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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

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Implementation of Graph Convolution Network Based on Analog Rram

Implementation of Graph Convolution Network Based on Analog ...

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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

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Impact of Circuit Limit and Device Noise on RRAM Based Conditional Generative Adversarial Network

Impact of Circuit Limit and Device Noise on RRAM Based Condi...

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China Semiconductor Technology International Conference (CSTIC)

作者： Shengyu Bao Zongwei Wang Tianyi Liu Daqin Chen 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, a Conditional Generative Adversarial Network (CGAN) [1] is demonstrated based on the Resistive Random Access Memory (RRAM). During training, the read noise of RRAM is utilized as a random bias source to enrich the diversity of the generator in CGAN. Further, we evaluate the impact of both read noise (RRAM as weight storage cell) and the resolution of the AD/DA circuit on the performance of CGAN through a comprehensive simulation.

关键词： Training Performance evaluation Random access memory Semisupervised learning Generative adversarial networks Generators Integrated circuit modeling

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Low Voltage Dual-Modulus Frequency Divider Based on Extended True Single-Phase Clock Logic

Low Voltage Dual-Modulus Frequency Divider Based on Extended...

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2018 IEEE International Conference on Electron devices and Solid State circuits, EDSSC 2018

作者： Wang, Wanlu Jia, Song Wang, Ziyi Pan, Tao Wang, Yuan Key Laboratory of Microelectronic Devices and Circuits Institute of Microelectronics Peking University Beijing100871 China

ISBN: (纸本)9781538662342

Two low voltage dual-modulus frequency divider based on extended true single-phase clock (E-TSPC) logic are proposed. By reducing the number of serial transistors from VDD to GND, the proposed designs can effectively work at low voltage. Simulation results in SMIC 40nm technology show that the presented design I has better power and speed performance with lower supply voltage. Compared to the referenced designs, the presented design II can work at the lowest supply voltage with little loss of performance. © 2018 IEEE.

关键词： Computer circuits

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Design of low-power high-speed dual-modulus frequency divider with improved MOS current mode logic

Design of low-power high-speed dual-modulus frequency divide...

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2018 IEEE International Conference on Electron devices and Solid State circuits, EDSSC 2018

作者： Wang, Wanlu Jia, Song Pan, Tao Wang, Yuan Key Laboratory of Microelectronic Devices and Circuits Institute of Microelectronics Peking University Beijing100871 China

ISBN: (纸本)9781538662342

One design technique that aims to reduce power consumption of MOS current mode logic dual-modulus frequency divider is presented in this paper. With combinational logic transferred the proposed scheme can obtain high working frequency. By merging the first master latches of CML DFFs, the simplified structure can achieve lower power consumption. Circuit simulation of the proposed structure in 28nm CMOS technology shows a 21% reduction of power consumption over conventional scheme is achieved at frequency of 56GHz. © 2018 IEEE.

关键词： Electric power utilization

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Deep Spiking Convolutional Neural Networks for Programmable Neuro-synaptic System

Deep Spiking Convolutional Neural Networks for Programmable ...

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International Conference on ASIC

作者： Chenglong Zou Xinan Wang Boxing Xu Yisong Kuang Xiaoxin Cui Key Laboratory of Microelectronics Devices and Circuits Institute of Microelectronics Peking University Beijing China Key Laboratory of Integrated Microsystem School of ECE Peking University Shenzhen Graduate School Shenzhen China

Binary neural networks (BNNs) - neural networks with binary {-1, +1} weights and activations at run-time, have attracted much attention in recent years, which drastically reduce resource requirement and power consumption with tolerable accuracy loss. While, spiking neural networks (SNNs) are designed as a brain-inspired computing model, which can be deployed on large-scale distributed and event-based neuromorphic hardware efficiently. To enable a conversion of above two, we develop a variant of BNNs which employ binary-valued {0, +1} activations and ternary-valued {-1, 0, +1} weights and achieve start-of-the-art classification accuracy across two standard vision datasets, even better than the previous proposals using BNNs or SNNs. Furtherly, we propose an effective configuration method for network deployment on our designed programmable neuro-synaptic system with only one timestep and milliwatt level power consumption.

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A Novel High-speed FPGA-based True Random Number Generator Based on Chaotic Ring Oscillator

A Novel High-speed FPGA-based True Random Number Generator B...

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International Conference on ASIC

作者： Xinning Liu Song Jia Hanzun Zhang Institute of Microelectronics Peking University Beijing China Key Laboratory of Microelectronic Devices and Circuits Peking University Beijing China

This paper presents a novel true random number generator with entropy source based on swappable matrix feedback ring oscillator (SMFRO). The proposed SMFRO structure utilizes multiple feedback mechanism to produce reliable chaotic oscillation. Three SMFROs implemented on FPGA with different resource configurations are tested. All of them can pass chaotic oscillation and NIST tests. Proposed design can generate random bits at 500MHz without extra post-processing.

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Technology Roadmap for Flexible Sensors

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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 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 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

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

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12-bit 2.6 GS/s RF DAC based on return-to-zero technology

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The Journal of China Universities of Posts and Telecommunications 2019年第4期26卷 36-42页

作者： Li Xiaopeng Wang Zhigong Zhang Yi Zhang Youtao Zhang Mi Institute of RF and OE ICs Southeast UniversityNanjing 210096China Nanjing Guobo Electronics Company Limited Nanjing 210016China Nanjing Electronic Devices Institute Nanjing 210016China National and Local Joint Engineering Laboratory of RF Integration and Micro-assembly Technology Nanjing 210046China College of Microelectronics Nanjing University of Posts and TelecommunicationsNanjing 210046China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China Science and Technology on Monolithic Intergrated Circuits and Modules Laboratory Nanjing 210016China

A 12-bit 2.6 GS/s radio frequency digital to analog converter(RF DAC)based on 1 um GaAs heterojunction bipolar transistor(HBT)process is *** DAC integrates a 4:1 multiplexer to reduce the data rate of input ports,which greatly facilitates the *** core adopts 4+8 segmented current steering structure.R-2R ladder network is used for 8 least significant bit(LSB)to realize binary current weighting and thermometer coding is used for 4 most significant bit(MSB).Return-to-zero(RZ)technology is used to expand the effective bandwidth of DAC output to the third Nyquist *** proposed DAC has a better output power flatness and spurious-free dynamic range(SFDR).Compared to traditional DAC,measured results demonstrate that the output power of this RZ DAC is increased by 33 dB and the SFDR is enhanced by 27 dB near the second Nyquist band.

关键词： DAC RZ Nyquist band

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Complementary Photo-Synapses: Complementary Photo-Synapses Based on Light-Stimulated Porphyrin-Coated Silicon Nanowires Field-Effect Transistors (LPSNFET) (Small 30/2021)

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Small 2021年第30期17卷

作者： Xiaokang Li Bocheng Yu Bowen Wang Ran Bi Haixia Li Kun Tu Gong Chen Zhihong Li Ru Huang Ming Li Key Laboratory of Microelectronic Devices and Circuits (MOE) Institute of Microelectronics Peking University Beijing 100871 China National Key Laboratory of Science and Technology on Micro/Nano Fabrication Institute of Microelectronics Peking University Beijing 100871 China Frontiers Science Center for Nano-optoelectronics Peking University Beijing 100871 China

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