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arXiv 2024年

作者： Chen, Hegan Yang, Jichang Chen, Jia Wang, Songqi Wang, Shaocong Wang, Dingchen Tian, Xinyu Yu, Yifei Chen, Xi Lin, Yinan He, Yangu Wu, Xiaoshan Li, Yi Zhang, Xinyuan Lin, Ning Xu, Meng Li, Yi Zhang, Xumeng Wang, Zhongrui Wang, Han Shang, Dashan 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 Institute of the Mind The University of Hong Kong Hong Kong Key Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China 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 State Key Laboratory of Integrated Chips and Systems Frontier Institute of Chip and System Fudan University Shanghai200433 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Digital twins, the cornerstone of Industry 4.0, replicate real-world entities through computer models, revolutionising fields such as manufacturing management and industrial automation. Recent advances in machine learning provide data-driven methods for developing digital twins using discrete-time data and finite-depth models on digital computers. However, this approach fails to capture the underlying continuous dynamics and struggles with modelling complex system behaviour. Additionally, the architecture of digital computers, with separate storage and processing units, necessitates frequent data transfers and Analogue-Digital (A/D) conversion, thereby significantly increasing both time and energy costs. Here, we introduce a memristive neural ordinary differential equation (ODE) solver for digital twins, which is capable of capturing continuous-time dynamics and facilitates the modelling of complex systems using an infinite-depth model. By integrating storage and computation within analogue memristor arrays, we circumvent the von Neumann bottleneck, thus enhancing both speed and energy efficiency. We experimentally validate our approach by developing a digital twin of the HP memristor, which accurately extrapolates its nonlinear dynamics, achieving a 4.2-fold projected speedup and a 41.4-fold projected decrease in energy consumption compared to state-of-the-art digital hardware, while maintaining an acceptable error margin. Additionally, we demonstrate scalability through experimentally grounded simulations of Lorenz96 dynamics, exhibiting projected performance improvements of 12.6-fold in speed and 189.7-fold in energy efficiency relative to traditional digital approaches. By harnessing the capabilities of fully analogue computing, our breakthrough accelerates the development of digital twins, offering an efficient and rapid solution to meet the demands of Industry 4.0. © 2024, CC BY-NC-SA.

关键词： Energy utilization

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Polarization tunable bidirectional photoresponse in Van der Waals α−In2Se3/NbX2 (X=S,Se,andTe) ferroelectric diodes

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Physical Review Materials 2023年第8期7卷 084412-084412页

作者： Shibo Fang Qiuhui Li Chen Yang Baochun Wu Shiqi Liu Jie Yang Jiachen Ma Zongmeng Yang Kechao Tang Jing Lu State Key Laboratory for Mesoscopic Physics and School of Physics Peking University Beijing 100871 People's Republic of China State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100871 People's Republic China Key Laboratory of Spintronics Materials Devices and Systems of Zhejiang Province Hangzhou 311305 People's Republic of China Key Laboratory of Material Physics School of Physics and Microelectronics Ministry of Education Zhengzhou University Zhengzhou 450001 People's Republic of China Research Center for Materials Architectures and Integration of Nanomembranes (MAIN) Chemnitz University of Technology 09126 Chemnitz Germany School of Integrated Circuits Peking University Beijing 100871 People's Republic of China Collaborative Innovation Center of Quantum Matter Beijing 100871 People's Republic of China Beijing Key Laboratory for Magnetoelectric Materials and Devices (BKL-MEMD) Peking University Beijing 100871 People's Republic of China Peking University Yangtze Delta Institute of Optoelectronics Nantong 226010 People's Republic of China Key Laboratory for the Physics and Chemistry of Nanodevices Peking University Beijing 100871 People's Republic of China

Ferroelectric diodes can generate a polarization-controlled bidirectional photoresponse to simulate inhibition and promotion behaviors in the artificial neuromorphic system with fast speed, high energy efficiency, and nonvolatility. However, the existing ferroelectric diodes based on ferroelectric oxides suffer from a weak bidirectional photoresponse (below 1 mA/W), difficult miniaturization, and a large response photon energy (over 3 eV). Here, we design a series of van der Waals α−In2Se3/NbX2 (X = S, Se, and Te) ferroelectric diodes with bidirectional photoresponse by using ab initio quantum transport simulation. These devices show a maximum bidirectional photoresponse of 30 (−19) mA/W and a minimum response photon energy of 1.3 eV at the monolayer thickness. Our work shows advanced optoelectronic applications of the van der Waals ferroelectric diodes in the future artificial neuromorphic system.

