检索结果-内蒙古大学图书馆

Technology Roadmap for Flexible Sensors

ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A*STAR) 2 Fusionopolis Way #08-03 Innovis Singapore 138634 Republic of Singapore；Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States；Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden；Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

来源：评论

学校读者我要写书评

暂无评论

In-memory search with learning to hash based on resistive memory for recommendation acceleration

npj Unconventional Computing

引用

npj Unconventional Computing 2024年第1期1卷 1-9页

作者： Fei Wang Woyu Zhang Zhi Li Rui Bao Xiaoxin Xu Chunmeng Dou Dashan Shang Ning Lin Zhongrui Wang Key Lab of Fabrication Technologies for Integrated Circuits Institute of Microelectronics Chinese Academy of Sciences Beijing 100029 China Key Laboratory of Microelectronic Devices and Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing 100029 China University of Chinese Academy of Sciences Beijing 100049 China Department of Electrical and Engineering the University of Hong Kong Hong Kong China ACCESS - AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong China School of Microelectronics Southern University of Science and Technology Shenzhen 518055 China

Similarity search is essential in current artificial intelligence applications and widely utilized in various fields, such as recommender systems. However, the exponential growth of data poses significant challenges in search time and energy consumption on traditional digital hardware. Here, we propose a software-hardware co-optimization to address these challenges. On the software side, we employ a learning-to-hash method for vector encoding and achieve an approximate nearest neighbor search by calculating Hamming distance, thereby reducing computational complexity. On the hardware side, we leverage the resistance random-access memory crossbar array to implement the hash encoding process and the content-addressable memory with an in-memory computing paradigm to lower the energy consumption during searches. Simulations on the MovieLens dataset demonstrate that the implementation achieves comparable accuracy to software and reduces energy consumption by 30-fold compared to traditional digital systems. These results provide insight into the development of energy-efficient in-memory search systems for edge computing.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Automatic Slope-Calibrated Ramp Generator for Single-Slope ADCs

An Automatic Slope-Calibrated Ramp Generator for Single-Slop...

引用

International Conference on ASIC

作者： Shoudong Huang Wengao Lu Ye Zhou Shanzhe Yu Yacong Zhang Xueyou Shi Zhongjian Chen Key Laboratory of Microelectronic Devices and Circuits Institute of Microelectronics Peking University Beijing China

This paper presents a ramp generator with automatic slope calibration which is able to compensate slope deviation due to process and environmental effects. A novel analog feedback circuit is proposed to regulate the stop voltage in successive approximation while fix the start voltage of the ramp automatically without much calibration code input or complicated auxiliary circuits, which reduces the complexity of circuitry and timing, meanwhile, saves area and power. A 12-bit single-slope ADC (SS-ADC), employing the proposed ramp generator, has been implemented in a 0.11-μm CMOS process. The simulation results show that the offset of the ramp's stop voltage is reduced to 0.1 LSB and the nonlinearity of the ramp is 0.034%. Besides, the measurement results verify that this structure can fulfill automatic slope calibration.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Novel Self-Aligned Dopant-Segregated Schottky Tunnel-FET with Asymmetry Sidewall Based on Standard CMOS Technology

A Novel Self-Aligned Dopant-Segregated Schottky Tunnel-FET w...

引用

International Conference on Solid-State and Integrated Circuit Technology

作者： Yiqing Li Qianqian Huang Mengxuan Yang Ting Li Zhixuan Wang Weihai Bu Jin Kang Wenbo Wang Shengdong Zhang Ru Huang School of Electronic and Computer Engineering Peking University Shenzhen China Key Laboratory of Microelectronic Devices and Circuits (MOE) Institute of Microelectronics Peking University Beijing China Semiconductor Technology Innovation Center (Beijing) Beijing China

ISBN: (数字)9781728162355

ISBN: (纸本)9781728162362

A novel Si-based tunnel field effect transistor (TFET) with self-aligned dopant-segregated Schottky (DSS) source and underlap-drain is proposed and manufactured based on standard CMOS process. Asymmetric sidewall structures are introduced and manufactured for the process of self-aligned DSS source/drain. In the proposed device, DSS structure is able to optimize the source junction of devices without introducing ambipolar effect. The fabricated device improves the on-current by about two orders of magnitude compared with the conventional TFET without DSS structure, and suppress the ambipolar current over two orders of magnitude compared with the DSS-TFET without asymmetric sidewall structures. The experimental results demonstrate the great potential of the device for ultra-low power IOT applications.

