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2019 IEEE International Conference on Power, Intelligent Computing and systems, ICPICS 2019

作者： Liu, Zhong Wang, Xin'an Le, Yang Sun, Jone School of Electronics Engineering and Computer Science Peking University China Key Laboratory of Integrated Micro-System Science and Engineering Applications Peking University Shenzhen Graduate School Shenzhen China IFutureLab Inc. United States

ISBN: (纸本)9781728137209

Sleep is an important part of life, and sleep position recognition is an important indicator which reflects sleep quality, warns diseases and prevents pressure sores. Actually, sleep quality is usually related to the body pressure, which affects muscle comfort and blood circulation during sleep. However, in order to regulate the body pressure, one vital step is to recognize the sleep position. Body pressure is a common indicator of the recognition of sleep position. This investigation put forward a body pressure recognition method combining with kernel principal component analysis (KPCA) and support vector machine. The feature extraction based on KPCA can well solve the nonlinear separable problems as well as dimension reduction processing of PCA, and reduce the system's complexity. Compared with the image recognition method and computer vision recognition method, it realized a higher recognition accuracy at a lower cost. Ultimately, the recognition accuracy of six common sleep positions reached 96.5%. © 2019 IEEE.

关键词： Support vector machines

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A Method to Recognize Sleeping Position Using an CNN Model Based on Human Body Pressure Image

A Method to Recognize Sleeping Position Using an CNN Model B...

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2019 IEEE International Conference on Power, Intelligent Computing and systems, ICPICS 2019

作者： Liu, Zhong Wang, Xin'an Su, Mingliang Lu, Ken School of Electronics Engineering and Computer Science Peking University China Key Laboratory of Integrated Micro-System Science and Engineering Applications Peking University Shenzhen Graduate School Shenzhen China IFutureLab Inc. United States

ISBN: (纸本)9781728137209

Sleep is an important part of human life. Recognition of sleeping position is an important part of judging the quality of sleep, warning disease and prevention of pressure sores. At present, the body pressure is being used as a commonly method to recognize sleep gesture, and most of it is based on methods of extracting features, mainly based on local signs and global features. This paper proposes a deep learning method based on CNN deep learning method to deal with recognition of body pressure image. Compared with the previous methods, the method based on CNN neural network achieves higher accuracy of recognition at a lower cost and is suitable for complex application environments, which has good practical value. © 2019 IEEE.

关键词： Sleep research

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Failure Analysis on Diode-triggered Silicon-Controlled Rectifiers By using Nondestructive X-ray microscopy

Failure Analysis on Diode-triggered Silicon-Controlled Recti...

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International Symposium on Physical & Failure Analysis of integrated Circuits

作者： Xinqian Chen Mengge Jin Feihou Fang Liang Zijian Zhang Yanan Wang Dongming Liu Le Chen Chaolun Wang Zhiwei Liu Xing Wu Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai China School of Electronic Information and Electrical Engineering Chengdu University Chengdu China Center for Advanced Semiconductor & Integrated Micro-System University of Electronic Science and Technology of China

As the complexity of integrated circuits increases, the electrostatic discharge (ESD) protection devices become critical to reliability issues. However, the physical failure analysis of ESD devices is destructive and time-consuming. In this work, by using the X-ray microscopy (XRM), we study the abnormal change in the leakage current of diode-triggered silicon-controlled rectifiers (DTSCRs) ESD structure under transmission line pulsing stressing. XRM is nondestructive physical failure analysis method by showing the in-depth morphology information of the devices. The results show that the proposed ESD devices have two parts, one is the trigger diodes part and the other is the SCR part. The SCR part was severely damaged and the contact and vias are melted under the electrical stressing, while the trigger diodes part remains intact. The failure analysis is nondestructive, multi-view, and time-saving. It is enlightening the design of novel ESD devices.

关键词： integrated circuits Power transmission lines microscopy Contacts Failure analysis Rectifiers Morphology

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Metal Migration Induced Breakdown from Gate Contact in Bulk FinFET Devices

Metal Migration Induced Breakdown from Gate Contact in Bulk ...

