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IEEE International Symposium on circuits and Systems (ISCAS)

作者： Shanzhe Yu Xueyou Shi Yacong Zhang Guangyi Chen Siyuan Ye Wengao Lu Zhongjian Chen Key Laboratory of Microelectronic Devices and Circuits Department of Microelectronics Peking University China Peking University Information Technology Institute (Tianjin Binhai) China

This paper presents a low-power readout circuit with 14-bit column-level extended-counting ADC for 17μm-pitch 1024 × 768 silicon diode uncooled infrared imagers. The ADC's coarse conversion adopts a current-mode DAC to feedback charge into a CTIA-based integrator during integration and the fine conversion is implemented by a SAR ADC after integration. A current self-compensator and a modified DAC are proposed to reduce more than 50% power of the overall integrator compared with conventional structure. The 1024 × 768 readout circuit has been fabricated in the 0.18 μm 1P5M CMOS process. Power consumption of the readout circuit is 104mW and each column ADC only consumes 15μW. Simulation results show that the RMS noise of the readout circuit is 0.9LSB and nonlinearity is 0.06% at 30fps.

关键词： Power demand circuits and systems Simulation Linearity CMOS process Silicon

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Multiple anomalous Hall effects induced by partial Mn substitution for V in the kagome metal ScV6Sn6

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Physical Review B 2025年第19期111卷 195134-195134页

作者： Zicheng Tao Jianyang Ding Haonan Wang Zhicheng Jiang Renjie Zhang Yaobo Huang Zhenzhong Yang Dawei Shen Shihao Zhang Yanfeng Guo State Key Laboratory of Quantum Functional Materials School of Physical Science and Technology ShanghaiTech University Shanghai 201210 China National Key Laboratory of Materials for Integrated Circuits Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China National Synchrotron Radiation Laboratory and School of Nuclear Science and Technology University of Science and Technology of China Hefei 230026 China Key Laboratory of Polar Materials and Devices (MOE) Shanghai Center of Brain-Inspired Intelligent Materials and Devices School of Physics and Electronic Science East China Normal University Shanghai 200241 China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China Beijing National Laboratory for Condensed Matter Physics and Institute of Physics Chinese Academy of Sciences Beijing 100190 China and University of Chinese Academy of Sciences Beijing 100049 China Shanghai Synchrotron Radiation Facility Shanghai Advanced Research Institute Chinese Academy of Sciences 201204 Shanghai China School of Physics and Electronics Hunan University Changsha 410082 China ShanghaiTech Laboratory for Topological Physics Shanghai 201210 China

The kagome metal ScV6Sn6 displays a variety of exotic electronic states, including charge density wave (CDW) order, nontrivial topological states, Van Hove singularity (VHS), and electronic nematic order, which strongly correlate with the V kagome lattice. We unveil herein that the partial substitution of V by Mn in the kagome lattice ScV6Sn6 results in multiple magnetic transitions including the long-range magnetic order above room temperature with the transition temperature of ∼320 K. In addition, the Mn substitution suppresses the CDW order and the VHSs, while leading to unconventional anomalous and other anomalous Hall effects caused by both intrinsic Berry curvature and extrinsic skew scattering. Our work provides valuable insights into the correlation between magnetism and the electronic band structure of the kagome lattice, which is instructive for future research on this system.

关键词： Charge density waves

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Efficient 16 Boolean logic and arithmetic based on bipolar oxide memristors

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Science China(Information Sciences) 2020年第10期63卷 203-210页

作者： Rui YUAN Mingyuan MA Liying XU Zhenhua ZHU Qingxi DUAN Teng ZHANG Yu ZHU Yu WANG Ru HUANG Yuchao YANG Key Laboratory of Microelectronic Devices and Circuits (MOE) Department of Micro/NanoelectronicsPeking University Department of Electronic Engineering Beijing National Research Center for Information Science and TechnologyTsinghua University Center for Brain Inspired Chips Academy for Artificial IntelligencePeking University Frontiers Science Center for Nano-optoelectronics Peking University

The physically separated memory and logic units in traditional von Neumann computers place essential limits on the performance and cause increased energy consumption, and hence in-memory computing is required to overcome this bottleneck. Here, a new bipolar memristor based in-memory logic approach is proposed, which is capable of achieving all 16 possible Boolean logic functions in a single device in less than3 steps. This approach does not require initialization and can facilitate logic cascading, and the calculation taking place in-situ is showcased by 1-bit full adder and 2-bit multiplier with improved efficiency, thus showing a great prospect in future in-memory computing architecture.

关键词： memristor nonvolatile logic in-memory computing full adder multiplier

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Deep Understanding of Reliability in Hf-based FeFET during Bipolar Pulse Cycling: Trap Profiling for Read-After-Write Delay and Memory Window Degradation

Deep Understanding of Reliability in Hf-based FeFET during B...

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International Electron devices Meeting (IEDM)

作者： Puyang Cai Tianxiang Zhu Jiahui Duan Zixuan Sun Hao Li Yongkang Xue Zhiwei Liu Hao Xu Liangliang Zhang Xiaolei Wang Zhigang Ji Runsheng Wang Ru Huang School of Integrated Circuits Peking University Beijing China Key Laboratory of Microelectronics Devices and Integrated Technology Chinese Academy of Sciences Beijing China School of Microelectronics University of Chinese Academy of Sciences Beijing China Departure of Micro/Nano Electronics Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665489607

Reliability issues are the last hurdle for the hafnium-based FeFET to be adopted in practice. This work is focused on two key reliability issues: the read-after-write delay and memory window degradation under bipolar pulse operation. Through advanced characterization, two types of traps are identified in FeFET. Further investigation reveals that the fast type is in the interfacial layer and the slow type is in the ferroelectric layer. Due to the dramatically different properties in trapping kinetics and energy locations, they become the key roots for these two reliability issues, respectively. The deep understanding of the origin of these reliability issues paves the way for future process optimization.