关键词： TFETs Silicides Standards Internet of Things Logic gates CMOS process Junctions

来源：评论

学校读者我要写书评

暂无评论

Erratum to: CMN: a co-designed neural architecture search for efficient computing-in-memory-based mixture-of-experts

引用

Science China Information Sciences 2024年第11期67卷 1-1页

作者： Han, Shihao Liu, Sishuo Du, Shucheng Li, Mingzi Ye, Zijian Xu, Xiaoxin Li, Yi Wang, Zhongrui Shang, Dashan Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong China ACCESS-AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong China Key Lab of Fabrication Technologies for Integrated Circuits Chinese Academy of Sciences Beijing China Laboratory of Microelectronics Devices and Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Microelectronics Southern University of Science and Technology Shenzhen China

来源：评论

学校读者我要写书评

暂无评论

A 12-GHz All-Digital Calibration-Free FMCW Signal Generator Based on a Retiming Fractional Frequency Divider

A 12-GHz All-Digital Calibration-Free FMCW Signal Generator ...

引用

IEEE Asian Conference on Solid-State circuits (ASSCC)

作者： Zhengkun Shen Heyi Li Haoyun Jiang Zherui Zhang Junhua Liu Huailin Liao Key Laboratory of Microelectronic Devices and Circuits (MOE) Institute of Microelectronics Peking University Beijing China

ISBN: (数字)9781728151069

ISBN: (纸本)9781728151076

A 12-GHz all-digital calibration-free frequency-modulated continuous-wave (FMCW) signal generator is presented in this paper based on a retiming fractional frequency divider (FFD). Instead of modulating a multi-modulus divider (MMD) by a ΔΣ modulator, a fractional divider is utilized to release the narrow loop bandwidth limitation and achieve better phase noise without active noise cancellation techniques. Thus, the FMCW signal generator can achieve low root-mean-square (RMS) frequency error under a fast chirp slop. A calibration-free retiming FFD architecture which is not sensitive to process, voltage and temperature (PVT) variations is proposed to avoid complex and slow calibration process. Implemented in 40-nm CMOS, the generator consumes 33.8 mW power and 0.32 mm 2 chip area. Measurement results show that the generator achieves 78 MHz/μs maximum chirp slope and 6.0 kHz RMS frequency error when 300 MHz frequency is swept in 2 ms. The phase noise from 12 GHz carrier is -113.6 dBc/Hz at 1 MHz offset.

关键词： Chirp Generators Frequency conversion Phase noise Bandwidth Linearity Frequency modulation

来源：评论

学校读者我要写书评

暂无评论

A novel strategy for GaN-on-diamond device with a high thermal boundary conductance

arXiv

引用

arXiv 2021年

作者： Mu, Fengwen Xu, Bin Wang, Xinhua Gao, Runhua Huang, Sen Wei, Ke Takeuchi, Kai Chen, Xiaojuan Yin, Haibo Wang, Dahai Yu, Jiahan Suga, Tadatomo Shiomi, Junichiro Liu, Xinyu High-Frequency High-Voltage Device and Integrated Circuits RandD Center Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China Department of Mechanical Engineering The University of Tokyo Tokyo Bunkyo113-8656 Japan University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Microelectronic Devices and Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China Collaborative Research Center Meisei University Tokyo Hino-shi191-8506 Japan Integrated Circuit Advanced Process RandD Center Institute of Microelectronics of Chinese Academy of Sciences Beijing100029 China

To achieve high device performance and high reliability for the gallium nitride (GaN)-based high electron mobility transistors (HEMTs), efficient heat dissipation is important but remains challenging. Enormous efforts have been made to transfer a GaN device layer onto a diamond substrate with a high thermal conductivity by bonding. In this work, two GaN-diamond bonded composites are prepared via modified surface activated bonding (SAB) at room temperature with silicon interlayers of different thicknesses (15 nm and 22 nm). Before and after post annealing process at 800 oC, thermal boundary conductance (TBC) across the bonded interface including the interlayer and the stress of GaN layer are investigated by time-domain thermoreflectance and Raman spectroscopy, respectively. After bonding, the 15 nm Si interlayer achieved a higher TBC. The post-annealing significantly increased the TBC of both interfaces, while the TBC of 22 nm silicon interlayer increased greater and became higher than that of 15 nm. Detailed investigation of the microstructure and composition of the interfaces were carried out to understand the difference in interfacial thermal conduction. The obtained stress was no more than 230 MPa for both before and after the annealing, and this high thermal stability of the bonded composites indicates that the room temperature bonding can realize a GaN-on-diamond template suitable for further epitaxial growth or device process. This work brings a novel strategy of SAB followed by high-temperature annealing to fabricate a GaN-on-diamond device with a high TBC. © 2021, CC BY.

