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arXiv 2023年

作者： Li, Yi Wang, Songqi Zhao, Yaping Wang, Shaocong Zhang, Woyu He, Yangu Lin, Ning Cui, Binbin Chen, Xi Zhang, Shiming Jiang, Hao Lin, Peng Zhang, Xumeng Qi, Xiaojuan Wang, Zhongrui Xu, Xiaoxin Shang, Dashan Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong State Key Lab of Fabrication Technologies for Integrated Circuits Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China Frontier Institute of Chip and System Fudan University Shanghai200433 China College of Computer Science and Technology Zhejiang University Zhejiang310027 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong University of Chinese Academy of Sciences Beijing100049 China Institute of Mind The University of Hong Kong Hong Kong

The rapid advancement of artificial intelligence (AI) has been marked by the large language models exhibiting human-like intelligence. However, these models also present unprecedented challenges to energy consumption and environmental sustainability, which are exacerbated by the increasing number of users and the development of even larger models. One promising solution is to revisit analogue computing, a technique that predates digital computing and exploits emerging analogue electronic devices, such as resistive memory, which features in-memory computing, high scalability, and nonvolatility that addresses the von Neumann bottleneck, slowdown of Moore’s law, and volatile DRAM of conventional digital hardware. However, analogue computing still faces the same challenges as before: programming nonidealities and expensive programming due to the underlying devices physics. Therefore, leveraging the efficiency advantage while mitigating the programming disadvantage of analogue computing with resistive memory is a major open problem in AI hardware and electronics communities. Here, we report a universal solution, software-hardware co-design using structural plasticity-inspired edge pruning to optimize the topology of a randomly weighted analogue resistive memory neural network. Software-wise, the topology of a randomly weighted neural network is optimized by pruning connections rather than precisely tuning resistive memory weights. Hardware-wise, we reveal the physical origin of the programming stochasticity using transmission electron microscopy, which is leveraged for large-scale and low-cost implementation of an overparameterized random neural network containing high-performance sub-networks. We implemented the co-design on a 40nm 256K resistive memory macro, observing 17.3% and 19.9% accuracy improvements in image and audio classification on FashionMNIST and Spoken digits datasets, as well as 9.8% (2%) improvement in PR (ROC) in image segmentation on DRIVE datasets, r

关键词： Energy utilization

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Ferroelectricity in HfO2 from a chemical perspective

arXiv

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arXiv 2022年

作者： Yuan, Jun-Hui Mao, Ge-Qi Xue, Kan-Hao Bai, Na Wang, Chengxu Cheng, Yan Lyu, Hangbing Sun, Huajun Wang, Xingsheng Miao, Xiangshui School of Integrated Circuits School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China Hubei Yangtze Memory Laboratories Wuhan430205 China Department of Electronics East China Normal University Shanghai200241 China Key Laboratory of Microelectronics Devices and Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China

Ferroelectricity observed in thin film HfO2, either doped with Si, Al, etc. or in the Hf0.5Zr0.5O2 form, has gained great technical significance. However, the soft mode theory faces a difficulty in explaining the origin of such ferroelectricity. In this work, we propose that the 7 cation coordination number of HfO2/ZrO2 lies at the heart of this ferroelectricity, which stems from the proper ionic radii of Hf/Zr compared with O. Among the numerous compounds with non-centrosymmetric nature, e.g., mm2 point group, HfO2 and ZrO2 are special in that they are close to the border of 7 and 8 cation coordination, such that the 8-coordination tetragonal intermediate phase could greatly reduce the switching barrier. Other 7-coordination candidates, including SrI2, TaON, YSBr and YOF are also studied in comparison to HfO2/ZrO2, and six switching paths are analyzed in detail for the Pca21 phase. A rule of preferred switching path in terms of ionic radii ratio and coordination number has been established. We also show the possible route from ferroelectric Pca21 phase to monoclinic P21/c phase in HfO2, which is relevant to the fatigue phenomenon. Copyright © 2022, The Authors. All rights reserved.