关键词： Ferroelectricity First-principles calculations Photocurrent 2-dimensional systems Diodes Ferroelectrics Density functional theory Landauer formula

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Density-wave-like gap evolution in La3Ni2O7 under high pressure revealed by ultrafast optical spectroscopy

arXiv

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arXiv 2024年

作者： Meng, Yanghao Yang, Yi Sun, Hualei Zhang, Sasa Luo, Jianlin Chen, Liucheng Ma, Xiaoli Wang, Meng Hong, Fang Wang, Xinbo Yu, Xiaohui Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Education Ministry Laser and Infrared System Integration Technology Shandong University Qingdao266237 China School of Science Sun Yat-sen University Shenzhen518107 China School of Information Science and Engineering Shandong University Qingdao266237 China Songshan Lake Materials Laboratory Guangdong Dongguan523808 China Center for Neutron Science and Technology Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices School of Physics Sun Yat-Sen University Guangzhou China

Density wave (DW) order is believed to be correlated with superconductivity in the recently discovered high-temperature superconductor La3Ni2O7. However, experimental investigations of its evolution under high pressure are still lacking. Here, we explore the quasiparticle dynamics in bilayer nickelate La3Ni2O7 single crystals using ultrafast optical pump-probe spectroscopy under high pressures up to 34.2 GPa. At ambient pressure, the temperature-dependent relaxation dynamics demonstrate a phonon bottleneck effect due to the opening of an energy gap around 151 K. The energy scale of the DW-like gap is determined to be 66 meV by the Rothwarf-Taylor model. Combined with recent experiential results, we propose that this DW-like transition at ambient pressure and low temperature is spin density wave (SDW). With increasing pressure, this SDW order is significantly suppressed up to 13.3 GPa before it completely disappears around 26 GPa. Remarkably, at pressures above 29.4 GPa, we observe the emergence of another DW-like order with a transition temperature of approximately 135 K, which is probably related to the predicted charge density wave (CDW) order. Our study provides the experimental evidence of the evolution of the DW-like gap under high pressure, offering critical insights into the correlation between DW order and superconductivity in La3Ni2O7. Copyright © 2024, The Authors. All rights reserved.

关键词： High temperature superconductors

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Hf1-xZrxO2based bipolar selector with high uniformity and high selectivity for large-scale integration of memristor crossbars

Hf1-xZrxO2based bipolar selector with high uniformity and hi...

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IEEE Electron devices technology and Manufacturing Conference (EDTM)

作者： Caidie Cheng Keqin Liu Bingjie Dang Living Xu Zhen Yang Xiaoqin Yan Yuchao Yang Ru Huang Key Laboratory of Microelectronic Devices and Circuits (MOE) Peking University Beijing China State Key Laboratory for Advanced Metals and Materials School of Materials Science and Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781728181769;9781728181776

The sneak path problem is a main limitation that affects the scale of the memristor crossbar array and hence its practical applications in memory and computing. Here, a bipolar, highly nonlinear selector based on Hf_1-xZr_xO₂ (HZO) was fabricated, which exhibits remarkable performance, including high selectivity of >10⁶, robust endurance of >10⁷, off-state current of ~pA and excellent uniformity, making it attractive for future high-density memristor crossbar arrays.

关键词： Neuromorphic engineering Memristors Switches Large scale integration Manufacturing Leakage currents

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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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Plasmon hybridization induced by quasi bound state in the continuum of graphene metasurfaces oriented for high-accuracy polarization-insensitive two-dimensional sensors

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Chinese Optics Letters 2022年第4期20卷 65-70页

作者： Xiuyu Wang Jihong Xin Qun Ren Haocheng Cai Jiaqi Han Chengyi Tian Pengcheng Zhang Lijie Jiang Zhihao Lan Jianwei You Wei E.I.Sha Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology School of MicroelectronicsTianjin UniversityTianjin 300072China School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China Key Laboratory of High Speed Circuit Design and EMC of Ministry of Education School of Electronic EngineeringXidian UniversityXi'an 710071China Huawei Technologies Company Ltd. Shanghai 518129China Department of Electronic and Electrical Engineering University College LondonLondon WC1E7JEUK Key Laboratory of Micro-Nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science and Electronic EngineeringZhejiang UniversityHangzhou 310027China

Plasmonics could provide compact and powerful solutions for manipulating light in deep-subwavelength dimensions,which is promising for a great range of nanophotonic technologies such as plasmonic rulers and ***,the effective area of enhanced localized field induced by surface plasmon polaritons is typically restricted to the structural *** this work,we propose a method to generate high quality-factor extended electromagnetic fields via hybridizing the superradiant state and the quasi bound state in the continuum of graphene *** coupling interaction involved operates as a three-level system with multiple sharp resonances immune to the polarization,which holds great promise for developing nanodevices with high sensing capacity in two dimensions.

关键词： plasmon hybridization quasi bound state in the continuum high-Q sensing

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In-depth Understanding of Polarization Switching Kinetics in Polycrystalline $\mathbf{Hf}_{0.5} \mathbf{Zr}_{0.5} \mathbf{O}_{2}$ Ferroelectric Thin Film: A Transition From NLS to KAI

In-depth Understanding of Polarization Switching Kinetics in...

