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Physics based circuit compatible model for hybrid antiferroelectric random access memory

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Science China(Information Sciences) 2025年第1期68卷 396-397页

作者： Qiuxia WU Yue PENG Wenwu XIAO Wenxuan MA Shuo ZHANG Litao SUN Chunfu ZHANG Xiaohua MA Yue HAO school of microelectronics Xidian University Xi'an Uni IC Semiconductors

Since the discovery of HfO2-based ferroelectric （FE） materials in 2011,considerable advancements have been made in material preparation,mechanism research,and device realization [1].Consequently,these materials are now regarded as leading candidates for embedded non-volatile memory（eNVM）.Beyond eNVM applications,recent studies have demonstrated that memory devices with antiferroelectric（AFE） films can achieve high response speeds,low latency,and reduced power consumption while maintaining excellent endurance characteristics [2,3].Additionally,the typical double hysteresis loops and multiple non-overlapping polarization current peaks of AFE films offer a novel approach to designing multistate ***,in order to further promote the application of high storage density AFE,an AFRAM electronic design automation （EDA） model must be developed,and it has yet to be proposed.

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Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(Information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG school of Journalism and Communication Sun Yat-sen University school of software Technology Zhejiang University school of software Engineering Sun Yat-sen University school of software Dalian University of Technology school of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel software Technology Nanjing University school of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences school of Computer Science Fudan University State Key Laboratory of Complex & Critical software Environment (CCSE) School of Software Beihang University school of Computer and Information Technology Beijing Jiaotong University school of Computer Science and Technology Harbin Institute of Technology school of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

Analysis and solution of streak effect in high dynamic range CMOS image sensors

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Science China(Information Sciences) 2025年第1期68卷 402-403页

作者： Wanbin ZHA Jiangtao XU Jinghua AO Kaiming NIE Zhiyuan GAO school of microelectronics Tianjin University Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology Tianjin University

The high dynamic range (HDR) CMOS image sensor has been widely used in many fields,such as autonomous driving and security guarding [1].The dual conversion gain (DCG)structure,which adjusts the conversion gain (CG) by changing the capacitors connected to the floating diffusion (FD)nodes in the pixel,is an effective and prevalent method for realizing HDR imaging [2].While the DCG structure can extend the dynamic range,in the practical HDR imaging process,the substantial number of photo-generated electrons in bright areas can cause significant voltage variations in the pixel *** variations affect the gray values in darker areas of the same image,leading to the streak effect [3].This study primarily investigates the streak effect caused by forward conduction current of the PN junction in DCG pixels,and proposes a circuit to suppress this effect.

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Adaptive-basis decomposition-based low-rank network for efficient non-uniform motion deblurring

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Optoelectronics Letters 2025年第1期21卷 43-50页

作者： CHEN Lei XIONG Qingbo ZHANG Wei LI Runde school of software Henan University school of Applied Technology China University of Labor Relations

In this study, we present a unified sparsity-driven framework that significantly enhances motion deblurring performance by integrating two key components: a custom-designed dataset and a low-rank module(LRM). This framework leverages the inherent sparsity of per-pixel blur kernels to bolster both deblurring accuracy and model interpretability. Firstly, we propose an adaptive-basis decomposition-based deblurring(ADD) approach, which constructs a tailored training dataset to enhance the generalization capacity of the deblurring network. The ADD framework adaptively decomposes motion blur into sparse basis elements, effectively addressing the intricacies associated with non-uniform blurs. Secondly, an LRM is proposed to improve the interpretability of deblurring models as a plug-and-play module, primarily designed to identify and harness the intrinsic sparse features in sharp images. A series of ablation studies have been conducted to substantiate the synergistic advantages of combining the proposed ADD with the LRM for overall improvement in deblurring efficacy. Subsequently, we empirically demonstrate through rigorous experimentation that incorporating the LRM into an existing Uformer network leads to substantial enhancement in reconstruction performance. This integration yields a sparsity-guided low-rank network(SGLRN). Operating under the overarching principle of sparsity, SGLRN consistently outperforms state-of-the-art methods across multiple standard deblurring benchmarks. Comprehensive experimental results, assessed through quantitative metrics and qualitative visual evaluations, provide compelling evidence of its effectiveness. The overall deblurring results are available at Google Drive.

关键词： Image enhancement

Voxel event graph neural network for event-based human gait recognition

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Optoelectronics Letters 2025年

作者： MA Guangyuan GONG Xiaolin XU Jiangtao GAO Jiandong GUO Zhaoxuan school of microelectronics， Tianjin University

To improve the accuracy of event-based gait recognition， a method called voxel event graph neural network (VEGNN) is proposed. This method voxelizes the event stream and selects representative voxels as vertices of the graph. Then the edges are constructed based on spatio-temporal distance and temporal order constraints so that the event stream is constructed as a graph structure. Finally， a lightweight feature extraction network based on graph neural networks (GNNs) is used to efficiently capture spatio-temporal information and motion cues from the event graph. To evaluate our method， an event-based gait recognition dataset called Celex-Gait is created. Experimental results show that VEGNN achieves a gait recognition accuracy of 95.8% (best among all methods) and 93.9% (second best among all methods) on the Celex-Gait dataset and the DVS128-Gait-Day dataset， respectively. Furthermore， compared to the state-of-the-art methods， VEGNN reduces the number of model parameters by 30%. This indicates that VEGNN achieves higher recognition accuracy with lower model complexity.

