1
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Panda D, Hui YF, Tseng TY. Harnessing a WO x-based flexible transparent memristor synapse with a hafnium oxide layer for neuromorphic computing. NANOSCALE 2024; 16:16148-16158. [PMID: 39114954 DOI: 10.1039/d4nr01155d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2024]
Abstract
Transparent memristor-based neuromorphic synapses are expected to be specialised devices for high-speed information transmission and processing. The synaptic linearity and potentiation/depression cycles are imperative issues for the application of memristors. This work explores a memristor for improving switching uniformity by introducing a thin HfOx interfacial layer as a diffusion-limiting layer sandwiched between WOx and ITO bottom electrodes. An optimized HfOx thickness not only provides the best switching properties but also shows superior synaptic properties. The optimized 15 nm thin WOx layer can retain the memristor's excellence in P/D linearity, a cycling stability of 494 epochs and image recognition up to 3 mm bending, making it suitable for flexible devices. The artificial synapse is capable of reversible short-term and long-term learning behaviors confirmed by spike-timing-dependent-plasticity (STDP) results. X-ray photoelectron spectroscopy confirms the device composition and provides the oxygen vacancy concentration at the WOx/HfOx interface to realize the switching mechanism. The thicknesses of the different layers are estimated from the high-resolution transmission electron microscopy observations. The fabricated device exhibits 92.2% transparency, as confirmed by the UV-Vis spectrum.
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Affiliation(s)
- Debashis Panda
- Department of Electronics and Communication Engineering, C.V. Raman Global University, Odisha 752054, India.
| | - Yu-Fong Hui
- Institute of Electronics, National Yang-Ming Chiao Tung University, Hsinchu 30010, Taiwan, Republic of China
| | - Tseung-Yuen Tseng
- Institute of Electronics, National Yang-Ming Chiao Tung University, Hsinchu 30010, Taiwan, Republic of China
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2
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Kim DH, Cheong WH, Song H, Jeon JB, Kim G, Kim KM. Memristive Monte Carlo DropConnect crossbar array enabled by device and algorithm co-design. MATERIALS HORIZONS 2024; 11:4094-4103. [PMID: 38916265 DOI: 10.1039/d3mh02049e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Device and algorithm co-design aims to develop energy-efficient hardware that directly implements complex algorithms and optimizes algorithms to match the hardware's characteristics. Specifically, neuromorphic computing algorithms are constantly growing in complexity, necessitating an ongoing search for hardware implementations capable of handling these intricate algorithms. Here, we present a memristive Monte Carlo DropConnect (MC-DC) crossbar array developed through a hardware algorithm co-design approach. To implement the MC-DC neural network, stochastic switching and analog memory characteristics are required, and we achieved them using Ag-based diffusive selectors and Ru-based electrochemical metalization (ECM) memristors, respectively. The devices were integrated with a one-selector one-memristor (1S1M) structure, and their well-matched operating voltages and currents enabled stochastic readout and deterministic analog programming. With the integrated hardware, we successfully demonstrated the MC-DC operation. Additionally, the selector allowed for the control of switching polarity, and by understanding this hardware characteristic, we were able to modify the algorithm to fit it and further improve the network performance.
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Affiliation(s)
- Do Hoon Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Woon Hyung Cheong
- Applied Science Research Institute, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Hanchan Song
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Jae Bum Jeon
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Geunyoung Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Kyung Min Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
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3
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Wang Y, Wang H, Guo D, An Z, Zheng J, Huang R, Bi A, Jiang J, Wang S. High-Linearity Ta 2O 5 Memristor and Its Application in Gaussian Convolution Image Denoising. ACS APPLIED MATERIALS & INTERFACES 2024. [PMID: 39188162 DOI: 10.1021/acsami.4c09056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/28/2024]
Abstract
In the image Gaussian filtering process, convolving with a Gaussian matrix is essential due to the numerous arithmetic computations involved, predominantly multiplications and additions. This can heavily tax the system's memory, particularly with frequent use. To address this issue, a W/Ta2O5/Ag memristor was employed to substantially mitigate the computational overhead associated with convolution operations. Additionally, an interlayer of ZnO was subsequently introduced into the memristor. The resulting Ta2O5/ZnO heterostructure layer exhibited improved linearity in the pulse response, which enhanced linearity facilitates easy adjustment of the conductance magnitude through a linear mapping of the number of pulses and the conductance. Subsequently, the conductance of the W/Ta2O5/ZnO/Ag bilayer memristor was employed as the weights for the convolution kernel in convolution operations. Gaussian noise removal in image processing was achieved by assembling a 5 × 5 memristor array as the kernel. When denoising was performed using memristor arrays, compared to denoising achieved through Gaussian matrix convolution, an average loss of less than 5% was observed. The provided memristors demonstrate significant potential in convolutional computations, particularly for subsequent applications in convolutional neural networks (CNNs).
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Affiliation(s)
- Yucheng Wang
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China
| | - Hexin Wang
- School of Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Dingyun Guo
- School of Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Zeyang An
- School of Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Jiawei Zheng
- School of Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Ruixi Huang
- School of Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Antong Bi
- School of Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Junyu Jiang
- School of Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Shaoxi Wang
- School of Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China
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Malchow K, Zellweger T, Cheng B, Leray A, Leuthold J, Bouhelier A. Self-Induced Light Emission in Solid-State Memristors Replicates Neuronal Biophotons. ACS NANO 2024. [PMID: 39175442 DOI: 10.1021/acsnano.4c02924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2024]
Abstract
Key neuronal functions have been successfully replicated in various hardware systems. Noticeable examples are neuronal networks constructed from memristors, which emulate complex electrochemical biological dynamics such as the efficacy and plasticity of a neuron. Neurons are highly active cells, communicating with chemical and electrical stimuli, but also emit light. These so-called biophotons are suspected to be a complementary vehicle to transport information across the brain. Here, we show that a memristor also releases photons during its operation akin to the production of neuronal light. Critical attributes of biophotons, such as self-generation, stochasticity, spectral coverage, sparsity, and correlation with the neuron's electrical activity, are replicated by our solid-state approach. Importantly, our time-resolved analysis of the correlated current transport and photon activity shows that emission takes place within a nanometer-sized active area and relies on electrically induced single-to-few active electroluminescent centers excited with moderate voltage (<3 V). Our findings further extend the emulating capability of a memristor to encompass neuronal optical activity and allow to construct memristive atomic-scale devices capable of handling simultaneously electrons and photons as information carriers.
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Affiliation(s)
- Konstantin Malchow
- Laboratoire Interdisciplinaire Carnot de Bourgogne CNRS UMR 6303, Université de Bourgogne, 21000 Dijon, France
| | - Till Zellweger
- Integrated Systems Laboratory, ETH Zurich, 8092 Zurich, Switzerland
| | - Bojun Cheng
- Microelectronics Thrust, Function Hub, Hong Kong University of Science and Technology, Guangzhou 510530, China
| | - Aymeric Leray
- Laboratoire Interdisciplinaire Carnot de Bourgogne CNRS UMR 6303, Université de Bourgogne, 21000 Dijon, France
| | - Juerg Leuthold
- Institute of Electromagnetic Fields (IEF), ETH Zurich, 8092 Zurich, Switzerland
| | - Alexandre Bouhelier
- Laboratoire Interdisciplinaire Carnot de Bourgogne CNRS UMR 6303, Université de Bourgogne, 21000 Dijon, France
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Guo X, Li X, Wang R, Zhu W, Wang L, Zhang L. Building a depletion-region width modulation model and realizing memory characteristics in PN heterostructure devices. NANOSCALE 2024; 16:15722-15729. [PMID: 39104187 DOI: 10.1039/d4nr01666a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/07/2024]
Abstract
Memristive systems have potential applications in nonvolatile memories and even unexplored functionalities in electronics. However, progress has been delayed by difficulties in the controllability of memory behaviors and the dependence on material conductivity. Considering this, a new depletion-region width modulation model is proposed to realize and explain memory characteristics. The coexistence of memristive and memcapacitive behaviors is demonstrated in p-CuAlO2/n-ZnO, p+-Si/n-ZnO and p-NiO/n-ZnO heterostructure devices. A high external electric field induces the migration of oxygen ions and electrons/holes between the p-type and n-type semiconductor layers. It can regulate the oxygen vacancy concentration of the n-type side and cation vacancy concentration of the p-type side, changing the depletion-region width and modulating device conductivity and capacitance. Several essential synaptic functions were accurately imitated, including spike-timing-dependent plasticity (STDP) and "learning-experience" behaviors. This work provides new opportunities in fabricating a memristor and memcapacitor based on a PN heterostructure for synaptic simulation.
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Affiliation(s)
- Xing Guo
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha, 410000, China.
| | - Xinmiao Li
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha, 410000, China.
| | - Ruixiao Wang
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha, 410000, China.
| | - Wenhui Zhu
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha, 410000, China.
| | - Liancheng Wang
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha, 410000, China.
| | - Lei Zhang
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha, 410000, China.
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Zhang Y, Zhang W, Wang S, Lin N, Yu Y, He Y, Wang B, Jiang H, Lin P, Xu X, Qi X, Wang Z, Zhang X, Shang D, Liu Q, Cheng KT, Liu M. Semantic memory-based dynamic neural network using memristive ternary CIM and CAM for 2D and 3D vision. SCIENCE ADVANCES 2024; 10:eado1058. [PMID: 39141720 PMCID: PMC11323881 DOI: 10.1126/sciadv.ado1058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 07/08/2024] [Indexed: 08/16/2024]
Abstract
The brain is dynamic, associative, and efficient. It reconfigures by associating the inputs with past experiences, with fused memory and processing. In contrast, AI models are static, unable to associate inputs with past experiences, and run on digital computers with physically separated memory and processing. We propose a hardware-software co-design, a semantic memory-based dynamic neural network using a memristor. The network associates incoming data with the past experience stored as semantic vectors. The network and the semantic memory are physically implemented on noise-robust ternary memristor-based computing-in-memory (CIM) and content-addressable memory (CAM) circuits, respectively. We validate our co-designs, using a 40-nm memristor macro, on ResNet and PointNet++ for classifying images and three-dimensional points from the MNIST and ModelNet datasets, which achieves not only accuracy on par with software but also a 48.1 and 15.9% reduction in computational budget. Moreover, it delivers a 77.6 and 93.3% reduction in energy consumption.
