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Emerging Internet of Things driven carbon nanotubes-based devices

Nano Research 2022年第5期15卷 4613-4637页

作者： Shu Zhang Jinbo Pang Yufen Li Feng Yang Thomas Gemming Kai Wang Xiao Wang Songang Peng Xiaoyan Liu Bin Chang Hong Liu Weijia Zhou Gianaurelio Cuniberti Mark HRümmeli Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of Shandong Institute for Advanced Interdisciplinary Research(iAIR)University of JinanJinan 250022China Department of Chemistry Guangdong Provincial Key Laboratory of CatalysisSouthern University of Science and TechnologyShenzhen 518055China Leibniz Institute for Solid State and Materials Research Dresden P.O.Box 270116Dresden D-01171Germany State Key Laboratory of Crystal Materials Center of Bio&Micro/Nano Functional MaterialsShandong University27 Shandanan RoadJinan 250100China School of Electrical Engineering Qingdao UniversityQingdao 266071China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences 1068 Xueyuan AvenueShenzhen University TownShenzhen 518055China High-Frequency High-Voltage Device and Integrated Circuits R&D Center Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China Key Laboratory of Microelectronic Devices&Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China School of Chemistry and Chemical Engineering University of JinanJinan 250022China College of Energy Soochow Institute for Energy and Materials InnovationsSoochow UniversitySuzhou 215006China Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province Soochow UniversitySuzhou 215006China Centre of Polymer and Carbon Materials Polish Academy of SciencesM.Curie Sklodowskiej 34Zabrze 41-819Poland Institute of Environmental Technology(CEET) VSB-Technical University of Ostrava17.Listopadu 15Ostrava 70833Czech Republic Dresden Center for Computational Materials Science Technische Universität DresdenDresden 01062Germany Dresden Center for Intelligent Materials(GCL DCIM) Technische Universität DresdenDresden 01062Germany Institute for Materials Science and Max Bergmann Center of Biomaterials Technische Universität DresdenDresden 01069Germany Center for Advancing Electronics D

Carbon nanotubes(CNTs)have attracted great attentions in the field of electronics,sensors,healthcare,and energy *** emerging applications have driven the carbon nanotube research in a rapid ***,the structure control over CNTs has inspired an intensive research vortex due to the high promises in electronic and optical device ***,this in-depth review is anticipated to provide insights into the controllable synthesis and applications of high-quality ***,the general synthesis and post-purification of CNTs are briefly ***,the state-of-the-art electronic device applications are discussed,including field-effect transistors,gas sensors,DNA biosensors,and pressure ***,the optical sensors are delivered based on the *** addition,energy applications of CNTs are discussed such as thermoelectric energy ***,future opportunities are proposed for the Internet of Things(IoT)oriented sensors,data processing,and artificial intelligence.

关键词： carbon nanotubes electronics sensors electronic skins Internet of Things artificial intelligence

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In-memory computing with emerging nonvolatile memory devices

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Science China(Information Sciences) 2021年第12期64卷 23-68页

作者： Caidie CHENG Pek Jun TIW Yimao CAI Xiaoqin YAN Yuchao YANG Ru HUANG State Key Laboratory for Advanced Metals and Materials School of Materials Science and EngineeringUniversity of Science and Technology Beijing Key Laboratory of Microelectronic Devices and Circuits (MOE) Department of Micro/nanoelectronicsPeking University Center for Brain Inspired Chips Institute for Artificial IntelligencePeking University Center for Brain Inspired Intelligence Chinese Institute for Brain Research (CIBR)

The von Neumann bottleneck and memory wall have posed fundamental limitations in latency and energy consumption of modern computers based on von Neumann architecture. In-memory computing represents a radical shift in the computer architecture that can address such problems by merging computing functions within the memory itself. In this article, we review the emerging nonvolatile memory devices,such as resistance-based and charge-based memory devices, that are explored for in-memory computing applications. We will provide an overview of the materials, mechanisms, and integration of these devices,and discuss the optimizations at the device and array levels that are required to better support in-memory computing. Recent progress in the application of in-memory computing in artificial neural networks, spiking neural networks, digital logic in memory as well as hardware security will also be discussed. Finally, we will discuss the remaining challenges in this field and potential pathways to address them.