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An AND-type 1T-FeFET array with robust write and read operations

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Science China(Information Sciences) 2025年

作者： Jiacheng XU Jiayi ZHAO Hongrui ZHANG Haoji QIAN Jiani GU Bowen CHEN Gaobo LIN Rongzong SHEN Xinda SONG Huan LIU Yian DING Minglei MA Miaomiao ZHANG Xiao YU Bing CHEN Ran CHENG Gaobo XU Huaxiang YIN Yan LIU Jiajia CHEN Chengji JIN Genquan HAN Hangzhou Institute of Technology and school of microelectronics Xidian University Zhejiang Lab College of Integrated Circuits Zhejiang University Key Laboratory of microelectronics Devices and Integrated Technology Institute of MicroelectronicsChinese Academy of Sciences

HfO2-based ferroelectric field-effect transistor （FeF ET） has become a promising solution for next-generation embedded non-volatile memory （NVM） owing to its complementary metal-oxide-semiconductor （CMOS） compatibility,small footprint, high performance, and non-destructive readout [1]. Recent research of HfO2-based FeF ET has focused on the device operation mechanisms as well as reliability issues such as the interplay between polarization switching and charge trapping/detrapping [2], read-after-write latency [3] and endurance degradation [4]. It has been demonstrated that endurance higher than 1010cycles with negligible read-after-write latency is possible if the gate stack of FeF ET is well engineered [5].

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A Generative Model-Based Network Framework for Ecological Data Reconstruction

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Computers, Materials & Continua 2025年第1期82卷 929-948页

作者： Shuqiao Liu Zhao Zhang Hongyan Zhou Xuebo Chen school of Electronic and Information Engineering University of Science and Technology LiaoningAnshan114051China school of Computer Science and software Engineering University of Science and Technology LiaoningAnshan114051China

This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection *** Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data *** model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT *** model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample *** data is used to retain more semantic information to generate *** model was applied to species in Southern California,USA,citing SWOT analysis data to train the *** show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development *** model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data *** study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.

关键词： Convolutional Neural Network(CNN) VAE GAN TOPSIS data reconstruction

Mutual-structure weighted guided image filtering for depth map restoration

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Optoelectronics Letters 2025年第1期21卷 51-56页

作者： LIU Zijian SONG Jian CHEN Quanmin XU Jiangtao Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology School of Microelectronics Tianjin University Spikesee Technology Co. Ltd.

Although guided image filtering(GIF) is known for preserving edges and fast computation, it may produce inaccurate outputs in depth map restoration. In this paper, a novel confidence-weighted GIF called mutual-structure weighted GIF(MSWGIF) is proposed, which replaces the mean filtering strategy in GIF during handling overlapping windows. The confidence value is composed of a depth term and a mutual-structure term, where the depth term is utilized to protect the edges of the output, and the mutual-structure term helps to select accurate windows during the structure characteristics of the guidance image are transferred to the output. Experimental results show that MSWGIF reduces the root mean square error(RMSE) by an average of 12.37%, and the average growth rate of correlation(CORR) is 0.07% on average. Additionally, the average growth rate of structure similarity index measure(SSIM) is 0.34%.

关键词： Wiener filtering

Dynamic performance enhancement in Ti/Au ohmic contacts on β-Ga2O3-on-Si C substrates: evidence from pulsed measurements

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Science China(Information Sciences) 2025年第1期68卷 400-401页

作者： Chenyu LIU Yibo WANG Wenhui XU Shuqi HUANG Chunxiao YU Zeyu YANG Xiaole JIA Xiaoxi LI Bochang LI Zhengdong LUO Cizhe FANG Yan LIU Tiangui YOU Xin OU Genquan HAN school of microelectronics Xidian University Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of Sciences Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Hangzhou Institute of Technology Xidian University

Titanium/Gold （Ti/Au） metals have been widely employed as ohmic metal contacts on n+Ga2O3,exhibiting the specific contact resistance （ρc） of 10-5-10-6Ω·cm2[1].But previous studies have revealed the presence of a defective Ga2O3layer and reactive Ti-Ga2O3layer at the annealed Ti/*** an interfacial layer can afect the performance of ohmic contact,which may become more pronounced in power devices subjected to electrical stress and high-frequency ***,there have been no reports on the impact of bias stress effects and dynamic degradation of Ti/Au ohmic contacts on n+Ga2O3.

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