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Affiliation(s)
- Yue Zhang
- Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China
- ACCESS - AI Chip Center for Emerging Smart Systems, InnoHK Centers, Hong Kong Science Park, Hong Kong, China
- Institute of the Mind, the University of Hong Kong, Hong Kong, China
| | - Woyu Zhang
- Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100049, China
- Key Lab of Fabrication Technologies for Integrated Circuits, Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shaocong Wang
- Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China
- ACCESS - AI Chip Center for Emerging Smart Systems, InnoHK Centers, Hong Kong Science Park, Hong Kong, China
- Institute of the Mind, the University of Hong Kong, Hong Kong, China
| | - Ning Lin
- Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China
- ACCESS - AI Chip Center for Emerging Smart Systems, InnoHK Centers, Hong Kong Science Park, Hong Kong, China
- Institute of the Mind, the University of Hong Kong, Hong Kong, China
| | - Yifei Yu
- Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China
- ACCESS - AI Chip Center for Emerging Smart Systems, InnoHK Centers, Hong Kong Science Park, Hong Kong, China
- Institute of the Mind, the University of Hong Kong, Hong Kong, China
| | - Yangu He
- Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China
- ACCESS - AI Chip Center for Emerging Smart Systems, InnoHK Centers, Hong Kong Science Park, Hong Kong, China
- Institute of the Mind, the University of Hong Kong, Hong Kong, China
| | - Bo Wang
- Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China
- ACCESS - AI Chip Center for Emerging Smart Systems, InnoHK Centers, Hong Kong Science Park, Hong Kong, China
- Institute of the Mind, the University of Hong Kong, Hong Kong, China
| | - Hao Jiang
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
| | - Peng Lin
- College of Computer Science and Technology, Zhejiang University, Zhejiang 310027, China
| | - Xiaoxin Xu
- Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100049, China
- Key Lab of Fabrication Technologies for Integrated Circuits, Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaojuan Qi
- Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China
| | - Zhongrui Wang
- Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China
- ACCESS - AI Chip Center for Emerging Smart Systems, InnoHK Centers, Hong Kong Science Park, Hong Kong, China
- Institute of the Mind, the University of Hong Kong, Hong Kong, China
| | - Xumeng Zhang
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
| | - Dashan Shang
- Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100049, China
- Key Lab of Fabrication Technologies for Integrated Circuits, Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qi Liu
- Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100049, China
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
| | - Kwang-Ting Cheng
- ACCESS - AI Chip Center for Emerging Smart Systems, InnoHK Centers, Hong Kong Science Park, Hong Kong, China
- Department of Electronic and Computer Engineering, the Hong Kong University of Science and Technology, Hong Kong, China
| | - Ming Liu
- Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100049, China
- State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
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Hu L, Li Z, Shao J, Cheng P, Wang J, Vasilakos AV, Zhang L, Chai Y, Ye Z, Zhuge F. Electronically Reconfigurable Memristive Neuron Capable of Operating in Both Excitation and Inhibition Modes. NANO LETTERS 2024. [PMID: 39142648 DOI: 10.1021/acs.nanolett.4c02470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Threshold switching (TS) memristors are promising candidates for artificial neurons in neuromorphic systems. However, they often lack biological plausibility, typically functioning solely in an excitation mode. The absence of an inhibitory mode limits neurons' ability to synergistically process both excitatory and inhibitory synaptic signals. To address this limitation, we propose a novel memristive neuron capable of operating in both excitation and inhibition modes. The memristor's threshold voltage can be reversibly tuned using voltages of different polarities because of its bipolar TS behavior, enabling the device to function as an electronically reconfigurable bi-mode neuron. A variety of neuronal activities such as all-or-nothing behavior and tunable firing probability are mimicked under both excitatory and inhibitory stimuli. Furthermore, we develop a self-adaptive neuromorphic vision sensor based on bi-mode neurons, demonstrating effective object recognition in varied lighting conditions. Thus, our bi-mode neuron offers a versatile platform for constructing neuromorphic systems with rich functionality.
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Affiliation(s)
- Lingxiang Hu
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Zongxiao Li
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Jiale Shao
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Peihong Cheng
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo 315211, China
| | - Jingrui Wang
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo 315211, China
| | | | - Li Zhang
- Healthcare Engineering Centre, School of Engineering, Temasek Polytechnic, Tampines Avenue, 529757, Singapore
| | - Yang Chai
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Zhizhen Ye
- Institute of Wenzhou, Zhejiang University, Wenzhou 325006, China
- State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Fei Zhuge
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Institute of Wenzhou, Zhejiang University, Wenzhou 325006, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200072, China
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Verma RS, Raj RK, Verma G, Kaushik BK. Energy-efficient synthetic antiferromagnetic skyrmion-based artificial neuronal device. NANOTECHNOLOGY 2024; 35:435401. [PMID: 39084230 DOI: 10.1088/1361-6528/ad6997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 07/31/2024] [Indexed: 08/02/2024]
Abstract
Magnetic skyrmions offer unique characteristics such as nanoscale size, particle-like behavior, topological stability, and low depinning current density. These properties make them promising candidates for next-generation spintronics-based memory and neuromorphic computing. However, one of their distinctive features is their tendency to deviate from the direction of the applied driving force that may lead to the skyrmion annihilation at the edge of nanotrack during skyrmion motion, known as the skyrmion Hall effect (SkHE). To overcome this problem, synthetic antiferromagnetic (SAF) skyrmions that having bilayer coupling effect allows them to follow a straight path by nullifying SkHE making them alternative for ferromagnetic (FM) counterpart. This study proposes an integrate-and-fire (IF) artificial neuron model based on SAF skyrmions with asymmetric wedge-shaped nanotrack having self-sustainability of skyrmion numbers at the device window. The model leverages inter-skyrmion repulsion to replicate the IF mechanism of biological neuron. The device threshold, determined by the maximum number of pinned skyrmions at the device window, can be adjusted by tuning the current density applied to the nanotrack. Neuronal spikes occur when initial skyrmion reaches the detection unit after surpassing the device window by the accumulation of repulsive force that result in reduction of the device's contriving current results to design of high energy efficient for neuromorphic computing. Furthermore, work implements a binarized neuronal network accelerator using proposed IF neuron and SAF-SOT-MRAM based synaptic devices for national institute of standards and technology database image classification. The presented approach achieves significantly higher energy efficiency compared to existing technologies like SRAM and STT-MRAM, with improvements of 2.31x and 1.36x, respectively. The presented accelerator achieves 1.42x and 1.07x higher throughput efficiency per Watt as compared to conventional SRAM and STT-MRAM based designs.
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Affiliation(s)
- Ravi Shankar Verma
- Department of Electronics and Communication Engineering, Indian Institute of Technology, Roorkee 247667, India
| | - Ravish Kumar Raj
- Department of Electronics and Communication Engineering, Indian Institute of Technology, Roorkee 247667, India
| | - Gaurav Verma
- Department of Electronics and Communication Engineering, Indian Institute of Technology, Roorkee 247667, India
| | - Brajesh Kumar Kaushik
- Department of Electronics and Communication Engineering, Indian Institute of Technology, Roorkee 247667, India
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Ko J, Kim D, Nguyen QH, Lee C, Kim N, Lee H, Eo J, Kwon JE, Jeon SY, Jang BC, Im SG, Joo Y. A nonconjugated radical polymer enables bimodal memory and in-sensor computing operation. SCIENCE ADVANCES 2024; 10:eadp0778. [PMID: 39121228 PMCID: PMC11313951 DOI: 10.1126/sciadv.adp0778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 07/08/2024] [Indexed: 08/11/2024]
Abstract
This study reports intrinsic multimodal memristivity of a nonconjugated radical polymer with ambient stability. Organic memristive devices represent powerful candidates for biorealistic data storage and processing. However, there exists a substantial knowledge gap in realizing the synthetic biorealistic systems capable of effectively emulating the cooperative and multimodal activation processes in biological systems. In addition, conventional organic memristive materials are centered on conjugated small and macromolecules, making them synthetically challenging or difficult to process. In this work, we first describe the intrinsic resistive switching of the radical polymer that resulted in an exceptional state retention of >105 s and on/off ratio of >106. Next, we demonstrate its bimodal cooperative switching, in response to the proton accumulation as a biological input. Last, we expand our system toward an advanced in-sensor computing system. Our research demonstrates a nonconjugated radical polymer with intrinsic memristivity, which is directly applicable to future electronics including data storage, neuromorphics, and in-sensor computing.
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Affiliation(s)
- Jaehyoung Ko
- Institute of Advanced Composite Materials, Korea Institute of Science and Technology (KIST), Wanju-gun, Jeonbuk 55324, Republic of Korea
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Daeun Kim
- Institute of Advanced Composite Materials, Korea Institute of Science and Technology (KIST), Wanju-gun, Jeonbuk 55324, Republic of Korea
- School of Materials Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - Quynh H. Nguyen
- Institute of Advanced Composite Materials, Korea Institute of Science and Technology (KIST), Wanju-gun, Jeonbuk 55324, Republic of Korea
| | - Changhyeon Lee
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Namju Kim
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Hoyeon Lee
- Institute of Advanced Composite Materials, Korea Institute of Science and Technology (KIST), Wanju-gun, Jeonbuk 55324, Republic of Korea
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Joohwan Eo
- Institute of Advanced Composite Materials, Korea Institute of Science and Technology (KIST), Wanju-gun, Jeonbuk 55324, Republic of Korea
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Ji Eon Kwon
- Institute of Advanced Composite Materials, Korea Institute of Science and Technology (KIST), Wanju-gun, Jeonbuk 55324, Republic of Korea
- Department of JBNU-KIST Industry Academia Convergence Research, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju, Jeonbuk 54896, Republic of Korea
| | - Seung-Yeol Jeon
- Institute of Advanced Composite Materials, Korea Institute of Science and Technology (KIST), Wanju-gun, Jeonbuk 55324, Republic of Korea
| | - Byung Chul Jang
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Sung Gap Im
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Yongho Joo
- Institute of Advanced Composite Materials, Korea Institute of Science and Technology (KIST), Wanju-gun, Jeonbuk 55324, Republic of Korea
- Division of Nano and Information Technology, KIST School, Korea University of Science and Technology (UST), Jeonbuk 55324, Republic of Korea
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Zhao X, Chen LW, Li K, Schmidt H, Polian I, Du N. Memristive True Random Number Generator for Security Applications. SENSORS (BASEL, SWITZERLAND) 2024; 24:5001. [PMID: 39124048 PMCID: PMC11314823 DOI: 10.3390/s24155001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 07/09/2024] [Accepted: 07/31/2024] [Indexed: 08/12/2024]
Abstract
This study explores memristor-based true random number generators (TRNGs) through their evolution and optimization, stemming from the concept of memristors first introduced by Leon Chua in 1971 and realized in 2008. We will consider memristor TRNGs coming from various entropy sources for producing high-quality random numbers. However, we must take into account both their strengths and weaknesses. The comparison with CMOS-based TRNGs will serve as an illustration that memristor TRNGs stand out due to their simpler circuits and lower power consumption- thus leading us into a case study involving electroless YMnO3 (YMO) memristors as TRNG entropy sources that demonstrate good security properties by being able to produce unpredictable random numbers effectively. The end of our analysis sees us pinpointing challenges: post-processing algorithm optimization coupled with ensuring reliability over time for memristor-based TRNGs aimed at next-generation security applications.
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Affiliation(s)
- Xianyue Zhao
- Institute for Solid State Physics, Friedrich Schiller University Jena, 07743 Jena, Germany; (X.Z.); (K.L.); (H.S.)
- Department of Quantum Detection, Leibniz Institute of Photonic Technology (IPHT), 07745 Jena, Germany
| | - Li-Wei Chen
- Institute of Computer Science and Computer Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (L.-W.C.); (I.P.)
| | - Kefeng Li
- Institute for Solid State Physics, Friedrich Schiller University Jena, 07743 Jena, Germany; (X.Z.); (K.L.); (H.S.)
- Department of Quantum Detection, Leibniz Institute of Photonic Technology (IPHT), 07745 Jena, Germany
| | - Heidemarie Schmidt
- Institute for Solid State Physics, Friedrich Schiller University Jena, 07743 Jena, Germany; (X.Z.); (K.L.); (H.S.)
- Department of Quantum Detection, Leibniz Institute of Photonic Technology (IPHT), 07745 Jena, Germany
| | - Ilia Polian
- Institute of Computer Science and Computer Engineering, University of Stuttgart, 70569 Stuttgart, Germany; (L.-W.C.); (I.P.)
| | - Nan Du
- Institute for Solid State Physics, Friedrich Schiller University Jena, 07743 Jena, Germany; (X.Z.); (K.L.); (H.S.)