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International Symposium on Physical & Failure Analysis of integrated Circuits

作者： Xin Yang Yihong Qing Kuei-Shu Chang-Liao Yuchong Qiao Chaolun Wang Zhiwei Liu Luoyong Li Chihang Tsai Yongren Wu Yazhen Xie Weisong Yu Xing Wu Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Center for Advanced Semiconductor & Integrated Micro-System University of Electronic Science and Technology of China Chengdu China Engineering & System Science National Tsing Hua University Taiwan China China Chinaisti (shanghai) Testing Technology Co. Ltd Shanghai China

Bulk fin field-effect transistor (FinFET) devices with excellent gate control ability are promising to succeed the planar devices under Moore’s law. With the scaling down of the feature size, the reliability of the FinFET device becomes an urgent issue. Also, the miniaturized three-dimensional structure of bulk FinFET makes the failure characterization more challenging. In this paper, we carried out the direct current (DC) breakdown and transmission line pulse (TLP) breakdown in the bulk FinFET. Corresponding microstructural and chemical elements’ differences between gate contact and gate electrode are characterized by transmission electron microscopy (TEM). For both experiments, the fin nearest to gate contact is completely damaged under electrical stressing. The destruction of DC is more serious, which has a large area fusion with metal migration. The TLP damage is local and tends to form a void. The gate contact structure is intact with slight elements’ migration. This work paves a guideline for the reliability improvements of FinFET.

关键词： integrated circuits Electric breakdown Transmission electron microscopy Contacts Metals Logic gates FinFETs

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Bionics-inspired highly robust and fatigue-resistant hydrogel composite elastomers with multi-applications

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Chemical engineering Journal 2025年 516卷

作者： Shen, Jiaxin Huang, Haizhou Li, Chen Shi, Huihui Hou, Shisheng Yin, Kuibo Bi, Hengchang Wan, Shu Sun, Litao SEU-FEI Nano-Pico Center Key Laboratory of MEMS of Ministry of Education Collaborative Innovation Center for Micro/Nano Fabrication Device and System Southeast University Nanjing210096 China Key Laboratory of Opto-Electronic Science and Technology for Medicine of Ministry of Education Fujian Provincial Key Laboratory of Photonics Technology College of Photonic and Electronic Engineering Fujian Normal University Fuzhou350117 China Chongqing Key Laboratory of Precision Optics Chongqing Institute of East China Normal University Chongqing401120 China In Situ Devices Center School of Integrated Circuits East China Normal University Dongchuan Road Shanghai200241 China Key Laboratory of Optoelectronic Technology and Systems Ministry of Education Key Disciplines Laboratory of Novel Micro-Nano Devices and System Technology School of Optoelectronics Engineering Chongqing University Chongqing400044 China

The key factors limiting the potential applications of hydrogels are their weak mechanical properties, specifically fatigue and hysteresis, which arise from the breakage of low-energy amorphous polymer chains within the hydrogels. To address this issue, this study presents a universal approach for fabricating composite hydrogels that exhibit remarkable elasticity and mechanical stability. The Ecoflex elastomer backbone was integrated into the organic hydrogel to mimic the cytoskeletal structure. This innovation serves to bolster the material's fatigue resistance and eliminate hysteresis. The findings reveal that the organic hydrogel/porous Ecoflex (OHPE) composite displays impressive mechanical stability. Remarkably, it can endure up to 5,000 load-unload cycles, even at a 250 % strain, with minimal alteration in its mechanical properties. Furthermore, OHPE exhibits excellent resistance to dewatering and freezing, making it a robust material for various applications. Additionally, OHPE performs admirably as strain sensors, temperature sensors, and triboelectric nanogenerators, highlighting its versatility and potential for widespread use. © 2025 Elsevier B.V.

关键词： Hydroelasticity

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Unlocking phonon dynamics at interfaces: Angle-resolved transmission, mode conversion, and thermal conductance engineering

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International Journal of Heat and Mass Transfer 2025年 250卷

作者： Quanjie Wang Cheng Shao Yucheng Xiong Shouhang Li Renzong Wang Jianshi Sun Yifan Liu Xiangjun Liu School of Mechanical Engineering Shandong Key Laboratory of CNC Machine Tool Functional Components Qilu University of Technology (Shandong Academy of Sciences) Jinan PR China Institute of Micro/Nano Electromechanical System and Integrated Circuit College of Mechanical Engineering State Key Laboratory for Modification of Chemical Fibers and Polymer Materials Donghua University Shanghai PR China Thermal Science Research Center Shandong Institute of Advanced Technology Jinan PR China Institute of Micro/Nano Electromechanical System and Integrated Circuit College of Mechanical Engineering Donghua University Shanghai PR China Centre de Nanosciences et de Nanotechnologies CNRS Université Paris-Saclay 91120 Palaiseau France