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International Electron devices Meeting (IEDM)

作者： Wei Wei Weiqiang Zhang Lu Tai Guoqing Zhao Pengpeng Sang Qianwen Wang Fei Chen Mingfeng Tang Yang Feng Xuepeng Zhan Qing Luo Yuan Li Jiezhi Chen School of Information Science and Engineering Shandong University Qingdao China Key Laboratory of Microelectronic Devices and Integrated Technology Institute of Microelectronics of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665425735

To achieve deep insights into the polarization switching kinetics in ferroelectric $\text{Hf}0.5\text{Zr}0.5\mathrm{O}_{2}$ (HZO), the intrinsic switching characteristics are measured accurately by using a novel pulse sequence to eliminate the charge trapping, polarization unsaturation and imprint effects. By NLS fittings, the average switching time and the concentration of pinning sites are extracted, with main focus on their dependences on the external electric field, the electrode size $l$ and the temperature $T$ . Although the average switching time shows weak dependence on $T,$ , it decreases sharply with $l$ scaling and sub-ns switching behavior is predicted at $l\leq 3.89\mu \mathrm{m}$ . More importantly, our observations intensely imply that a transition happens from NLS to KAI model when $T$ is below 161K or $l$ is approaching the grain size (10~30nm). This surprised but reasonable transition indicates that, as scaling the ferroelectric memory size or for operations under cryogenic temperatures, HZO ferroelectrics can switch much faster and more uniform.

关键词： Electrodes Temperature dependence Voltage measurement Pulse measurements Switches Transforms Size measurement

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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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Structural phase transition of monochalcogenides investigated with machine learning

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Physical Review B 2022年第9期105卷 094116-094116页

作者： J. Zhang F. Zhang D. Wei L. Liu X. Liu D. Fang G. X. Zhang X. Chen D. Wang School of Microelectronics & State Key Laboratory for Mechanical Behavior of Materials Xi'an Jiaotong University Xi'an 710049 China Key Lab of Micro-Nano Electronics and System Integration of Xi'an City Xi'an Jiaotong University Xi'an 710049 China Guangxi Key Laboratory of Optical and Electronic Materials and Devices College of Materials Science and Engineering Guilin University of Technology Guilin 541004 China Engineering Research Center of Nanoelectronic Integration and Advanced Equipment Ministry of Education School of Physics and Electronic Science East China Normal University Shanghai 200062 China MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter School of Physics Xi' an Jiaotong University Xi'an 710049 China School of Chemistry and Chemical Engineering Harbin Institute of Technology Harbin 150001 China Department of Applied Physics Aalto University Espoo 00076 Finland

As machine learning becomes increasingly important in science and engineering, it holds the promise to provide a universal approach applicable to various systems to investigate their crystalline phase transitions. Here, we build and train accurate artificial neural networks that can distinguish tiny energy difference, which is crucial to predict the crystalline phase transitions. Employing the trained artificial neural networks in Monte Carlo simulations as the surrogate energy function, we apply this approach to monochalcogenides, including bulk and two-dimensional monolayer SnTe and GeTe, investigating their crystalline phase transitions. The machine-learning approach, when viewed as providing a universal mathematical structure, can be transferred to the investigation of other materials when the training data set generated with ab initio methods are available. Moreover, the machine-learning approach resolves the difficulties associated with constructing the effective Hamiltonian for monochalcogenides, showing great potential with its accuracy and efficiency.

关键词： Ferroelectricity Structural phase transition Chalcogenides Machine learning

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A Systematic Characterization Method for Time-resolved Stability and Reliability Issues on Lateral GaN Power devices

A Systematic Characterization Method for Time-resolved Stabi...

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International Symposium on Power Semiconductor devices and Ics (ISPSD)

作者： Yifei Huang Qimeng Jiang Sen Huang Xinyu Liu High-Frequency High-Voltage Device and Integrated Circuits R&D Center Institute of Microelectronics of the Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics of the Chinese Academy of Sciences Beijing China Institute of Microelectronics University of Chinese Academy of Sciences Beijing China

In this work, three testing modes, including continuous hard switching testing (HSW), high-voltage DC stress testing (DC) and recovery testing (RE), are implemented to characterize time-resolved dynamic $R_{\text{ON}}$ behaviors of GaN HEMT devices, based on the inductive-load evaluation platform. The proposed stressing pattern (DC-HSW-DC-RE) enables the separation of de-stress- and transient-stress-induced dynamic $R_{\text{ON}}$ . Based on the stressing pattern, a novel physical-based characterization method is proposed to identify the irreversible degradation of dynamic $R_{\text{ON}}$ , featuring excellent sensitivity when compared with the traditional methods. In addition, lifetime acceleration experiments are carried out, and the irreversible $R_{\text{ON}}$ degradation exhibits a strong dependence on voltage and current, but a weak dependence on temperature.

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