- Department of Quantum Detection, Leibniz Institute of Photonic Technology (IPHT), 07745 Jena, Germany
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11
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Qin S, Zhu H, Ren Z, Zhai Y, Wang Y, Liu M, Lai W, Rahimi-Iman A, Zhao S, Wu H. Floating-gate memristor based on a MoS 2/h-BN/AuNPs mixed-dimensional heterostructure. NANOTECHNOLOGY 2024; 35:425202. [PMID: 38941985 DOI: 10.1088/1361-6528/ad5cfc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 06/28/2024] [Indexed: 06/30/2024]
Abstract
Memristors have recently received substantial attention because of their promising and unique emerging applications in neuromorphic computing, which can achieve gains in computation speed by mimicking the topology of the brain in electronic circuits. Traditional memristors made of bulk MoO3and HfO2, for example, suffer from a low switching ratio and poor durability and stability. In this work, a floating-gate memristor is developed based on a mixed-dimensional heterostructure comprising two-dimensional (2D) molybdenum disulfide (MoS2) and zero-dimensional (0D) Au nanoparticles (AuNPs) separated by an insulating hexagonal boron nitride (h-BN) layer (MoS2/h-BN/AuNPs). We find that under the modulation of back-gate voltages, the MoS2/h-BN/AuNPs device operates reliably between a high-resistance state (HRS) and a low-resistance state (LRS) and shows multiple stable LRS states, demonstrating the excellent potential of our memristor in multibit storage applications. The modulation effect can be attributed to electron quantum tunneling between the AuNP charge-trapping layer and the MoS2channel. Our memristor exhibits excellent durability and stability: the HRS and LRS are retained for more than 104s without obvious degradation and the on/off ratio is >104after more than 3000 switching cycles. We also demonstrate frequency-dependent memory properties upon stimulation with electrical and optical pulses.
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Affiliation(s)
- Shirong Qin
- Zhejiang Province Key Laboratory of Quantum Technology and Devices, School of Physics, and State Key Laboratory of Silicon and Advanced Semiconductor Materials, Zhejiang University, Hangzhou 310058, People's Republic of China
| | - Haiming Zhu
- Zhejiang Province Key Laboratory of Quantum Technology and Devices, School of Physics, and State Key Laboratory of Silicon and Advanced Semiconductor Materials, Zhejiang University, Hangzhou 310058, People's Republic of China
| | - Ziyang Ren
- Zhejiang Province Key Laboratory of Quantum Technology and Devices, School of Physics, and State Key Laboratory of Silicon and Advanced Semiconductor Materials, Zhejiang University, Hangzhou 310058, People's Republic of China
| | - Yihui Zhai
- Zhejiang Province Key Laboratory of Quantum Technology and Devices, School of Physics, and State Key Laboratory of Silicon and Advanced Semiconductor Materials, Zhejiang University, Hangzhou 310058, People's Republic of China
| | - Yao Wang
- Zhejiang Province Key Laboratory of Quantum Technology and Devices, School of Physics, and State Key Laboratory of Silicon and Advanced Semiconductor Materials, Zhejiang University, Hangzhou 310058, People's Republic of China
| | - Mengjuan Liu
- Zhejiang Province Key Laboratory of Quantum Technology and Devices, School of Physics, and State Key Laboratory of Silicon and Advanced Semiconductor Materials, Zhejiang University, Hangzhou 310058, People's Republic of China
| | - Weien Lai
- National Engineering Laboratory of Special Display Technology, National Key Laboratory of Advanced Display Technology, Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, Academy of Opto-Electronic Technology, Hefei University of Technology, Hefei 230009, People's Republic of China
| | - Arash Rahimi-Iman
- Physics Institute, Justus Liebig University, Heinrich-Buff-Ring 16, D-35392 Giessen, Germany
| | - Sihan Zhao
- Zhejiang Province Key Laboratory of Quantum Technology and Devices, School of Physics, and State Key Laboratory of Silicon and Advanced Semiconductor Materials, Zhejiang University, Hangzhou 310058, People's Republic of China
| | - Huizhen Wu
- Zhejiang Province Key Laboratory of Quantum Technology and Devices, School of Physics, and State Key Laboratory of Silicon and Advanced Semiconductor Materials, Zhejiang University, Hangzhou 310058, People's Republic of China
- Research Center for Sensing Materials and Devices, Zhejiang Lab, Hangzhou, Zhejiang 311121, People's Republic of China
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12
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Youn S, Hwang Y, Kim TH, Kim S, Hwang H, Park J, Kim H. Threshold learning algorithm for memristive neural network with binary switching behavior. Neural Netw 2024; 176:106355. [PMID: 38759411 DOI: 10.1016/j.neunet.2024.106355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 02/04/2024] [Accepted: 04/29/2024] [Indexed: 05/19/2024]
Abstract
On-chip learning is an effective method for adjusting artificial neural networks in neuromorphic computing systems by considering hardware intrinsic properties. However, it faces challenges due to hardware nonidealities, such as the nonlinearity of potentiation and depression and limitations on fine weight adjustment. In this study, we propose a threshold learning algorithm for a variation-tolerant ternary neural network in a memristor crossbar array. This algorithm utilizes two tightly separated resistance states in memristive devices to represent weight values. The high-resistance state (HRS) and low-resistance state (LRS) defined as read current of < 0.1 μA and > 1 μA, respectively, were successfully programmed in a 32 × 32 crossbar array, and exhibited half-normal distributions due to the programming method. To validate our approach experimentally, a 64 × 10 single-layer fully connected network were trained in the fabricated crossbar for an 8 × 8 MNIST dataset using the threshold learning algorithm, where the weight value is updated when a gradient determined by backpropagation exceeds a threshold value. Thanks to the large margin between the two states of the memristor, we observed only a 0.42 % drop in classification accuracy compared to the baseline network results. The threshold learning algorithm is expected to alleviate the programming burden and be utilized in variation-tolerant neuromorphic architectures.
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Affiliation(s)
- Sangwook Youn
- Division of Materials Science and Engineering, Seoul 04763, Korea
| | - Yeongjin Hwang
- Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Korea
| | - Tae-Hyeon Kim
- Department of Semiconductor Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
| | - Sungjoon Kim
- Department of AI Semiconductor Engineering, Korea University, Sejong 30019, Korea
| | - Hwiho Hwang
- Division of Materials Science and Engineering, Seoul 04763, Korea
| | - Jinwoo Park
- Division of Materials Science and Engineering, Seoul 04763, Korea
| | - Hyungjin Kim
- Division of Materials Science and Engineering, Seoul 04763, Korea.
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13
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Li S, Gao L, Liu C, Guo H, Yu J. Biomimetic Neuromorphic Sensory System via Electrolyte Gated Transistors. SENSORS (BASEL, SWITZERLAND) 2024; 24:4915. [PMID: 39123962 PMCID: PMC11314768 DOI: 10.3390/s24154915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Revised: 07/26/2024] [Accepted: 07/27/2024] [Indexed: 08/12/2024]
Abstract
Biomimetic neuromorphic sensing systems, inspired by the structure and function of biological neural networks, represent a major advancement in the field of sensing technology and artificial intelligence. This review paper focuses on the development and application of electrolyte gated transistors (EGTs) as the core components (synapses and neuros) of these neuromorphic systems. EGTs offer unique advantages, including low operating voltage, high transconductance, and biocompatibility, making them ideal for integrating with sensors, interfacing with biological tissues, and mimicking neural processes. Major advances in the use of EGTs for neuromorphic sensory applications such as tactile sensors, visual neuromorphic systems, chemical neuromorphic systems, and multimode neuromorphic systems are carefully discussed. Furthermore, the challenges and future directions of the field are explored, highlighting the potential of EGT-based biomimetic systems to revolutionize neuromorphic prosthetics, robotics, and human-machine interfaces. Through a comprehensive analysis of the latest research, this review is intended to provide a detailed understanding of the current status and future prospects of biomimetic neuromorphic sensory systems via EGT sensing and integrated technologies.
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Affiliation(s)
| | | | | | | | - Junsheng Yu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
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14
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van Doremaele ERW, Stevens T, Ringeling S, Spolaor S, Fattori M, van de Burgt Y. Hardware implementation of backpropagation using progressive gradient descent for in situ training of multilayer neural networks. SCIENCE ADVANCES 2024; 10:eado8999. [PMID: 38996020 PMCID: PMC11244533 DOI: 10.1126/sciadv.ado8999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 06/07/2024] [Indexed: 07/14/2024]
Abstract
Neural network training can be slow and energy-expensive due to the frequent transfer of weight data between digital memory and processing units. Neuromorphic systems can accelerate neural networks by performing multiply-accumulate operations in parallel using nonvolatile analog memory. However, executing the widely used backpropagation training algorithm in multilayer neural networks requires information-and therefore storage-of the partial derivatives of the weight values preventing suitable and scalable implementation in hardware. Here, we propose a hardware implementation of the backpropagation algorithm that progressively updates each layer using in situ stochastic gradient descent, avoiding this storage requirement. We experimentally demonstrate the in situ error calculation and the proposed progressive backpropagation method in a multilayer hardware-implemented neural network. We confirm identical learning characteristics and classification performance compared to conventional backpropagation in software. We show that our approach can be scaled to large and deep neural networks, enabling highly efficient training of advanced artificial intelligence computing systems.
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Affiliation(s)
- Eveline R. W. van Doremaele
- Department of Mechanical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5612AP, Netherlands
- Eindhoven Hendrik Casimir Institute, Eindhoven University of Technology, Eindhoven 5612AP, Netherlands
| | - Tim Stevens
- Department of Mechanical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5612AP, Netherlands
- Eindhoven Hendrik Casimir Institute, Eindhoven University of Technology, Eindhoven 5612AP, Netherlands
| | - Stijn Ringeling
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5612AP, Netherlands
| | - Simone Spolaor
- Department of Mechanical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5612AP, Netherlands
| | - Marco Fattori
- Eindhoven Hendrik Casimir Institute, Eindhoven University of Technology, Eindhoven 5612AP, Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5612AP, Netherlands
| | - Yoeri van de Burgt
- Department of Mechanical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5612AP, Netherlands
- Eindhoven Hendrik Casimir Institute, Eindhoven University of Technology, Eindhoven 5612AP, Netherlands
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15
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Yin H, Pang J, Zhao S, Wu H, Song Z, Li X, Zheng Z, Xu L, Tang J, Niu G. Retinomorphic X-ray detection using perovskite with hydrion-conductive organic cations. Innovation (N Y) 2024; 5:100654. [PMID: 39021527 PMCID: PMC11253655 DOI: 10.1016/j.xinn.2024.100654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 06/03/2024] [Indexed: 07/20/2024] Open
Abstract
X-ray detection is crucial across various sectors, but traditional techniques face challenges such as inefficient data transmission, redundant sensing, high power consumption, and complexity. The innovative idea of a retinomorphic X-ray detector shows great potential. However, its implementation has been hindered by the absence of active layers capable of both detecting X-rays and serving as memory storage. In response to this critical gap, our study integrates hybrid perovskite with hydrion-conductive organic cations to develop a groundbreaking retinomorphic X-ray detector. This novel device stands at the nexus of technological innovation, utilizing X-ray detection, memory, and preprocessing capabilities within a single hardware platform. The core mechanism underlying this innovation lies in the transport of electrons and holes within the metal halide octahedral frameworks, enabling precise X-ray detection. Concurrently, the hydrion movement through organic cations endows the device with short-term resistive memory, facilitating rapid data processing and retrieval. Notably, our retinomorphic X-ray detector boasts an array of formidable features, including reconfigurable short-term memory, a linear response curve, and an extended retention time. In practical terms, this translates into the efficient capture of motion projections with minimal redundant data, achieving a compression ratio of 18.06% and an impressive recognition accuracy of up to 98.6%. In essence, our prototype represents a paradigm shift in X-ray detection technology. With its transformative capabilities, this retinomorphic hardware is poised to revolutionize the existing X-ray detection landscape.
