt changes in land cover types an

关键词： Surface stress

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Temperature Self-Reference Gas Pressure Sensor Based on Intracavity Sensing of Fiber Ring Laser

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Journal of Lightwave technology 2025年第9期43卷 4259-4265页

作者： Shi, Boya Huo, Xinjuan Wang, Shaona Niu, Pingjuan Tang, Longhuang Li, Hongmei Yao, Jianquan Shi, Jia Tiangong University Tianjin Key Laboratory of Optoelectronic Detection Technology and System School of Electronics and Information Engineering Tianjin300387 China China Academy of Engineering Physics Institute of Fluid Physics Mianyang621900 China Aero Engine Corporation of China Commercial Aircraft Engine Company Limited Shanghai200241 China Ministry of Education Tianjin University Key laboratory of Opto-Electronic Information Technology Tianjin300072 China

The temperature cross-sensitivity is always challenging for the design and application of fiber-optic gas pressure sensors. Here, a temperature self-reference gas pressure sensor based on intracavity sensing of fiber ring laser has been demonstrated. The Fabry-Perot interferometer and fiber Bragg grating are cascaded as reflection sensing head and coupled into a fiber ring laser. The Fabry-Perot interferometer shows high sensitivities of gas pressure and temperature because of its specific fiber micro cavity structure. The fiber Bragg grating has narrow-band filtering characteristics at a certain wavelength, which ensures the desired accuracy for intracavity sensing, and has good temperature response characteristics. In the temperature self-reference sensing system, the output wavelength of the fiber ring laser is only modulated by the sensor temperature, while the output intensity is simultaneously modulated by the sensor temperature and gas pressure. The sensing model of the temperature self-reference gas pressure sensor has been presented. The experiment verifies that the scheme can measure gas pressure and temperature simultaneously. The stability and measurement errors have been analyzed. The proposed sensing system provides a potential solution for solving temperature cross-sensitivity in gas pressure measurement and can be extensible to similar fiber-optic sensors. © 1983-2012 IEEE.

关键词： Fabry-Perot interferometers

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Point cloud classification method based on local dynamic edge graph convolution and global information fusion 4

Point cloud classification method based on local dynamic edg...

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4th International Conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Du, Jie Sheng, Zhiwei Jin, Xingyu Huang, Yuanyuan Tian, Junbiao Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

The development of automatic driving, unmanned aerial vehicle detection and virtual reality cannot be separated from the research of 3D point cloud. Existing studies either lack of local shape capture and local spatial feature description of point cloud, resulting in poor accuracy of point cloud classification network, or pursue fine and complex local feature extractors, sacrificing extraction time and memory overhead to extract classification progress, but the results show that the effect is not significantly improved. Therefore, this paper proposes a Point cloud classification method based on Local Point Dynamic Edge Graph Convolution and Global information Fusion. Among them, the local point dynamic edge graph convolution module improves the edge graph convolution, which can improve the local extraction feature effect without deepening the local feature extractor, and reduce the time and memory overhead. The global information fusion block concatenates multi-layer local features to extract and fuse feature information to improve the classification accuracy of the network. Our network is tested on the public datasets ModelNet40 dataset and ScanObjectNN dataset. The results show that compared with the current mainstream point cloud classification algorithms, the point cloud classification accuracy of the proposed classification method is improved, and it has good robustness. © 2025 SPIE.

关键词： Point clouds Feature extraction Convolution Data modeling information fusion Data conversion Education and training Transformers Deep learning Statistical modeling

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MMH-FE:AMulti-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural network Training Based on Functional Encryption

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Computers, Materials & Continua 2025年第3期82卷 5387-5405页

作者： Hao Li Kuan Shao Xin Wang Mufeng Wang Zhenyong Zhang The State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing *** address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training ***,we design a multi-precision functional encryption computation based on Euclidean ***,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced ***,we conduct experiments on three *** results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.

关键词： Functional encryption multi-sourced heterogeneous data privacy preservation neural networks

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