关键词： Degradation Pulse measurements Switches Materials reliability Logic gates Iron Delays

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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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Topology Optimization of Random Memristors for Input-Aware Dynamic SNN

arXiv

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arXiv 2024年

作者： Wang, Bo Wang, Shaocong Lin, Ning Li, Yi Yu, Yifei Zhang, Yue Yang, Jichang Wu, Xiaoshan He, Yangu Wang, Songqi Chen, Rui Li, Guoqi Qi, Xiaojuan Wang, Zhongrui Shang, Dashan 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 Key Lab of Fabrication Technologies for Integrated Circuits Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

There is unprecedented development in machine learning, exemplified by recent large language models (GPT4) and world simulators (SORA), which are artificial neural networks (ANNs) running on digital computers. However, they still cannot parallel human brains in terms of energy efficiency and the streamlined adaptability to inputs of different difficulties, due to differences in signal representation, optimization, run-time reconfigurability, and hardware architecture. To address these fundamental challenges, we introduce pruning optimization for input-aware dynamic memristive spiking neural network (PRIME). Signal representation-wise, PRIME employs leaky integrate-and-fire neurons to emulate the brain’s inherent spiking mechanism. Drawing inspiration from the brain’s structural plasticity, PRIME optimizes the topology of a random memristive SNN, without expensive memristor conductance fine-tuning. For runtime reconfigurability, inspired by the brain’s dynamic adjustment of computational depth, PRIME employs an input-aware dynamic early stop policy to minimize latency during inference, thereby boosting energy efficiency without compromising performance. Architecture-wise, PRIME leverages memristive in-memory computing, mirroring the brain and mitigating the von Neumann bottleneck. We validated our system using a 40 nm 256 Kb memristor-based in-memory computing macro on neuromorphic image classification and image inpainting. Our results demonstrate the classification accuracy and Inception Score (IS) are comparable to the software baseline, while achieving 37.83× and 62.50× improvements in energy efficiency. Furthermore, we attained 77.0% and 12.5% computational load savings with minimal reduction in performance. The system also exhibits robustness against stochastic synaptic noise of analogue memristors. Our software-hardware co-designed model paves the way to future brain-inspired neuromorphic computing of brain-like energy efficiency and adaptivity. Copyright © 202

关键词： Memristors

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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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Applications of MXenes in human-like sensors and actuators

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Nano Research 2023年第4期16卷 5767-5795页

作者： Jinbo Pang Songang Peng Chongyang Hou Xiao Wang Ting Wang Yu Cao Weijia Zhou Ding Sun Kai Wang Mark H.Rümmeli Gianaurelio Cuniberti Hong Liu Institute for Advanced Interdisciplinary Research(iAIR) Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of ShandongUniversity of JinanJinan 250022China Institute for Materials Science and Max Bergmann Center of Biomaterials Technische Universität DresdenDresden 01069Germany Center for Advancing Electronics Dresden Technische Universität DresdenDresden 01069Germany Dresden Center for Computational Materials Science Technische Universität DresdenDresden 01062Germany Dresden Center for Intelligent Materials(GCL DCIM) Technische Universität DresdenDresden 01062Germany 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 Shenzhen Key Laboratory of Nanobiomechanics Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China State Key Laboratory of Biobased Material and Green Papermaking Qilu University of TechnologyShandong Academy of SciencesJinan 250353China School of Bioengineering Qilu University of TechnologyShandong Academy of ScienceJinan 250353China Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology(Ministry of Education) Northeast Electric Power UniversityJilin 132012China School of Electrical Engineering Northeast Electric Power UniversityJilin 132012China School of Electrical Engineering Weihai Innovation Research InstituteQingdao UniversityQingdao 266000China School of Electrical and Computer Engineering Jilin Jianzhu UniversityChangchun 130118China Institute for Complex Materials Leibniz Institute for Solid State and Materials Research Dresden(IFW Dresden)20 Helmholtz StrasseDresden 01069Germany College of Energy Soochow Institute for Energy and Materials Innovations Soochow UniversitySuzhou 215006China Key L

Human beings perceive the world through the senses of sight,hearing,smell,taste,touch,space,and *** first five senses are prerequisites for people to *** sensing organs upload information to the nervous systems,including the brain,for interpreting the surrounding ***,the brain sends commands to muscles reflexively to react to stimuli,including light,gas,chemicals,sound,and ***,as an emerging two-dimensional material,has been intensively adopted in the applications of various sensors and *** this review,we update the sensors to mimic five primary senses and actuators for stimulating muscles,which employ MXene-based film,membrane,and composite with other functional ***,a brief introduction is delivered for the structure,properties,and synthesis methods of ***,we feed the readers the recent reports on the MXene-derived image sensors as artificial retinas,gas sensors,chemical biosensors,acoustic devices,and tactile sensors for electronic ***,the actuators of MXene-based composite are ***,future opportunities are given to MXene research based on the requirements of artificial intelligence and humanoid robot,which may induce prospects in accompanying healthcare and biomedical engineering applications.

关键词： MXenes sensors actuators gas sensors biosensors tactile sensors sound sensors artificial retina

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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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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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