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Affiliation(s)
- Hang Yin
- Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jincong Pang
- Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Shan Zhao
- Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Haodi Wu
- Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Zihao Song
- Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xing Li
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China
| | - Zhiping Zheng
- Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
- Optical Valley Laboratory, Wuhan 430074, China
| | - Ling Xu
- Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
- Optical Valley Laboratory, Wuhan 430074, China
| | - Jiang Tang
- Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
- Optical Valley Laboratory, Wuhan 430074, China
| | - Guangda Niu
- Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
- Optical Valley Laboratory, Wuhan 430074, China
- Research Institute of Huazhong University of science and technology in Shenzhen, Shenzhen 518057, China
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16
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Kim J, Im J, Shin W, Lee S, Oh S, Kwon D, Jung G, Choi WY, Lee J. Demonstration of In-Memory Biosignal Analysis: Novel High-Density and Low-Power 3D Flash Memory Array for Arrhythmia Detection. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308460. [PMID: 38709909 PMCID: PMC11234417 DOI: 10.1002/advs.202308460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 02/08/2024] [Indexed: 05/08/2024]
Abstract
Smart healthcare systems integrated with advanced deep neural networks enable real-time health monitoring, early disease detection, and personalized treatment. In this work, a novel 3D AND-type flash memory array with a rounded double channel for computing-in-memory (CIM) architecture to overcome the limitations of conventional smart healthcare systems: the necessity of high area and energy efficiency while maintaining high classification accuracy is proposed. The fabricated array, characterized by low-power operations and high scalability with double independent channels per floor, exhibits enhanced cell density and energy efficiency while effectively emulating the features of biological synapses. The CIM architecture leveraging the fabricated array achieves high classification accuracy (93.5%) for electrocardiogram signals, ensuring timely detection of potentially life-threatening arrhythmias. Incorporated with a simplified spike-timing-dependent plasticity learning rule, the CIM architecture is suitable for robust, area- and energy-efficient in-memory arrhythmia detection systems. This work effectively addresses the challenges of conventional smart healthcare systems, paving the way for a more refined healthcare paradigm.
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Affiliation(s)
- Jangsaeng Kim
- Department of Electrical and Computer Engineering and Inter‐university Semiconductor Research CenterSeoul National UniversitySeoul08826Republic of Korea
| | - Jiseong Im
- Department of Electrical and Computer Engineering and Inter‐university Semiconductor Research CenterSeoul National UniversitySeoul08826Republic of Korea
| | - Wonjun Shin
- Department of Electrical and Computer Engineering and Inter‐university Semiconductor Research CenterSeoul National UniversitySeoul08826Republic of Korea
| | - Soochang Lee
- Department of Electrical and Computer Engineering and Inter‐university Semiconductor Research CenterSeoul National UniversitySeoul08826Republic of Korea
| | - Seongbin Oh
- Department of Electrical and Computer Engineering and Inter‐university Semiconductor Research CenterSeoul National UniversitySeoul08826Republic of Korea
| | - Dongseok Kwon
- Department of Electrical and Computer Engineering and Inter‐university Semiconductor Research CenterSeoul National UniversitySeoul08826Republic of Korea
| | - Gyuweon Jung
- Department of Electrical and Computer Engineering and Inter‐university Semiconductor Research CenterSeoul National UniversitySeoul08826Republic of Korea
| | - Woo Young Choi
- Department of Electrical and Computer Engineering and Inter‐university Semiconductor Research CenterSeoul National UniversitySeoul08826Republic of Korea
| | - Jong‐Ho Lee
- Department of Electrical and Computer Engineering and Inter‐university Semiconductor Research CenterSeoul National UniversitySeoul08826Republic of Korea
- Ministry of Science and ICTSejong30121Republic of Korea
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17
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Shi S, Zhao Y, Sun J, Yu G, Zhou H, Wang J. Strain-mediated multistate skyrmion for neuron devices. NANOSCALE 2024; 16:12013-12020. [PMID: 38805240 DOI: 10.1039/d4nr01464b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Magnetic skyrmions are potential candidates for neuromorphic computing because of their inherent topological stability, low drive current density and nanoscale size. However, an artificial neuron device based on current-driven skyrmion motion cannot satisfy the requirement of energy efficiency and integration density due to hundreds of millions of interconnected neurons and synapses present in the deep networks. Here, we present a compact and energy efficient skyrmion-based artificial neuron consisting of ferromagnetic/heavy metal/ferroelectric layers which uses strain-mediated voltage manipulation of skyrmion states to mimic the Integrate-and-Fire (IF) function of biological neurons. By implementation of a spiking neural network (SNN) based on the proposed skyrmionic neuronal devices, it can achieve a high accuracy of 95.08% on a modified National Institute of Standards and Technology (MNIST) handwritten digit dataset, as well as a low power consumption of ∼46.8 fJ per epoch per neuron. The present work suggests a novel way to realize energy-efficient and high-density neuromorphic computing.
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Affiliation(s)
- Shengbin Shi
- Department of Engineering Mechanics, Zhejiang University, Zheda Road 38, Hangzhou, Zhejiang 310027, China.
| | - Yunhong Zhao
- Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China
| | - Jiajun Sun
- Department of Engineering Mechanics, Zhejiang University, Zheda Road 38, Hangzhou, Zhejiang 310027, China.
| | - Guoliang Yu
- Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou 310018, People's Republic of China
| | - Haomiao Zhou
- Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou 310018, People's Republic of China
| | - Jie Wang
- Department of Engineering Mechanics, Zhejiang University, Zheda Road 38, Hangzhou, Zhejiang 310027, China.
- Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China
- Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province, Zhejiang University, Zheda Road 38, Hangzhou, Zhejiang 310027, China
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18
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Belleri P, Pons I Tarrés J, McCulloch I, Blom PWM, Kovács-Vajna ZM, Gkoupidenis P, Torricelli F. Unravelling the operation of organic artificial neurons for neuromorphic bioelectronics. Nat Commun 2024; 15:5350. [PMID: 38914568 PMCID: PMC11196688 DOI: 10.1038/s41467-024-49668-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 06/17/2024] [Indexed: 06/26/2024] Open
Abstract
Organic artificial neurons operating in liquid environments are crucial components in neuromorphic bioelectronics. However, the current understanding of these neurons is limited, hindering their rational design and development for realistic neuronal emulation in biological settings. Here we combine experiments, numerical non-linear simulations, and analytical tools to unravel the operation of organic artificial neurons. This comprehensive approach elucidates a broad spectrum of biorealistic behaviors, including firing properties, excitability, wetware operation, and biohybrid integration. The non-linear simulations are grounded in a physics-based framework, accounting for ion type and ion concentration in the electrolytic medium, organic mixed ionic-electronic parameters, and biomembrane features. The derived analytical expressions link the neurons spiking features with material and physical parameters, bridging closer the domains of artificial neurons and neuroscience. This work provides streamlined and transferable guidelines for the design, development, engineering, and optimization of organic artificial neurons, advancing next generation neuronal networks, neuromorphic electronics, and bioelectronics.
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Affiliation(s)
- Pietro Belleri
- Department of Information Engineering, University of Brescia, via Branze 38, 25123, Brescia, Italy
| | - Judith Pons I Tarrés
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany
| | - Iain McCulloch
- Department of Chemistry, University of Oxford, 12 Mansfield Road, Oxford, UK
| | - Paul W M Blom
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany
| | - Zsolt M Kovács-Vajna
- Department of Information Engineering, University of Brescia, via Branze 38, 25123, Brescia, Italy
| | - Paschalis Gkoupidenis
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany.
- Department of Electrical and Computer Engineering, North Carolina State University, 890 Oval Dr, Raleigh, NC, USA.
- Department of Physics, North Carolina State University, 2401 Stinson Dr, Raleigh, NC, USA.
| | - Fabrizio Torricelli
- Department of Information Engineering, University of Brescia, via Branze 38, 25123, Brescia, Italy.
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19
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Lai XC, Tang Z, Fang J, Feng L, Yao DJ, Zhang L, Jiang YP, Liu QX, Tang XG, Zhou YC, Shang J, Zhong GK, Gao J. An adjustable multistage resistance switching behavior of a photoelectric artificial synaptic device with a ferroelectric diode effect for neuromorphic computing. MATERIALS HORIZONS 2024; 11:2886-2897. [PMID: 38563639 DOI: 10.1039/d4mh00064a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Neuromorphic computing, which mimics biological neural networks, is widely regarded as the optimal solution for addressing the limitations of traditional von Neumann computing architecture. In this work, an adjustable multistage resistance switching ferroelectric Bi2FeCrO6 diode artificial synaptic device was fabricated using a sol-gel method with a simple process. The device exhibits nonlinearity in its electrical characteristics, demonstrating tunable multistage resistance switching behavior and a strong ferroelectric diode effect through the manipulation of ferroelectric polarization. One of its salient advantages resides in its capacity to dynamically regulate its polarization state in response to an external electric field, thereby facilitating the fine-tuning of synaptic connection strength while maintaining synaptic stability. The device is capable of accurately simulating the fundamental properties of biological synapses, including long/short-term plasticity, paired-pulse facilitation, and spike-timing-dependent plasticity. Additionally, the device exhibits a distinctive photoelectric response and is capable of inducing synaptic plasticity by light signal activation. The utilization of a femtosecond laser for the scrutiny of carrier transport mechanisms imparts profound insights into the intricate dynamics governing the optical memory effect. Furthermore, utilizing a convolutional neural network (CNN) architecture, the recognition accuracy of the MNIST and fashion MNIST datasets was improved to 95.6% and 78%, respectively, through the implementation of improved random adaptive algorithms. These findings present a new opportunity for utilizing Bi2FeCrO6 materials in the development of artificial synapses for neuromorphic computation.
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Affiliation(s)
- Xi-Cai Lai
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Zhenhua Tang
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Junlin Fang
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Leyan Feng
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Di-Jie Yao
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Li Zhang
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Yan-Ping Jiang
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Qiu-Xiang Liu
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Xin-Gui Tang
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P. R. China.
| | - Yi-Chun Zhou
- School of Advanced Materials and Nanotechnology, Xidian University, Xian 710126, China
| | - Jie Shang
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Gao-Kuo Zhong
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ju Gao
- Department of Physics, The University of Hong Kong, Hong Kong 999077, P. R. China
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20
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Zhou Y, Li S, Liang X, Zhou Y. Topological Spin Textures: Basic Physics and Devices. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2312935. [PMID: 38861696 DOI: 10.1002/adma.202312935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 05/24/2024] [Indexed: 06/13/2024]
Abstract
In the face of escalating modern data storage demands and the constraints of Moore's Law, exploring spintronic solutions, particularly the devices based on magnetic skyrmions, has emerged as a promising frontier in scientific research. Since the first experimental observation of skyrmions, topological spin textures have been extensively studied for their great potential as efficient information carriers in spintronic devices. However, significant challenges have emerged alongside this progress. This review aims to synthesize recent advances in skyrmion research while addressing the major issues encountered in the field. Additionally, current research on promising topological spin structures in addition to skyrmions is summarized. Beyond 2D structures, exploration also extends to 1D magnetic solitons and 3D spin textures. In addition, a diverse array of emerging magnetic materials is introduced, including antiferromagnets and 2D van der Waals magnets, broadening the scope of potential materials hosting topological spin textures. Through a systematic examination of magnetic principles, topological categorization, and the dynamics of spin textures, a comprehensive overview of experimental and theoretical advances in the research of topological magnetism is provided. Finally, both conventional and unconventional applications are summarized based on spin textures proposed thus far. This review provides an outlook on future development in applied spintronics.