关键词： Ferroelectricity

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Resistive memory-based zero-shot liquid state machine for multimodal event data learning

arXiv

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arXiv 2023年

作者： Lin, Ning Wang, Shaocong Li, Yi Wang, Bo Shi, Shuhui He, Yangu Zhang, Woyu Yu, Yifei Zhang, Yue Zhang, Xinyuan Wong, Kwunhang Wang, Songqi Chen, Xiaoming Jiang, Hao Zhang, Xumeng Lin, Peng Xu, Xiaoxin Qi, Xiaojuan Wang, Zhongrui Shang, Dashan Liu, Qi Liu, Ming Department of Electrical and Electronic Engineering the University of Hong Kong Hong Kong Key Lab of Fabrication Technologies for Integrated Circuits and Key Laoratory of Microelectronic Devices and Integrated Technology Institute of Microelectronics the Chinese Academy of Sciences Beijing100029 China The School of Microelectronics Southern University of Science and Technology Shenzhen518055 China ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong University of Chinese Academy of Sciences Beijing100049 China Frontier Institute of Chip and System Fudan University Shanghai200433 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

The human brain is a complex spiking neural network (SNN), capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges. Hardware limitations, such as the slowdown of Moore’s law and Von Neumann bottleneck, hinder the efficiency of digital computers. Additionally, SNNs are characterized by their software training complexities. To this end, we propose a hardware-software co-design on a 40 nm 256 Kb in-memory computing macro that physically integrates a fixed and random liquid state machine (LSM) SNN encoder with trainable artificial neural network (ANN) projections. We showcase the zero-shot LSM-based learning of multimodal events on the N-MNIST and N-TIDIGITS datasets, including visual and audio data association, as well as neural and visual data alignment for brain-machine interfaces. Our co-design achieves classification accuracy comparable to fully optimized software models, resulting in a 152.83 and 393.07-fold reduction in training costs compared to SOTA contrastive language-image pre-training (CLIP) and Prototypical networks, and a 23.34 and 160-fold improvement in energy efficiency compared to cutting-edge digital hardware, respectively. These proof-of-principle prototypes demonstrate zero-shot multimodal events learning capability for emerging efficient and compact neuromorphic hardware. © 2023, CC BY-NC-SA.

关键词： Image enhancement

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Highly-Sensitive FET-based Sensor via Heterogeneous Selective-Assembling Integration of Porphyrin and Silicon Nanowires

Highly-Sensitive FET-based Sensor via Heterogeneous Selectiv...

引用

International Conference on Solid-State and Integrated Circuit Technology

作者： Xiaokang Li Bocheng Yu Gong Chen Ming Li Key Laboratory of Microelectronic Devices and Circuits (MOE) Institute of Microelectronics Peking University Beijing China Frontiers Science Center for Nano-optoelectronics Peking University Beijing China

ISBN: (数字)9781728162355

ISBN: (纸本)9781728162362

In this paper, we proposed and fabricated a novel heterogeneous porphyrin/silicon nanowire transistor-based sensor using CMOS process technology. With the difference in hydrophobicity of Si 3 N 4 and SiO 2 , the enrichment of porphyrin molecules on silicon nanowires was achieved, and the device showed highly sensitive photo-sensing even under very low illumination power (ΔVt=3.32V @11.3μW/cm 2 ), great dynamic photo-memory characteristic (ΔV t =31.3V @ programming/erasing time of 10s) and excellent temperature sensing (ΔV t =13.6V @ 90°C temperature range). Those performances make the devices applicable to be used in applications of sensitive and smart non-memory light sensing and reliable temperature sensing.

关键词： Nanowires Silicon Sensors Temperature sensors Threshold voltage Sensitivity Transistors

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TCAD Simulation on Random Telegraphy Noise and Grain-Induced Fluctuation of 3D Nand Cell Transisitors

TCAD Simulation on Random Telegraphy Noise and Grain-Induced...

引用

China Semiconductor Technology International Conference (CSTIC)

作者： Shijie Hu Ming Li Ru Huang Key Laboratory of Microelectronic Devices and Circuits (MOE) Institute of Microelectronics Peking University Beijing CHINA Frontiers Science Center for Nano-optoelectronics Peking University Beijing CHINA

ISBN: (数字)9781728165585

ISBN: (纸本)9781728165592

In this work, a TCAD simulation platform was set up to study the real poly-channel modeling, trap-induced noise and random grain doping in 3D NAND cell transistor. The random telegraph noise and size dependence was simulated and analyzed. Simulation results show that the RTN and grain size change have greater influence on threshold voltage (V T ) and channel current (I D ) in 3D NAND than doping fluctuation. It is shown that the instability caused by random doping cannot be ignored, too.