关键词： in-memory computing von Neumann bottleneck nonvolatile memory energy efficiency neural network

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Thermalization Effect in semiconductor Si, and metallic silicide NiSi2, CoSi2 by using Non-Adiabatic Molecular Dynamics Approach

arXiv

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arXiv 2023年

作者： Luo, Kun Gan, Weizhuo Hou, Zhaozhao Zhan, Guohui Xu, Lijun Liu, Jiangtao Lu, Ye Wu, Zhenhua Key Laboratory of Microelectronics Devices and Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China School of Integrated Circuits University of Chinese Academy of Sciences Beijing100049 China HiSilicon Technologies Shenzhen China College of Mechanical and Electrical Engineering Guizhou Minzu University Guiyang550025 China The School of Information Science and Technology Fudan University Shanghai200433 China

Recently, cold source transistor (CSFET) with steep-slope subthreshold swing (SS) 2 and CoSi2). The dependence of the thermalization factor, relaxation time, scattering time and scattering rate on energy level are obtained. The thermalization of carrier gradually increases from low energy to high energy. Partially thermalization from the ground state to reach the thermionic current window is realized with sub-100 fs time scale. Fully thermalization to entail energy region depends on the barrier height sensitively, i.e., the scattering rate decreases exponentially as the energy of the out-scattering state increase. The scattering rate of NiSi2 and CoSi2 is 2 orders of magnitude higher than that of Si, arising from their higher density of states than that in Silicon This study can shed light on the material design for low power tunneling FET as well as the emerging CSFET. Copyright © 2023, The Authors. All rights reserved.

关键词： Silicides

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Tunable synaptic devices based on ambipolar MoTe2 transistor

Tunable synaptic devices based on ambipolar MoTe2 transistor

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IEEE Electron devices Technology and Manufacturing Conference (EDTM)

作者： Tingting Gao Xuefei Li Linxin Han Yanqing Wu Wuhan National High Magnetic Field Center and School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan China Institute of Microelectronics and Key Laboratory of Microelectronic Devices and Circuits (MOE) Peking University Beijing China

Synapse is one of the main elements of hardware implementation in a neuron network. Complex CMOS technology-based circuits using various Si-based transistors, non-Si based memory devices like RRAM, and 2D FET devices serve as hardware implement for neuron network. In this work, we demonstrate ambipolar MoTe₂ FET devices with a wide range oftunability in synaptic characteristic and classical paired pulsed facilitation as well as spike-timing-dependent plasticity function.

关键词： Neurons Field effect transistors CMOS technology Hardware Biology Manufacturing Synapses

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Resistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model

arXiv

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arXiv 2024年

作者： Yang, Jichang Chen, Hegan Chen, Jia Wang, Songqi Wang, Shaocong Yu, Yifei Chen, Xi Wang, Bo Zhang, Xinyuan Cui, Binbin Li, Yi Lin, Ning Xu, Meng Li, Yi Xu, Xiaoxin Qi, Xiaojuan Wang, Zhongrui Zhang, Xumeng Shang, Dashan Wang, Han Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering the University of Hong Kong Hong Kong ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong Laboratory of Microelectronic Devices and Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China State Key Laboratory of Integrated Chips and Systems Frontier Institute of Chip and System Fudan University Shanghai200433 China Institute of the Mind the University of Hong Kong Hong Kong University of Chinese Academy of Sciences Beijing100049 China School of Integrated Circuits Hubei Key Laboratory for Advanced Memories Huazhong University of Science and Technology Wuhan430074 China Department of Electronic and Computer Engineering the Hong Kong University of Science and Technology Hong Kong