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Affiliation(s)
- Yuqing Zhou
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China
| | - Shuang Li
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China
| | - Xue Liang
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China
| | - Yan Zhou
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China
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21
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Yu Y, Ding Z, Ren Y, Wang X, Quan H, Jia H, Jiang C. Understanding the Resistive Switching Behaviors of Top Electrode (Au, Cu, and Al)-Dependent TiO 2-Based Memristive Devices. ACS OMEGA 2024; 9:24601-24609. [PMID: 38882132 PMCID: PMC11170736 DOI: 10.1021/acsomega.4c00320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 04/26/2024] [Accepted: 04/30/2024] [Indexed: 06/18/2024]
Abstract
Memristor-based neuromorphic computing is promising toward their potential application of handling complex parallel tasks in the period of big data. To implement brain-inspired applications of spiking neural networks, new physical architecture designs are needed. Here, a serial memristive structure (SMS) consisting of memristive devices with different top electrodes is proposed. Top electrodes Au, Cu, and Al are selected for nitrogen-doped TiO2 nanorod array-based memristive devices. The typical I-V cycles, retention, on/off ratio, and variations of cycle to cycle of top electrode-dependent memristive devices have been studied. Devices with Cu and Al electrodes exhibit a retention of over 104 s. And the resistance states of the device with the Al top electrode are reliable. Furthermore, the conductive mechanism underlining the I-V curves is discussed in detail. The interface-type mechanism and block conductance mechanism are illustrated, which are related to electron migration and ion/anion migration, respectively. Finally, the SMS has been constructed using memristive devices with Al and Cu top electrodes, which can mimic the spiking pulse-dependent plasticity of a synapse and a neuron body. The SMS provides a new approach to implement a fundamental physical unit for neuromorphic computing.
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Affiliation(s)
- Yantao Yu
- College of Physics and Electronic Information, Luoyang Normal University, Luoyang 471934, China
| | - Zizhao Ding
- Powder Metallurgy Research Institute, Central South University, Changsha 410083, China
| | - Yaoying Ren
- College of Physics and Electronic Information, Luoyang Normal University, Luoyang 471934, China
| | - Xiangfei Wang
- College of Physics and Electronic Information, Luoyang Normal University, Luoyang 471934, China
| | - Hongguang Quan
- College of Physics and Electronic Information, Luoyang Normal University, Luoyang 471934, China
| | - Hong Jia
- College of Physics and Electronic Information, Luoyang Normal University, Luoyang 471934, China
| | - Chao Jiang
- Powder Metallurgy Research Institute, Central South University, Changsha 410083, China
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22
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Acal C, Maldonado D, Cantudo A, González MB, Jiménez-Molinos F, Campabadal F, Roldán JB. Variability in HfO 2-based memristors described with a new bidimensional statistical technique. NANOSCALE 2024; 16:10812-10818. [PMID: 38766810 DOI: 10.1039/d4nr01237b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
A new statistical analysis is presented to assess cycle-to-cycle variability in resistive memories. This method employs two-dimensional (2D) distributions of parameters to analyse both set and reset voltages and currents, coupled with a 2D coefficient of variation (CV). This 2D methodology significantly enhances the analysis, providing a more thorough and comprehensive understanding of the data compared to conventional one-dimensional methods. Resistive switching (RS) data from two different technologies based on hafnium oxide are used in the variability study. The 2D CV allows a more compact assessment of technology suitability for applications such as non-volatile memories, neuromorphic computing and random number generation circuits.
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Affiliation(s)
- C Acal
- Departamento de Estadística e Investigación Operativa e Instituto de Matemáticas (IMAG), Universidad de Granada, Facultad de Ciencias, Avd. Fuentenueva s/n, 18071 Granada, Spain
| | - D Maldonado
- Departamento de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias, Avd. Fuentenueva s/n, 18071 Granada, Spain.
- IHP-Leibniz-Institut für innovative Mikroelektronik, 15236 Frankfurt (Oder), Germany
| | - A Cantudo
- Departamento de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias, Avd. Fuentenueva s/n, 18071 Granada, Spain.
| | - M B González
- Institut de Microelectrònica de Barcelona IMB-CNM (CSIC), Carrer dels Til·lers s/n, Campus UAB, 08193 Bellaterra, Spain
| | - F Jiménez-Molinos
- Departamento de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias, Avd. Fuentenueva s/n, 18071 Granada, Spain.
| | - F Campabadal
- Institut de Microelectrònica de Barcelona IMB-CNM (CSIC), Carrer dels Til·lers s/n, Campus UAB, 08193 Bellaterra, Spain
| | - J B Roldán
- Departamento de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias, Avd. Fuentenueva s/n, 18071 Granada, Spain.
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23
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Vishwanath SK, Febriansyah B, Ng SE, Das T, Acharya J, John RA, Sharma D, Dananjaya PA, Jagadeeswararao M, Tiwari N, Kulkarni MRC, Lew WS, Chakraborty S, Basu A, Mathews N. High-performance one-dimensional halide perovskite crossbar memristors and synapses for neuromorphic computing. MATERIALS HORIZONS 2024; 11:2643-2656. [PMID: 38516931 DOI: 10.1039/d3mh02055j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
Despite impressive demonstrations of memristive behavior with halide perovskites, no clear pathway for material and device design exists for their applications in neuromorphic computing. Present approaches are limited to single element structures, fall behind in terms of switching reliability and scalability, and fail to map out the analog programming window of such devices. Here, we systematically design and evaluate robust pyridinium-templated one-dimensional halide perovskites as crossbar memristive materials for artificial neural networks. We compare two halide perovskite 1D inorganic lattices, namely (propyl)pyridinium and (benzyl)pyridinium lead iodide. The absence of conjugated, electron-rich substituents in PrPyr+ prevents edge-to-face type π-stacking, leading to enhanced electronic isolation of the 1D iodoplumbate chains in (PrPyr)[PbI3], and hence, superior resistive switching performance compared to (BnzPyr)[PbI3]. We report outstanding resistive switching behaviours in (PrPyr)[PbI3] on the largest flexible crossbar implementation (16 × 16) to date - on/off ratio (>105), long term retention (105 s) and high endurance (2000 cycles). Finally, we put forth a universal approach to comprehensively map the analog programming window of halide perovskite memristive devices - a critical prerequisite for weighted synaptic connections in artificial neural networks. This consequently facilitates the demonstration of accurate handwritten digit recognition from the MNIST database based on spike-timing-dependent plasticity of halide perovskite memristive synapses.
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Affiliation(s)
- Sujaya Kumar Vishwanath
- School of Materials Science & Engineering, Nanyang Technological University, 639798, Singapore.
| | - Benny Febriansyah
- Energy Research Institute @ NTU (ERI@N), Nanyang Technological University, 637553, Singapore
| | - Si En Ng
- School of Materials Science & Engineering, Nanyang Technological University, 639798, Singapore.
| | - Tisita Das
- Materials Theory for Energy Scavenging (MATES) Lab, Harish-Chandra Research Institute(HRI) Allahabad, HBNI, Chhatnag Road, Jhunsi, Prayagraj (Allahabad), 211019, India.
| | - Jyotibdha Acharya
- School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore
| | - Rohit Abraham John
- School of Materials Science & Engineering, Nanyang Technological University, 639798, Singapore.
| | - Divyam Sharma
- School of Materials Science & Engineering, Nanyang Technological University, 639798, Singapore.
| | - Putu Andhita Dananjaya
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore
| | | | - Naveen Tiwari
- School of Materials Science & Engineering, Nanyang Technological University, 639798, Singapore.
| | | | - Wen Siang Lew
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore
| | - Sudip Chakraborty
- Materials Theory for Energy Scavenging (MATES) Lab, Harish-Chandra Research Institute(HRI) Allahabad, HBNI, Chhatnag Road, Jhunsi, Prayagraj (Allahabad), 211019, India.
| | - Arindam Basu
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong
| | - Nripan Mathews
- School of Materials Science & Engineering, Nanyang Technological University, 639798, Singapore.
- Energy Research Institute @ NTU (ERI@N), Nanyang Technological University, 637553, Singapore
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24
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Li B, Xia F, Du B, Zhang S, Xu L, Su Q, Zhang D, Yang J. 2D Halide Perovskites for High-Performance Resistive Switching Memory and Artificial Synapse Applications. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2310263. [PMID: 38647431 PMCID: PMC11187899 DOI: 10.1002/advs.202310263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/21/2024] [Indexed: 04/25/2024]
Abstract
Metal halide perovskites (MHPs) are considered as promising candidates in the application of nonvolatile high-density, low-cost resistive switching (RS) memories and artificial synapses, resulting from their excellent electronic and optoelectronic properties including large light absorption coefficient, fast ion migration, long carrier diffusion length, low trap density, high defect tolerance. Among MHPs, 2D halide perovskites have exotic layered structure and great environment stability as compared with 3D counterparts. Herein, recent advances of 2D MHPs for the RS memories and artificial synapses realms are comprehensively summarized and discussed, as well as the layered structure properties and the related physical mechanisms are presented. Furthermore, the current issues and developing roadmap for the next-generation 2D MHPs RS memories and artificial synapse are elucidated.
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Affiliation(s)
- Bixin Li
- School of Physics and ChemistryHunan First Normal UniversityChangsha410205China
- Shaanxi Institute of Flexible Electronics (SIFE)Northwestern Polytechnical University (NPU)Xi'anShaanxi710072China
- School of PhysicsCentral South University932 South Lushan RoadChangshaHunan410083China
| | - Fei Xia
- Shaanxi Institute of Flexible Electronics (SIFE)Northwestern Polytechnical University (NPU)Xi'anShaanxi710072China
| | - Bin Du
- School of Materials Science and EngineeringXi'an Polytechnic UniversityXi'an710048China
| | - Shiyang Zhang
- School of Physics and ChemistryHunan First Normal UniversityChangsha410205China
| | - Lan Xu
- School of Physics and ChemistryHunan First Normal UniversityChangsha410205China
| | - Qiong Su
- School of Physics and ChemistryHunan First Normal UniversityChangsha410205China
| | - Dingke Zhang
- School of Physics and Electronic EngineeringChongqing Normal UniversityChongqing401331China
| | - Junliang Yang
- School of PhysicsCentral South University932 South Lushan RoadChangshaHunan410083China
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25
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Oh J, Park S, Lee SH, Kim S, Lee H, Lee C, Hong W, Cha J, Kang M, Jin JH, Im SG, Kim MJ, Choi S. Ultrathin All-Solid-State MoS 2-Based Electrolyte Gated Synaptic Transistor with Tunable Organic-Inorganic Hybrid Film. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308847. [PMID: 38566434 PMCID: PMC11187882 DOI: 10.1002/advs.202308847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/24/2024] [Indexed: 04/04/2024]
Abstract
Electrolyte-gated synaptic transistors (EGSTs) have attracted considerable attention as synaptic devices owing to their adjustable conductance, low power consumption, and multi-state storage capabilities. To demonstrate high-density EGST arrays, 2D materials are recommended owing to their excellent electrical properties and ultrathin profile. However, widespread implementation of 2D-based EGSTs has challenges in achieving large-area channel growth and finding compatible nanoscale solid electrolytes. This study demonstrates large-scale process-compatible, all-solid-state EGSTs utilizing molybdenum disulfide (MoS2) channels grown through chemical vapor deposition (CVD) and sub-30 nm organic-inorganic hybrid electrolyte polymers synthesized via initiated chemical vapor deposition (iCVD). The iCVD technique enables precise modulation of the hydroxyl group density in the hybrid matrix, allowing the modulation of proton conduction, resulting in adjustable synaptic performance. By leveraging the tunable iCVD-based hybrid electrolyte, the fabricated EGSTs achieve remarkable attributes: a wide on/off ratio of 109, state retention exceeding 103, and linear conductance updates. Additionally, the device exhibits endurance surpassing 5 × 104 cycles, while maintaining a low energy consumption of 200 fJ/spike. To evaluate the practicality of these EGSTs, a subset of devices is employed in system-level simulations of MNIST handwritten digit recognition, yielding a recognition rate of 93.2%.