关键词： Solid modeling Three-dimensional displays Fluctuations Doping Tools Semiconductor process modeling Transistors

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A New Design Methodology of Highly Reliable TFT Based Integrated circuits in Display Applications

A New Design Methodology of Highly Reliable TFT Based Integr...

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International Conference on Display Technology, ICDT 2019

作者： Geng, Di Su, Yue Li, Ling Liu, Ming Key Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing China

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Efficient Electroabsorption Modulation of Mid- and Far-Infrared Radiation by Driving the Band-Inversion Transition of InAs/GaSb Type-II Quantum Wells

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Physical Review Applied 2021年第6期16卷 064053-064053页

作者： Jun Li Jiang-Tao Liu Zhenhua Wu Wei Huang Cheng Li Department of Physics Semiconductor Photonics Research Center Xiamen University Xiamen 361005 China College of Mechanical and Electrical Engineering Guizhou Minzu University Guiyang 550025 China Key Laboratory of Microelectronic Devices and Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing 100029 China

Based on the self-consistent calculations of the eight-band k⋅p model and Poisson equation, we theoretically demonstrate that the intersubband optical absorption of InAs/GaSb type-II quantum wells (QWs) is effectively tuned by the electric-field-driven band-inversion transition. By utilizing this property, InAs/GaSb QWs can be made into highly efficient electroabsorption modulators (EAMs) for TM-polarized mid-infrared radiation (MIR) (approximately 4.2–12μm) and TE-polarized far-infrared radiation (FIR) (approximately 12–100μm). At low temperature, an example p-i-n waveguide EAM composed of six-period InAs/GaSb QWs achieves a drive voltage of <0.5 V, a 3-dB cutoff bandwidth of approximately 24.6 GHz, and a dynamic power consumption of ≤0.06 mW/GHz for a 20-dB on:off ratio modulation of 4.46-μm TM-polarized radiation. Although the modulation efficiencies drop by 70%–80% when the temperature increases, the performance of InAs/GaSb EAMs is (at least) comparable with conventional 1.55-μm EAMs at 300 K. These results suggest that InAs/GaSb EAMs are promising devices for high-speed and low-power-consumption MIR (or FIR) free-space optical communications.

关键词： Integrated optics Optoelectronics Heterostructures III-V semiconductors Quantum wells Two-dimensional electron gas Waveguides Infrared techniques

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

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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

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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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Hybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks

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Science Bulletin 2021年第16期66卷 1624-1633,M0003页

作者： Xumeng Zhang Jian Lu Zhongrui Wang Rui Wang Jinsong Wei Tuo Shi Chunmeng Dou Zuheng Wu Jiaxue Zhu Dashan Shang Guozhong Xing Mansun Chan Qi Liu Ming Liu Frontier Institute of Chip and System Fudan UniversityShanghai 200433China Key Laboratory of Microelectronic Devices&Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China University of Chinese Academy of Sciences Beijing 100049China Department of Electrical and Electronic Engineering the University of Hong KongHong KongChina Zhejiang Laboratory Hangzhou 311122China Department of Electronic and Computer Engineering the Hong Kong University of Science and TechnologyHong KongChina

Spiking neural network,inspired by the human brain,consisting of spiking neurons and plastic synapses,is a promising solution for highly efficient data processing in neuromorphic ***,memristor-based neurons and synapses are becoming intriguing candidates to build spiking neural networks in hardware,owing to the close resemblance between their device dynamics and the biological ***,the functionalities of memristor-based neurons are currently very limited,and a hardware demonstration of fully memristor-based spiking neural networks supporting in-situ learning is very ***,a hybrid spiking neuron combining a memristor with simple digital circuits is designed and implemented in hardware to enhance neuron *** hybrid neuron with memristive dynamics not only realizes the basic leaky integrate-and-fire neuron function but also enables the in-situ tuning of the connected synaptic ***,a fully hardware spiking neural network with the hybrid neurons and memristive synapses is experimentally demonstrated for the first time,and in-situ Hebbian learning is achieved with this *** work opens up a way towards the implementation of spiking neurons,supporting in-situ learning for future neuromorphic computing systems.

关键词： Memristor Hybrid neuron In-situ learning Fully hardware Spiking neural network

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