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Deep-Learning-Enhanced Single-Spin Readout in Silicon Carbide at Room Temperature

Physical Review Applied 2022年第3期17卷 034046-034046页

作者： Yu-Wei Liao Qiang Li Mu Yang Zheng-Hao Liu Fei-Fei Yan Jun-Feng Wang Ji-Yang Zhou Wu-Xi Lin Yi-Dan Tang Jin-Shi Xu Chuan-Feng Li Guang-Can Guo CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei 230026 People’s Republic of China CAS Center For Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei 230026 People’s Republic of China High-Frequency High-Voltage Devices and Integrated Circuits R&D Center Institute of Microelectronics of Chinese Academy of Sciences 100029 Beijing China

Defect spin qubits in silicon carbide have recently drawn widespread attention. Extraction of spin information in the presence of noise always requires multiple repeated measurements, which consumes a large amount of time resources. In this paper, we propose a deep-learning-enhanced method to extract effective parameters from continuous-wave optically detected magnetic resonance (ODMR) spectra and Rabi oscillations with less time consumption. Even if the signal-to-noise ratio is reasonably low, a well-trained convolutional neural network (CNN) can predict the resonance peaks of ODMR spectra or the period of Rabi oscillations. Because of the fast output of predictions by the CNN, this method can be used to sense the magnetic field in the environment and microwave intensities in real time.

关键词： Coherent control Color centers Crystal defects Quantum information with solid state qubits Semiconductor compounds Artificial neural networks Machine learning Optically detected magnetic resonance

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Multi-Classification of Cardiovascular Diseases Based on Heart Sound Signals Using Audio Spectrogram Features with Pure Attention Transformer

SSRN

引用

SSRN 2022年

作者： Yang, Dongru Lin, Yi Wei, Jianwen Lin, Xiongwei Zhao, Xiaobo Yao, Yingbang Tao, Tao Liang, Bo Lu, Sheng-Guo Guangdong Provincial Research Center on Smart Materials and Energy Conversion Devices Guangdong Provincial Key Laboratory of Functional Soft Condensed Matter School of Integrated Circuits School of Materials and Energy Guangdong University of Technology Guangzhou510006 China School of Microelectronics Shenzhen Institute of Information Technology Shenzhen518000 China

Background and objectives: In computer-aided medical diagnosis or prognosis, the automatic classification of cardiovascular diseases (CVDs) based on heart sound (HS) signals is of great importance since the heart sound signal contains a wealth of information that can reflect the cardiovascular status. Traditional binary classification algorithms (normal and abnormal) currently cannot comprehensively assess CVDs based on analyzing various heart sounds. The differences between heart sound signals are relatively subtle, but the reflected heart conditions differ significantly. Consequently, the multiclassification of CVDs from heart sound signals is of utmost importance from a clinical ***: For the multi-classification of heart sound signals, we propose a creative non-convolutional neural networks (CNN) pure-attention transformer model. It has achieved remarkable results from four abnormal HS signals and the typical ***: According to a fivefold cross-validation strategy, the proposed method achieves a mean classification accuracy of 99% and a mean average precision of 0.99. Further, the classification accuracy for Aortic Stenosis (AS), Mitral Regurgitation (MR), Mitral Stenosis (MS), Mitral Valve Prolapse (MVP), and standard heart sound signals (N) is 99.39%, 99.53%, 99.42%, 99.48%, and 99.67%, ***: The results indicate that the framework can precisely classify five classes of heart sound signals. Our method provides an efficient tool for CVD multi-classification based on heart sound signals in clinical settings. © 2022, The Authors. All rights reserved.

关键词： Heart

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Continuous-Time Digital Twin with Analogue Memristive Neural Ordinary Differential Equation Solver

arXiv

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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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Low power and high uniformity of HfOx-based RRAM via tip-enhanced electric fields

引用

中国科学:化学（英文版） 2019年第10期62卷 160-166页

作者： Xiaokang LI Baotong ZHANG Bowen WANG Xiaoyan XU Yuancheng YANG Shuang SUN Qifeng CAI Shijie HU Xia AN Ming LI Ru HUANG Key Laboratory of Microelectronic Devices and Circuits(MOE) Institute of MicroelectronicsPeking University Beijing 100871 China

In this paper,the HfOx-based resistive random access memory (RRAM) devices with sub-100 nm pyramid-type electrodes were *** the help of tip-enhanced electric field around the pyramid-type electrodes,it was experimentally demonstrated that the novel devices have better cycle-to-cycle variation control,lower forming/set voltage (1.97/0.7 V) and faster switching speed (≤ 30 ns under 0.9 V pulse) as well as better endurance reliability than conventional flat electrode resistive memory *** addition,the novel RRAM is experimentally demonstrated with independence of electrode number for the first time to present great potential in scaling *** novel structure metal-oxide based RRAM will be suitable for the future low-power non-volatile memory application.