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Affiliation(s)
- Jungyeop Oh
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Seohak Park
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Sang Hun Lee
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Sungkyu Kim
- Department of Nanotechnology and Advanced Materials EngineeringSejong University209 Neungdong‐ro, Gwangjin‐guSeoul05006Republic of Korea
| | - Hyeonji Lee
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Changhyeon Lee
- Department of Chemical and Biomolecular EngineeringGraphene/2D Materials Research CenterKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Woonggi Hong
- School of Electronics and Electrical EngineeringDankook UniversityGyeonggi16890Republic of Korea
| | - Jun‐Hwe Cha
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Mingu Kang
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Jun Hyup Jin
- School of Electronics and Electrical EngineeringDankook UniversityGyeonggi16890Republic of Korea
| | - Sung Gap Im
- Department of Chemical and Biomolecular EngineeringGraphene/2D Materials Research CenterKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
| | - Min Ju Kim
- School of Electronics and Electrical EngineeringDankook UniversityGyeonggi16890Republic of Korea
| | - Sung‐Yool Choi
- School of Electrical EngineeringGraduate School of Semiconductor TechnologyKorea Advanced Institute of Science and Technology (KAIST)291 Daehak‐ro, Yuseong‐guDaejeon34141Republic of Korea
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26
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Jayakrishnan AR, Kim JS, Hellenbrand M, Marques LS, MacManus-Driscoll JL, Silva JPB. Growth of emergent simple pseudo-binary ferroelectrics and their potential in neuromorphic computing devices. MATERIALS HORIZONS 2024; 11:2355-2371. [PMID: 38477152 PMCID: PMC11104485 DOI: 10.1039/d4mh00153b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
Ferroelectric memory devices such as ferroelectric memristors, ferroelectric tunnel junctions, and field-effect transistors are considered among the most promising candidates for neuromorphic computing devices. The promise arises from their defect-independent switching mechanism, low energy consumption and high power efficiency, and important properties being aimed for are reliable switching at high speed, excellent endurance, retention, and compatibility with complementary metal-oxide-semiconductor (CMOS) technology. Binary or doped binary materials have emerged over conventional complex-composition ferroelectrics as an optimum solution, particularly in terms of CMOS compatibility. The current state-of-the-art route to achieving superlative ferroelectric performance of binary oxides is to induce ferroelectricity at the nanoscale, e.g., in ultra-thin films of doped HfO2, ZrO2, Zn1-xMgxO, Al-xScxN, and Bi1-xSmxO3. This short review article focuses on the materials science of emerging new ferroelectric materials, including their different properties such as remanent polarization, coercive field, endurance, etc. The potential of these materials is discussed for neuromorphic applications.
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Affiliation(s)
- Ampattu R Jayakrishnan
- Physics Center of Minho and Porto Universities (CF-UM-UP), University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.
- Laboratory of Physics for Materials and Emergent Technologies, LapMET, University of Minho, 4710-057 Braga, Portugal
| | - Ji S Kim
- Dept. of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Rd., Cambridge, CB3 OFS, UK.
| | - Markus Hellenbrand
- Dept. of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Rd., Cambridge, CB3 OFS, UK.
| | - Luís S Marques
- Physics Center of Minho and Porto Universities (CF-UM-UP), University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.
- Laboratory of Physics for Materials and Emergent Technologies, LapMET, University of Minho, 4710-057 Braga, Portugal
| | - Judith L MacManus-Driscoll
- Dept. of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Rd., Cambridge, CB3 OFS, UK.
| | - José P B Silva
- Physics Center of Minho and Porto Universities (CF-UM-UP), University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.
- Laboratory of Physics for Materials and Emergent Technologies, LapMET, University of Minho, 4710-057 Braga, Portugal
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27
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Pashin DS, Bastrakova MV, Rybin DA, Soloviev II, Klenov NV, Schegolev AE. Optimisation Challenge for a Superconducting Adiabatic Neural Network That Implements XOR and OR Boolean Functions. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:854. [PMID: 38786810 PMCID: PMC11124324 DOI: 10.3390/nano14100854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/01/2024] [Accepted: 05/11/2024] [Indexed: 05/25/2024]
Abstract
In this article, we consider designs of simple analog artificial neural networks based on adiabatic Josephson cells with a sigmoid activation function. A new approach based on the gradient descent method is developed to adjust the circuit parameters, allowing efficient signal transmission between the network layers. The proposed solution is demonstrated on the example of a system that implements XOR and OR logical operations.
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Affiliation(s)
- Dmitrii S. Pashin
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603022 Nizhny Novgorod, Russia
| | - Marina V. Bastrakova
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603022 Nizhny Novgorod, Russia
- Russian Quantum Centre, 143025 Moscow, Russia
| | - Dmitrii A. Rybin
- Faculty of Physics, Lobachevsky State University of Nizhni Novgorod, 603022 Nizhny Novgorod, Russia
| | - Igor. I. Soloviev
- Russian Quantum Centre, 143025 Moscow, Russia
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia;
- National University of Science and Technology MISIS, 119049 Moscow, Russia;
| | - Nikolay V. Klenov
- National University of Science and Technology MISIS, 119049 Moscow, Russia;
- Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Andrey E. Schegolev
- Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia;
- Science Department, Moscow Technical University of Communication and Informatics (MTUCI), 111024 Moscow, Russia
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28
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Lu C, Wang X, Liu XY. Flexible Meso Electronics and Photonics Based on Cocoon Silk and Applications. ACS Biomater Sci Eng 2024; 10:2784-2804. [PMID: 38597279 DOI: 10.1021/acsbiomaterials.4c00254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
Flexible electronics, applicable to enlarged health, AI big data medications, etc., have been one of the most important technologies of this century. Due to its particular mechanical properties, biocompatibility, and biodegradability, cocoon silk (or SF, silk fibroin) plays a key role in flexible electronics/photonics. The review begins with an examination of the hierarchical meso network structures of SF materials and introduces the concepts of meso reconstruction, meso doping, and meso hybridization based on the correlation between the structure and performance of silk materials. The SF meso functionalization was developed according to intermolecular nuclear templating. By implementation of the techniques of meso reconstruction and functionalization in the refolding of SF materials, extraordinary performance can be achieved. Relying on this strategy, particularly designed flexible electronic and photonic components can be developed. This review covers the latest ideas and technologies of meso flexible electronics and photonics based on SF materials/meso functionalization. As silk materials are biocompatible and human skin-friendly, SF meso flexible electronic/photonic components can be applied to wearable or implanted devices. These devices are applicable in human physiological signals and activities sensing/monitoring. In the case of human-machine interaction, the devices can be applicable in in-body information transmission, computation, and storage, with the potential for the combination of artificial intelligence and human intelligence.
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Affiliation(s)
- Changsheng Lu
- State Key Laboratory of Marine Environmental Science (MEL), College of Ocean and Earth Sciences, Xiamen University, Xiamen, Fujian 361102, P.R. China
| | - Xiao Wang
- State Key Laboratory of Marine Environmental Science (MEL), College of Ocean and Earth Sciences, Xiamen University, Xiamen, Fujian 361102, P.R. China
| | - Xiang Yang Liu
- State Key Laboratory of Marine Environmental Science (MEL), College of Ocean and Earth Sciences, Xiamen University, Xiamen, Fujian 361102, P.R. China
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29
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Zhang W, Wu M, Zhang Y, Yan H, Lee Y, Zhao Z, Hao H, Shi X, Zhang Z, Kim K, Liu N. Paraffin-Enabled Superlattice Customization for a Photostimulated Gradient-Responsive Artificial Reflex Arc. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2313267. [PMID: 38346418 DOI: 10.1002/adma.202313267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/25/2024] [Indexed: 02/21/2024]
Abstract
The development of photostimulated-motion artificial reflex arcs - a neural circuit inspired by light-driven motion reflexes - holds significant promises for advancements in robotic perception, navigation, and motion control. However, the fabrication of such systems, especially those that accommodate multiple actions and exhibit gradient responses, remains challenging. Here, a gradient-responsive photostimulated-motion artificial reflex arc is developed by integrating a programmable and tunable photoreceptor based on folded MoS2 at different twist angles. The twisted folded bilayer MoS2 used as photoreceptors can be customized via the transfer technique using patternable paraffin, where the twist angle and fold-line could be controlled. The photoluminescence (PL) intensity is 3.7 times higher at a twist angle of 29° compared to that at 0°, showing a monotonically decreasing indirect bandgap. Through tunable interlayer carrier transport, photoreceptors fabricated using folded bilayer MoS2 at different twist angles demonstrate gradient response time, enabling the photostimulated-motion artificial reflex arc for multiaction responses. They are transformed to digital command flow and studied via machine learning to control the gestures of a robotic hand, showing a prototype of photostimulated gradient-responsive artificial reflex arcs for the first time. This work provides a unique idea for developing intelligent soft robots and next-generation human-computer interfaces.