Human brains image complicated scenes when reading a novel. Replicating this imagination is one of the ultimate goals of AI-Generated Content (AIGC). However, current AIGC methods, such as score-based diffusion, are still deficient in terms of rapidity and efficiency. This deficiency is rooted in the difference between the brain and digital computers. Digital computers have physically separated storage and processing units, resulting in frequent data transfers during iterative calculations, incurring large time and energy overheads. This issue is further intensified by the conversion of inherently continuous and analog generation dynamics, which can be formulated by neural differential equations, into discrete and digital operations. Inspired by the brain, we propose a time-continuous and analog in-memory neural differential equation solver for score-based diffusion, employing emerging resistive memory. The integration of storage and computation within resistive memory synapses surmount the von Neumann bottleneck, benefiting the generative speed and energy efficiency. The closed-loop feedback integrator is time-continuous, analog, and compact, physically implementing an infinite-depth neural network. Moreover, the software-hardware co-design is intrinsically robust to analog noise. We experimentally validate our solution with 180 nm resistive memory in-memory computing macros. Demonstrating equivalent generative quality to the software baseline, our system achieved remarkable enhancements in generative speed for both unconditional and conditional generation tasks, by factors of 64.8 and 156.5, respectively. Moreover, it accomplished reductions in energy consumption by factors of 5.2 and 4.1. Our approach heralds a new horizon for hardware solutions in edge computing for generative AI applications. © 2024, CC BY-NC-SA.

关键词： Diffusion

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Breakdown voltage enhancement in GaN channel and AlGaN channel HEMTs using large gate metal height

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Chinese Physics B 2020年第2期29卷 420-424页

作者： Zhong-Xu Wang Lin Du Jun-Wei Liu Ying Wang Yun Jiang Si-Wei Ji Shi-Wei Dong Wei-Wei Chen Xiao-Hong Tan Jin-Long Li Xiao-Jun Li Sheng-Lei Zhao Jin-Cheng Zhang Yue Hao Key Laboratory for Wide Band-Gap Semiconductor Materials and Devices School of MicroelectronicsXidian UniversityXi'an 710071China Shanghai Precision Metrology and Testing Research Institute Shanghai 201109China China Academy of Space Technology(Xi'an) Xi'an 710000China Sichuan Institute of Solid-State Circuits CETCChongqing 400060China

A large gate metal height technique is proposed to enhance breakdown voltage in GaN channel and AlGaN channel high-electron-mobility-transistors(HEMTs).For GaN channel HEMTs with gate-drain spacing LGD=2.5μm,the breakdown voltage VBR increases from 518 V to 582 V by increasing gate metal height h from 0.2μm to 0.4μ*** GaN channel HEMTs with LGD=7μm,VBR increases from 953 V to 1310 V by increasing h from 0.8μm to 1.6μ*** breakdown voltage enhancement results from the increase of the gate sidewall capacitance and depletion region *** Al0.4Ga0.6N channel HEMT with LGD=7μm,VBR increases from 1535 V to 1763 V by increasing h from 0.8μm to 1.6μm,resulting in a high average breakdown electric field of 2.51 MV/*** and analysis indicate that the high gate metal height is an effective method to enhance breakdown voltage in GaN-based HEMTs,and this method can be utilized in all the lateral semiconductor devices.

关键词： GaN channel HEMTs AlGaN channel HEMTs breakdown voltage gate metal height

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A low-Power SRAM with charge cycling based read and write assist scheme

A low-Power SRAM with charge cycling based read and write as...

引用

International Conference on Solid-State and Integrated Circuit Technology

作者： Hanzun Zhang Song Jia Jiancheng Yang Yuan Wang Institute of Microelectronics Peking University Beijing China Key Laboratory of Microelectronic Devices and Circuits Peking University Beijing China

ISBN: (数字)9781728162355

ISBN: (纸本)9781728162362

In a SRAM array, the largest power consumer is pre-charging or voltage switching on bit-lines in read or write operations. The paper presents a bit-line charge cycling based read and write assist circuit for static random-access memory. With help of the assist circuit, the BLs charges wasted in conventional design is reused for BLs pre-charging in next period. The proposed array is simulated in SMIC 14nm FinFET process with a supply voltage of 0.8V. Simulation results show that a 23%-43% power reduction is achieved compared with conventional designs.

关键词： Random access memory Power demand Voltage control Transistors Timing Heating systems Circuit stability

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Improved Multi-bit Storage Reliablity by Design of Ferroelectric Modulated Anti-ferroelectric Memory

Improved Multi-bit Storage Reliablity by Design of Ferroelec...