关键词： Gallium nitride

来源：评论

学校读者我要写书评

暂无评论

Implementation of Graph Convolution Network Based on Analog Rram

Implementation of Graph Convolution Network Based on Analog ...

引用

China Semiconductor Technology International Conference (CSTIC)

作者： Daqin Chen Zongwei Wang Shengyu Bao Yimao Cai Ru Huang Institute of Microelectronics Peking University Beijing China Key Laboratory of Microelectronic Devices and Circuits Peking University Beijing China Frontiers science center for nano-optoelectronics Peking University Beijing P. R. China

ISBN: (数字)9781728165585

ISBN: (纸本)9781728165592

In this work, the implementation of Graph Convolutional Network (GCN) based on resistive switching memory is demonstrated through simulation. After training, the RRAM-based GCN can process a semi-supervised graph classification task. Further, the impacts of read noises and circuit bit-precision on the performance of GCN are analyzed. Results show the proposed GCN can reach high accuracy when bit-precisions; 4-bit. Moreover, read noise can severely affect accuracy.

关键词： Training Knowledge engineering Convolution Switches Task analysis Integrated circuit modeling Analog-digital conversion

来源：评论

学校读者我要写书评

暂无评论

An Energy-Efficient Mixed-Signal Parallel Multiply-Accumulate (MAC) Engine Based on Stochastic Computing

An Energy-Efficient Mixed-Signal Parallel Multiply-Accumulat...

引用

International Conference on ASIC

作者： Xinyue Zhang Jiahao Song Yuan Wang Yawen Zhang Zuodong Zhang Runsheng Wang Ru Huang Institute of Microelectronics and Key Laboratory of Microelectronics Devices and Circuits (MoE) Peking University Beijing P.R. China

Convolutional neural networks (CNN) have achieved excellent performance on various tasks, but deploying CNN to edge is constrained by the high energy consumption of convolution operation. Stochastic computing (SC) is an attractive paradigm which performs arithmetic operations with simple logic gates and low hardware cost. This paper presents an energy-efficient mixed-signal multiply-accumulate (MAC) engine based on SC. A parallel architecture is adopted in this work to solve the latency problem of SC. The simulation results show that the overall energy consumption of our design is 5.03pJ per 26-input MAC operation under 28nm CMOS technology.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Energy-Efficient Computing-in-Memory Neuromorphic System with On-Chip Training

An Energy-Efficient Computing-in-Memory Neuromorphic System ...

引用

IEEE Biomedical circuits and Systems (BIOCAS)

作者： Zhao Zhao Yuan Wang Xinyue Zhang Xiaoxin Cui Ru Huang Institute of Microelectronics and Key Laboratory of Microelectronics Devices and Circuits (MoE) Peking University Beijing P.R. China

The aim of neuromorphic computing system is to implement the computational power and efficiency of the human brain. Computing-in-memory (CIM) is a promising and energy-efficient way to perform intensive computations, whose structure is similar to human brain synapse. A 8.78TOPS/W biologically-inspired neuromorphic computing system for pattern recognition based on CIM architecture is presented in this work. The proposed system supports on-chip training with energy-efficient bio-plausible spike-timing-dependent plasticity (STDP) rule and performs multiply-and-accumulate (MAC) computations inside SRAM array during inference, which greatly reduces the energy consumption. Simulated in 65-nm technology, the proposed system achieves good performance and energy efficiency for pattern recognition. The total energy consumption of training and classifying per image of the proposed system is 0.20 nJ. And the proposed spiking neural network (SNN) just consumes 0.074mW at 1.0V with the throughput of 2.5M images/s in inference phase.

关键词： Neurons Training Synapses Random access memory Testing Neuromorphics Static VAr compensators

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：