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Affiliation(s)
- Weifeng Zhang
- Beijing Key Laboratory of Energy Conversion and Storage Materials, College of Chemistry, Beijing Normal University, Beijing, 100875, P. R. China
| | - Mengwei Wu
- College of Engineering, Peking University, Beijing, 100871, China
| | - Yan Zhang
- Beijing Key Laboratory of Energy Conversion and Storage Materials, College of Chemistry, Beijing Normal University, Beijing, 100875, P. R. China
| | - Hongyi Yan
- Department of Physics, Beijing Normal University, Beijing, 100875, P. R. China
| | - Yangjin Lee
- Center for Nanomedicine, Institute for Basic Science, Seoul, 03722, South Korea
- Department of Physics, Yonsei University, Seoul, 03722, South Korea
| | - Zihan Zhao
- Beijing Key Laboratory of Energy Conversion and Storage Materials, College of Chemistry, Beijing Normal University, Beijing, 100875, P. R. China
| | - He Hao
- Beijing Key Laboratory of Energy Conversion and Storage Materials, College of Chemistry, Beijing Normal University, Beijing, 100875, P. R. China
| | - Xiaohu Shi
- Beijing Key Laboratory of Energy Conversion and Storage Materials, College of Chemistry, Beijing Normal University, Beijing, 100875, P. R. China
| | - Zhaoxian Zhang
- College of Design and Engineering, National University of Singapore, 9 Engineering Drive 1, #07-26, EA, Singapore, 117575, Singapore
| | - Kwanpyo Kim
- Center for Nanomedicine, Institute for Basic Science, Seoul, 03722, South Korea
- Department of Physics, Yonsei University, Seoul, 03722, South Korea
| | - Nan Liu
- Beijing Key Laboratory of Energy Conversion and Storage Materials, College of Chemistry, Beijing Normal University, Beijing, 100875, P. R. China
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30
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Koh EK, Dananjaya PA, Poh HY, Liu L, Lee CXX, Thong JR, You YS, Lew WS. Unraveling the origins of the coexisting localized-interfacial mechanism in oxide-based memristors in CMOS-integrated synaptic device implementations. NANOSCALE HORIZONS 2024; 9:828-842. [PMID: 38450438 DOI: 10.1039/d3nh00554b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The forefront of neuromorphic research strives to develop devices with specific properties, i.e., linear and symmetrical conductance changes under external stimuli. This is paramount for neural network accuracy when emulating a biological synapse. A parallel exploration of resistive memory as a replacement for conventional computing memory ensues. In search of a holistic solution, the proposed memristive device in this work is uniquely poised to address this elusive gap as a unified memory solution. Opposite biasing operations are leveraged to achieve stable abrupt and gradual switching characteristics within a single device, addressing the demands for lower latency and energy consumption for binary switching applications, and graduality for neuromorphic computing applications. We evaluated the underlying principles of both switching modes, attributing the anomalous gradual switching to the modulation of oxygen-deficient layers formed between the active electrode and oxide switching layer. The memristive cell (1R) was integrated with 40 nm transistor technology (1T) to form a 1T-1R memory cell, demonstrating a switching speed of 50 ns with a pulse amplitude of ±2.5 V in its forward-biased mode. Applying pulse trains of 20 ns to 490 ns in the reverse-biased mode exhibited synaptic weight properties, obtaining a nonlinearity (NL) factor of <0.5 for both potentiation and depression. The devices in both modes also demonstrated an endurance of >106 cycles, and their conductance states were also stable under temperature stress at 85 °C for 104 s. With the duality of the two switching modes, our device can be used for both memory and synaptic weight-storing applications.
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Affiliation(s)
- Eng Kang Koh
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
- GLOBALFOUNDRIES Singapore Pte Ltd, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Putu Andhita Dananjaya
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
| | - Han Yin Poh
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
- GLOBALFOUNDRIES Singapore Pte Ltd, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Lingli Liu
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
| | - Calvin Xiu Xian Lee
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
- GLOBALFOUNDRIES Singapore Pte Ltd, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Jia Rui Thong
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
- GLOBALFOUNDRIES Singapore Pte Ltd, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Young Seon You
- GLOBALFOUNDRIES Singapore Pte Ltd, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Wen Siang Lew
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore.
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31
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Galloni AR, Yuan Y, Zhu M, Yu H, Bisht RS, Wu CTM, Grienberger C, Ramanathan S, Milstein AD. Neuromorphic one-shot learning utilizing a phase-transition material. Proc Natl Acad Sci U S A 2024; 121:e2318362121. [PMID: 38630718 PMCID: PMC11047090 DOI: 10.1073/pnas.2318362121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 03/25/2024] [Indexed: 04/19/2024] Open
Abstract
Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient AI and learning machines. An important factor in selection of the hardware building blocks is the identification of candidate materials with physical properties suitable to emulate the large dynamic ranges and varied timescales of neuronal signaling. Previous work has shown that the all-or-none spiking behavior of neurons can be mimicked by threshold switches utilizing material phase transitions. Here, we demonstrate that devices based on a prototypical metal-insulator-transition material, vanadium dioxide (VO2), can be dynamically controlled to access a continuum of intermediate resistance states. Furthermore, the timescale of their intrinsic relaxation can be configured to match a range of biologically relevant timescales from milliseconds to seconds. We exploit these device properties to emulate three aspects of neuronal analog computation: fast (~1 ms) spiking in a neuronal soma compartment, slow (~100 ms) spiking in a dendritic compartment, and ultraslow (~1 s) biochemical signaling involved in temporal credit assignment for a recently discovered biological mechanism of one-shot learning. Simulations show that an artificial neural network using properties of VO2 devices to control an agent navigating a spatial environment can learn an efficient path to a reward in up to fourfold fewer trials than standard methods. The phase relaxations described in our study may be engineered in a variety of materials and can be controlled by thermal, electrical, or optical stimuli, suggesting further opportunities to emulate biological learning in neuromorphic hardware.
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Affiliation(s)
- Alessandro R. Galloni
- Department of Neuroscience and Cell Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ08854
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ08854
| | - Yifan Yuan
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ08854
| | - Minning Zhu
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ08854
| | - Haoming Yu
- School of Materials Engineering, Purdue University, West Lafayette, IN47907
| | - Ravindra S. Bisht
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ08854
| | - Chung-Tse Michael Wu
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ08854
| | - Christine Grienberger
- Department of Neuroscience, Brandeis University, Waltham, MA02453
- Department of Biology and Volen National Center for Complex Systems, Brandeis University, Waltham, MA02453
| | - Shriram Ramanathan
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ08854
| | - Aaron D. Milstein
- Department of Neuroscience and Cell Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ08854
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ08854
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32
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Pal A, Chai Z, Jiang J, Cao W, Davies M, De V, Banerjee K. An ultra energy-efficient hardware platform for neuromorphic computing enabled by 2D-TMD tunnel-FETs. Nat Commun 2024; 15:3392. [PMID: 38649379 PMCID: PMC11035659 DOI: 10.1038/s41467-024-46397-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/26/2024] [Indexed: 04/25/2024] Open
Abstract
Brain-like energy-efficient computing has remained elusive for neuromorphic (NM) circuits and hardware platform implementations despite decades of research. In this work we reveal the opportunity to significantly improve the energy efficiency of digital neuromorphic hardware by introducing NM circuits employing two-dimensional (2D) transition metal dichalcogenide (TMD) layered channel material-based tunnel-field-effect transistors (TFETs). Our novel leaky-integrate-fire (LIF) based digital NM circuit along with its Hebbian learning circuitry operates at a wide range of supply voltages, frequencies, and activity factors, enabling two orders of magnitude higher energy-efficient computing that is difficult to achieve with conventional material and/or device platforms, specifically the silicon-based 7 nm low-standby-power FinFET technology. Our innovative 2D-TFET based NM circuit paves the way toward brain-like energy-efficient computing that can unleash major transformations in future AI and data analytics platforms.
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Affiliation(s)
- Arnab Pal
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA
| | - Zichun Chai
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA
| | - Junkai Jiang
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA
| | - Wei Cao
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA
| | | | | | - Kaustav Banerjee
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA.
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33
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Dong S, Liu H, Wang Y, Bian J, Su J. Ferroelectricity-Defects Synergistic Artificial Synapses for High Recognition Accuracy Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2024; 16:19235-19246. [PMID: 38584351 DOI: 10.1021/acsami.4c01489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
The ability of ferroelectric memristors to modulate conductance and offer multilevel storage has garnered significant attention in the realm of artificial synapses. On one hand, the resistance change of ferroelectric memristors mainly depends on the polarization reversal. On the other hand, the defects such as oxygen vacancies, which are inevitable presence during high-temperature processes, can undergo diffusion drift with the polarization reversal, thereby change the interface potential barrier. Thus, it is both desirable and necessary to investigate the synergistic effect of ferroelectricity and defects. Here, we prepare BaTiO3 ferroelectric memristor by pulse laser deposition and achieve resistance switching through the synergistic effect of ferroelectricity and oxygen vacancies. The memristor shows excellent switching characteristics with a large switching ratio (104) and good stability (103 s). It effectively emulates the features of artificial synapses and accomplishes decimal logical neural computing. In the neuromorphic system crafted with the memristor, the recognition accuracy of the 28 × 28 pixel image reaches 94.9%. These findings strongly support the research of ferroelectric memristors in neuromorphic devices.
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Affiliation(s)
- Shijie Dong
- College of Physics Science, Qingdao University, Qingdao 266071, People's Republic of China
| | - Hao Liu
- College of Electronic and Information Engineering, Qingdao University, Qingdao 266071, People's Republic of China
| | - Yan Wang
- College of Electronic and Information Engineering, Qingdao University, Qingdao 266071, People's Republic of China
| | - Jing Bian
- College of Electronic and Information Engineering, Qingdao University, Qingdao 266071, People's Republic of China
| | - Jie Su
- College of Physics Science, Qingdao University, Qingdao 266071, People's Republic of China
- College of Electronic and Information Engineering, Qingdao University, Qingdao 266071, People's Republic of China
- National Laboratory of Solid State Microstructures, Physics Department, Nanjing University, Nanjing 210093, People's Republic of China
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34
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Lee SU, Kim SY, Lee JH, Baek JH, Lee JW, Jang HW, Park NG. Artificial Synapse Based on a δ-FAPbI 3/Atomic-Layer-Deposited SnO 2 Bilayer Memristor. NANO LETTERS 2024. [PMID: 38619226 DOI: 10.1021/acs.nanolett.4c00253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Halide perovskite-based resistive switching memory (memristor) has potential in an artificial synapse. However, an abrupt switch behavior observed for a formamidinium lead triiodide (FAPbI3)-based memristor is undesirable for an artificial synapse. Here, we report on the δ-FAPbI3/atomic-layer-deposited (ALD)-SnO2 bilayer memristor for gradual analogue resistive switching. In comparison to a single-layer δ-FAPbI3 memristor, the heterojunction δ-FAPbI3/ALD-SnO2 bilayer effectively reduces the current level in the high-resistance state. The analog resistive switching characteristics of δ-FAPbI3/ALD-SnO2 demonstrate exceptional linearity and potentiation/depression performance, resembling an artificial synapse for neuromorphic computing. The nonlinearity of long-term potentiation and long-term depression is notably decreased from 12.26 to 0.60 and from -8.79 to -3.47, respectively. Moreover, the δ-FAPbI3/ALD-SnO2 bilayer achieves a recognition rate of ≤94.04% based on the modified National Institute of Standards and Technology database (MNIST), establishing its potential in an efficient artificial synapse.
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Affiliation(s)
- Sang-Uk Lee
- School of Chemical Engineering, Center for Antibonding Regulated Crystals, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - So-Yeon Kim
- School of Chemical Engineering, Center for Antibonding Regulated Crystals, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Joo-Hong Lee
- Department of Nano Science and Technology and Department of Nanoengineering, SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Ji Hyun Baek
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
| | - Jin-Wook Lee
- Department of Nano Science and Technology and Department of Nanoengineering, SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- SKKU Institute of Energy Science and Technology (SIEST), Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Ho Won Jang
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
- Advanced Institute of Convergence Technology, Seoul National University, Suwon 16229, Republic of Korea
| | - Nam-Gyu Park
- School of Chemical Engineering, Center for Antibonding Regulated Crystals, Sungkyunkwan University, Suwon 16419, Republic of Korea
- SKKU Institute of Energy Science and Technology (SIEST), Sungkyunkwan University, Suwon 16419, Republic of Korea
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35
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Fedotov M, Korotitsky V, Koveshnikov S. Modeling of Self-Aligned Selector Based on Ultra-Thin Metal Oxide for Resistive Random-Access Memory (RRAM) Crossbar Arrays. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:668. [PMID: 38668162 PMCID: PMC11054844 DOI: 10.3390/nano14080668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/05/2024] [Accepted: 04/10/2024] [Indexed: 04/29/2024]
Abstract
Resistive random-access memory (RRAM) is a crucial element for next-generation large-scale memory arrays, analogue neuromorphic computing and energy-efficient System-on-Chip applications. For these applications, RRAM elements are arranged into Crossbar arrays, where rectifying selector devices are required for correct read operation of the memory cells. One of the key advantages of RRAM is its high scalability due to the filamentary mechanism of resistive switching, as the cell conductivity is not dependent on the cell area. Thus, a selector device becomes a limiting factor in Crossbar arrays in terms of scalability, as its area exceeds the minimal possible area of an RRAM cell. We propose a tunnel diode selector, which is self-aligned with an RRAM cell and, thus, occupies the same area. In this study, we address the theoretical and modeling aspects of creating a self-aligned selector with optimal parameters to avoid any deterioration of RRAM cell performance. We investigate the possibilities of using a tunnel diode based on single- and double-layer dielectrics and determine their optimal physical properties to be used in an HfOx-based RRAM Crossbar array.