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International Electron devices Meeting (IEDM)

作者： Yannan Xu Yang Yang Shengjie Zhao Tiancheng Gong Pengfei Jiang Yuan Wang Peng Yuan Zhiwei Dang Yuting Chen Shuxian Lv Yaxin Ding Yan Wang Jinshun Bi Qing Luo Ming Liu Key Laboratory of Microelectronic Devices and Integrated Technology Institute of Microelectronics of Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665425735

For the first time, we present a new concept of ferroelectric modulated anti-ferroelectric memory with independent 2-step state switching and large polarization as a promising option for multi-bit storage in advanced technology nodes. We show that 1) based on the Landau-Ginzburg-Devonshire (LGD) theory, 4 stable states of AFE with built-in field can be obtained. 2) non-volatile 2-bit storage is demonstrated in AFE due to bipolar built-in field introduced by FE polarization switching. 3) The $F$ E+A $F$ E device shows double-peak $\mathrm{E}_{\mathrm{c}}$ distribution and large $2\mathrm{P}_{\mathrm{r}}$ of 33.8 $\mu \mathrm{C}/\text{cm}^{2}$ with good process stability. 4) Benefit from independently step-by-step switching and large density of effective FE domain, the improved multi-bit storage reliability is verified by kinetic Monte Carlo modeling, compared to conventional multi-bit technology.

关键词： Nonvolatile memory Shape Statistical distributions Superlattices Switches Control systems Reliability engineering

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Quantum transport quality of a processed undoped Ge/SiGe heterostructure

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Physical Review B 2023年第4期108卷 045303-045303页

作者： Yi-Xin Li Zhenzhen Kong Shimin Hou Guilei Wang Shaoyun Huang Beijing Key Laboratory of Quantum Devices Key Laboratory for the Physics and Chemistry of Nanodevices Peking University Beijing 100871 People's Republic of China Integrated Circuit Advanced Process R&D Center Institute of Microelectronics Chinese Academy of Sciences Beijing 100029 People's Republic of China School of Integrated Circuits University of Chinese Academy of Sciences Beijing 100049 People's Republic of China Hefei National Laboratory Hefei 230088 People's Republic of China and Beijing Superstring Academy of Memory Technology Beijing 100176 People's Republic of China

A degraded mobility of 5.2×105cm2V−1s−1 but a long quantum scattering time of 2.3 ps at the hole density of 2.25×1011cm−2 were obtained from a two-dimensional hole gas in a processed undoped Ge/SiGe heterostructure Hall-bar field-effect device. The heterostructure was grown by the reduced pressure chemical vapor deposition method and the device fabrications were compatible with well-established semiconductor technology. The percolation density of 0.69×1011cm−2 indicated a very low disorder potential landscape experienced by holes in the strained Ge quantum well. In addition to integer quantum Hall effects (IQHEs) of consecutive filling factors from ν=8 down to 1, we also observed the quantum states at filling factors between ν=1 and 2, and between ν=2 and 3 at 1.71 K. The observation of quantum Hall effects at the inferior mobility confirmed the mobility/quality dichotomy in the ultraclean undoped Ge/SiGe heterostructure. The study explicitly indicated that the device fabrication process likely compromised the transport mobility, whereas the quantum quality was less influenced, and established the heterostructure as an ideal platform for quantum device implementations.

关键词： Electrical properties Fractional quantum Hall effect Quantum Hall effect Quantum wells Two-dimensional electron system Epitaxy Hall bar Methods in transport Shubnikov-de Haas effect

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Adaptive Random Number Generator Based on RRAM Intrinsic Fluctuation for Reinforcement Learning

Adaptive Random Number Generator Based on RRAM Intrinsic Flu...

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International Symposium on VLSI Technology, Systems and Applications

作者： Lin Bao Zongwei Wang Zhizhen Yu Yichen Fang Yuchao Yang Yimao Cai Ru Huang Institute of Microelectronics Peking University Beijing China Key Laboratory of Microelectronic Devices and Circuits (MOE) Peking University Beijing China

ISBN: (数字)9781728142326

ISBN: (纸本)9781728142333

In this work, a novel random number generator with adjustable adaptability is demonstrated based on the intrinsic fluctuation of resistive random access memory (RRAM). Experimental data shows the standard deviation of resistance state has a negative dependence on compliance current (CC), thus an adaptive random number generator (ARNG) can be realized by controlling the CC in RRAM set process. Benchmarking results show that the RRAM based ARNG can achieve same performance compared with software ARNG in reinforcement learning process of zebra finch's singing pattern.

关键词： Resistance Learning (artificial intelligence) Fluctuations Generators Standards Software Random access memory

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