关键词： RRAM pyramid-type electrode variation reliability switching speed conducting filament

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A 1μW-to-158μW Output Power Pseudo Open-Loop Boost DC-DC with 86.7% Peak Efficiency using Frequency-Programmable Oscillator and Hybrid Zero Current Detection

A 1μW-to-158μW Output Power Pseudo Open-Loop Boost DC-DC w...

引用

IEEE International Symposium on circuits and Systems (ISCAS)

作者： Xiaolong Chen Enbin Gong Hao Zhang Le Ye Ru Huang Key Laboratory of Microelectronic Devices and Circuits (MOE) Institute of Microelectronics Peking university Beijing 100871 China

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

This paper proposed a pseudo-open boost DC-DC converter whose input voltage ranges from 300mV-to-500mV and output voltage ranges from 1.2V-to-1.8V. The output power ranges from 1μW to 158μW. Three key structures are designed to realize the high energy conversion efficiency when the load reaches ultra-light: the pseudo open-loop structure, the frequency-programmable oscillator, and the hybrid zero current detection (H-ZCD) circuit. The pseudo open-loop structure eliminates the analog comparators or error amplifiers used in converter and the quiescent power loss is much reduced. The frequency-programmable ring oscillator is able to output 9-bit binary frequency ranges from 500Hz-100KHz, so the power consumption of clock generation is saved when load power reaches to ultra-light. The proposed H-ZCD is designed to reduce the inductor power loss with just tiny power consumption. Accordingly, the high efficiency boost DC-DC works in the discontinuous condition mode (DCM) and the constant on time pulse frequency modulation (PFM) is proposed. As a simulation result, the peak efficiency of the converter reaches to 86.7% while the load power is 13.7μW.

关键词： Ring oscillators DC-DC power converters Power demand Switches Capacitors Energy efficiency

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An 81–99 GHz Tripler with Fundamental Cancellation and 3rd Harmonic Enhancement Technique in 40-nm CMOS

An 81–99 GHz Tripler with Fundamental Cancellation and 3rd ...

引用

IEEE International Symposium on circuits and Systems (ISCAS)

作者： Xiaolei Su Xiucheng Hao Dong Wang Zhengkun Shen Zexue Liu Junhua Liu Huailin Liao Key Laboratory of Microelectronic Devices and Circuits (MOE) Institute of Microelectronics Peking University Beijing 100871 China

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

An 81–99 GHz tripler for wireless transceiver LO generation is presented in this paper. To suppress the fundamental signal and enhance the 3rd harmonic, the inversion signal is applied to the gate of the cascading transistor of the push-push differential pair. A capacitor is connected to the source and drain of the cascading transistor to further suppress the fundamental signal. Without additional filters, the tripler achieves more than 40 dBc of fundamental suppression with an even harmonic suppression of up to 60 dBc. The tripler reaches an output power of −2.6 dBm (4-dBm input) at 91.5 GHz with a 20% bandwidth of from 81 to 99 GHz. The core area of the chip occupies only 190 μm×530 μm in TSMC 40-nm CMOS process and consumes 28.2 mW.

关键词： Harmonic analysis Power harmonic filters Transistors Radio frequency Parasitic capacitance Capacitors Inductors

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Direct Cu-polyimide Bonding Achieved by Surface Activation and Pt-catalyzed Formic Acid Atmosphere

Direct Cu-polyimide Bonding Achieved by Surface Activation a...

引用

IEEE International Conference on Integrated circuits, Technologies and Applications (ICTA)

作者： Ying Meng Runhua Gao Xinhua Wang Xiaojuan Chen Sen Huang Ke Wei Dahai Wang Fengwen Mu Xinyu Liu High-Frequency High-Voltage Device and Integrated Circuits R&D Center Institute of Microelectronics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665417488

Recent flexible printed circuit applications of high-frequency wireless communication systems require thicker metal layers on substrates for lower ohmic losses. In this paper, a Cu layer over 50-micron thick was directly generated on the polyimide substrate by heterogeneous bonding method without interlayer. The polyimide was first sputtered a 200 nm-thick Cu layer, and then was activated by oxygen plasma to improve its hydrophilicity and achieve direct adhesion between polyimide and nanoscale Cu deposition, and eventually was bonded with a 50micron-thick Cu foil in a Pt-catalyzed formic acid atmosphere by thermo-compression. The effectiveness of this method was verified by comparing bonding interfaces, shear strength, and fracture modes of bonded samples with and without a Cr interlayer. This method provides a new idea for the integration of heterogeneous materials needed in wearable products and flexible printed circuits.

关键词： Wireless communication Integrated circuit technology Flexible printed circuits Atmosphere Metals Polyimides Nanoscale devices

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

arXiv

引用

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

引用

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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Tunable synaptic devices based on ambipolar MoTe2 transistor

Tunable synaptic devices based on ambipolar MoTe2 transistor

引用

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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