Thermal transport across material interfaces is a critical factor in the thermal management and overall performance of integrated circuits (ICs). We investigate the angle- and mode-dependent phonon transmission across GaN/AlN interfaces, examining both smooth and rough morphologies. For smooth interface, phonon transmission is strongly influenced by the incident angle, with a critical angle (∼44.4°) limiting transverse mode transmission. Notably, longitudinal acoustic (LA) phonons exhibit anomalous behavior at high incident angles due to mode conversion, forming hybrid modes via coupling with optical phonons. At rough interface, diffuse scattering significantly alters transmission characteristics, enabling additional transport channels and enhancing transverse mode transmission. Frequency-dependent effects reveal a transition to complete diffuse scattering, with energy redistribution across acoustic and optical modes, highlighting acoustic-to-optical conversion. Our decomposition of transmission coefficients quantifies mode-specific contributions, revealing that mode conversion enhances the interfacial thermal conductance (ITC) by ∼10 %. Additionally, we quantify the interface specularity parameter, identifying polarization- and wavelength-dependent deviations from the Ziman model. These findings provide insight into optimizing ITC through interface engineering and phonon dynamics.

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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A 350 C piezoresistive n-type 4H-SiC pressure sensor for hydraulic and pneumatic pressure tests

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Journal of micromechanics and microengineering 2020年第5期30卷

作者： Fang, Xudong Wu, Chen Zhao, Yulong Jiang, Zhuangde Rong, Weibing Feng, Zhihong Lv, Yuanjie State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies Collaborative Innovation Center of Suzhou Nano Science and Technology Xi'an Jiaotong University Xi'an710049 China School of Mechanical Engineering Xi'an Jiaotong University Xi'an710049 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150080 China National Key Laboratory of Application Specific Integrated Circuit Hebei Semiconductor Research Institute Shijiazhuang050051 China

It has been a challenge to develop pressure sensors that can work in harsh environments. In this work, a piezoresistive n-type 4H-SiC pressure sensor is demonstrated, capable of working at 350 C under hydraulic and pneumatic pressure. The pressure measuring range of this sensor is 7 MPa and the chip dimension is 2.5 mm 2.5 mm 0.23 mm. A reliable Ti/TaSi2/Pt metal ohmic contact is used in the sensor and experimental results show that the metal combination can withstand a wide temperature range of 30 C-350 C. In order to avoid thermal stress induced by the mismatch of thermal expansion coefficients of different packaging materials when increasing temperature, a double-layer bonding scheme of cement glue and epoxy is proposed. The inner layer of the chip and the kovar alloy base are directly bonded with the epoxy, while the outer layer is wrapped with the high-temperature cement glue. Experiments confirm that this method can achieve reliable packaging with temperatures up to 350 C for 5 h. Based on the above key technologies, the sensitivity of this sensor can reach 13.2 mV V-1 kPa-1 and the test data exhibit high linearity and repeatability. Static tests prove that the sensor has good media compatibility which can operate under a hydraulic pressure load at 220 C and a barometric pressure load under 350 C. The sensor in this work can provide a reference for pressure measurements at high temperature and in complex media environments. © 2020 IOP Publishing Ltd.

关键词： Pressure sensors

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A Method to Recognize Sleeping Position Using an CNN Model Based on Human Body Pressure Image

A Method to Recognize Sleeping Position Using an CNN Model B...

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IEEE International Conference on Power, Intelligent Computing and systems (ICPICS)

作者： Zhong Liu Xin'an Wang Mingliang SU Ken Lu School of Electronics Engineering and Computer Science Peking University China Key Laboratory of Integrated Micro-System Science and Engineering Applications Peking University Shenzhen Graduate School Shenzhen China iFutureLab Inc. USA

关键词： Sleep apnea Feature extraction Convolution Kernel Image recognition Biological neural networks Sensors

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A Study on Sleep Position Recognition of Body Pressure Image based on KPCA and SVM

A Study on Sleep Position Recognition of Body Pressure Image...

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IEEE International Conference on Power, Intelligent Computing and systems (ICPICS)

作者： Zhong Liu Xin'an Wang Yang Le Jone Sun School of Electronics Engineering and Computer Science Peking University China Key Laboratory of Integrated Micro-System Science and Engineering Applications Peking University Shenzhen Graduate School Shenzhen China iFutureLab Inc. USA

ISBN: (纸本)9781728137216

关键词： Sleep apnea Support vector machines Image recognition Mathematical model Feature extraction Training Principal component analysis

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