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Affiliation(s)
- Mikhail Fedotov
- Institute of Microelectronics Technology and High-Purity Materials, Russian Academy of Science (IMT RAS), 6 Academician Ossipyan Str., Moscow District, Chernogolovka 142432, Russia
| | | | - Sergei Koveshnikov
- Institute of Microelectronics Technology and High-Purity Materials, Russian Academy of Science (IMT RAS), 6 Academician Ossipyan Str., Moscow District, Chernogolovka 142432, Russia
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36
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Zhou X, Zong Y, Wang Y, Sun M, Shi D, Wang W, Du G, Xie Y. Nanofluidic memristor based on the elastic deformation of nanopores with nanoparticle adsorption. Natl Sci Rev 2024; 11:nwad216. [PMID: 38487493 PMCID: PMC10939365 DOI: 10.1093/nsr/nwad216] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 06/13/2023] [Accepted: 07/15/2023] [Indexed: 03/17/2024] Open
Abstract
The memristor is the building block of neuromorphic computing. We report a new type of nanofluidic memristor based on the principle of elastic strain on polymer nanopores. With nanoparticles absorbed at the wall of a single conical polymer nanopore, we find a pinched hysteresis of the current within a scanning frequency range of 0.01-0.1 Hz, switching to a diode below 0.01 Hz and a resistor above 0.1 Hz. We attribute the current hysteresis to the elastic strain at the tip side of the nanopore, caused by electrical force on the particles adsorbed at the inner wall surface. Our simulation and analytical equations match well with experimental results, with a phase diagram for predicting the system transitions. We demonstrate the plasticity of our nanofluidic memristor to be similar to a biological synapse. Our findings pave a new way for ionic neuromorphic computing using nanofluidic memristors.
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Affiliation(s)
- Xi Zhou
- Department of Chemistry and Chemical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
| | - Yuanyuan Zong
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
| | - Yongchang Wang
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
| | - Miao Sun
- School of Aeronautics and Institute of Extreme Mechanics, Northwestern Polytechnical University, Xi’an 710072, China
| | - Deli Shi
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
| | - Wei Wang
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
| | - Guanghua Du
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China
| | - Yanbo Xie
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
- School of Aeronautics and Institute of Extreme Mechanics, Northwestern Polytechnical University, Xi’an 710072, China
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37
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Franco M, Kiazadeh A, Deuermeier J, Lanceros-Méndez S, Martins R, Carlos E. Inkjet printed IGZO memristors with volatile and non-volatile switching. Sci Rep 2024; 14:7469. [PMID: 38553556 PMCID: PMC10980760 DOI: 10.1038/s41598-024-58228-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/26/2024] [Indexed: 04/02/2024] Open
Abstract
Solution-based memristors deposited by inkjet printing technique have a strong technological potential based on their scalability, low cost, environmentally friendlier processing by being an efficient technique with minimal material waste. Indium-gallium-zinc oxide (IGZO), an oxide semiconductor material, shows promising resistive switching properties. In this work, a printed Ag/IGZO/ITO memristor has been fabricated. The IGZO thickness influences both memory window and switching voltage of the devices. The devices show both volatile counter8wise (c8w) and non-volatile 8wise (8w) switching at low operating voltage. The 8w switching has a SET and RESET voltage lower than 2 V and - 5 V, respectively, a retention up to 105 s and a memory window up to 100, whereas the c8w switching shows volatile characteristics with a low threshold voltage (Vth < - 0.65 V) and a characteristic time (τ) of 0.75 ± 0.12 ms when a single pulse of - 0.65 V with width of 0.1 ms is applied. The characteristic time alters depending on the number of pulses. These volatile characteristics allowed them to be tested on different 4-bit pulse sequences, as an initial proof of concept for temporal signal processing applications.
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Affiliation(s)
- Miguel Franco
- Center of Physics, University of Minho and Laboratory of Physics for Materials and Emergent Technologies, LapMET, Campus de Gualtar, 4710-057, Braga, Portugal
- CENIMAT|i3N, Department of Materials Science, School of Science and Technology, NOVA University Lisbon and CEMOP/UNINOVA, Caparica, Portugal
| | - Asal Kiazadeh
- CENIMAT|i3N, Department of Materials Science, School of Science and Technology, NOVA University Lisbon and CEMOP/UNINOVA, Caparica, Portugal.
| | - Jonas Deuermeier
- CENIMAT|i3N, Department of Materials Science, School of Science and Technology, NOVA University Lisbon and CEMOP/UNINOVA, Caparica, Portugal
| | - S Lanceros-Méndez
- Center of Physics, University of Minho and Laboratory of Physics for Materials and Emergent Technologies, LapMET, Campus de Gualtar, 4710-057, Braga, Portugal
- BCMaterials, Basque Center for Materials, Applications and Nanostructures, UPV/EHU Science Park, 48940, Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48009, Bilbao, Spain
| | - Rodrigo Martins
- CENIMAT|i3N, Department of Materials Science, School of Science and Technology, NOVA University Lisbon and CEMOP/UNINOVA, Caparica, Portugal
| | - Emanuel Carlos
- CENIMAT|i3N, Department of Materials Science, School of Science and Technology, NOVA University Lisbon and CEMOP/UNINOVA, Caparica, Portugal.
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38
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Choi S, Son J, MacManus-Driscoll JL, Lee S. Hydrogen-Driven Low-Temperature Topotactic Transition in Nanocomb Cobaltite for Ultralow Power Ionic-Magnetic Coupled Applications. NANO LETTERS 2024; 24:3606-3613. [PMID: 38483316 DOI: 10.1021/acs.nanolett.3c04414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
We reversibly control ferromagnetic-antiferromagnetic ordering in an insulating ground state by annealing tensile-strained LaCoO3 films in hydrogen. This ionic-magnetic coupling occurs due to the hydrogen-driven topotactic transition between perovskite LaCoO3 and brownmillerite La2Co2O5 at a lower temperature (125-200 °C) and within a shorter time (3-10 min) than the oxygen-driven effect (500 °C, tens of hours). The X-ray and optical spectroscopic analyses reveal that the transition results from hydrogen-driven filling of correlated electrons in the Co 3d-orbitals, which successively releases oxygen by destabilizing the CoO6 octahedra into CoO4 tetrahedra. The transition is accelerated by surface exchange, diffusion of hydrogen in and oxygen out through atomically ordered oxygen vacancy "nanocomb" stripes in the tensile-strained LaCoO3 films. Our ionic-magnetic coupling with fast operation, good reproducibility, and long-term stability is a proof-of-principle demonstration of high-performance ultralow power magnetic switching devices for sensors, energy, and artificial intelligence applications, which are keys for attaining carbon neutrality.
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Affiliation(s)
- Songhee Choi
- Department of Physics and Chemistry, DGIST, Daegu 42988, Republic of Korea
| | - Jaeseok Son
- Center for Correlated Electron Systems, Institute for Basic Science, Seoul 08826, Republic of Korea
- Department of Physics and Astronomy, Seoul National University, Seoul 08826, Republic of Korea
| | - Judith L MacManus-Driscoll
- Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, United Kingdom
| | - Shinbuhm Lee
- Department of Physics and Chemistry, DGIST, Daegu 42988, Republic of Korea
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39
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Su D, Liang X, Geng D, Wu Q, Liu B, Liu C. An Artificial Neural Network Based on Oxide Synaptic Transistor for Accurate and Robust Image Recognition. MICROMACHINES 2024; 15:433. [PMID: 38675245 PMCID: PMC11052312 DOI: 10.3390/mi15040433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 04/28/2024]
Abstract
Synaptic transistors with low-temperature, solution-processed dielectric films have demonstrated programmable conductance, and therefore potential applications in hardware artificial neural networks for recognizing noisy images. Here, we engineered AlOx/InOx synaptic transistors via a solution process to instantiate neural networks. The transistors show long-term potentiation under appropriate gate voltage pulses. The artificial neural network, consisting of one input layer and one output layer, was constructed using 9 × 3 synaptic transistors. By programming the calculated weight, the hardware network can recognize 3 × 3 pixel images of characters z, v and n with a high accuracy of 85%, even with 40% noise. This work demonstrates that metal-oxide transistors, which exhibit significant long-term potentiation of conductance, can be used for the accurate recognition of noisy images.
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Affiliation(s)
- Dongyue Su
- The State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (D.S.); (B.L.); (C.L.)
| | - Xiaoci Liang
- The State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (D.S.); (B.L.); (C.L.)
| | - Di Geng
- State Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China;
| | - Qian Wu
- School of Computer and Information Engineering, Guangdong Polytechnic of Industry and Commerce, Guangzhou 510510, China;
| | - Baiquan Liu
- The State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (D.S.); (B.L.); (C.L.)
| | - Chuan Liu
- The State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China; (D.S.); (B.L.); (C.L.)
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40
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Shi T, Zhang H, Cui S, Liu J, Gu Z, Wang Z, Yan X, Liu Q. Stochastic neuro-fuzzy system implemented in memristor crossbar arrays. SCIENCE ADVANCES 2024; 10:eadl3135. [PMID: 38517972 PMCID: PMC10959402 DOI: 10.1126/sciadv.adl3135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 02/16/2024] [Indexed: 03/24/2024]
Abstract
Neuro-symbolic artificial intelligence has garnered considerable attention amid increasing industry demands for high-performance neural networks that are interpretable and adaptable to previously unknown problem domains with minimal reconfiguration. However, implementing neuro-symbolic hardware is challenging due to the complexity in symbolic knowledge representation and calculation. We experimentally demonstrated a memristor-based neuro-fuzzy hardware based on TiN/TaOx/HfOx/TiN chips that is superior to its silicon-based counterpart in terms of throughput and energy efficiency by using array topological structure for knowledge representation and physical laws for computing. Intrinsic memristor variability is fully exploited to increase robustness in knowledge representation. A hybrid in situ training strategy is proposed for error minimizing in training. The hardware adapts easier to a previously unknown environment, achieving ~6.6 times faster convergence and ~6 times lower error than deep learning. The hardware energy efficiency is over two orders of magnitude greater than field-programmable gate arrays. This research greatly extends the capability of memristor-based neuromorphic computing systems in artificial intelligence.
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Affiliation(s)
- Tuo Shi
- State Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China
- Research Center for Intelligent Computing Hardware, Zhejiang Laboratory, Hangzhou 311122, China
| | - Hui Zhang
- Research Center for Intelligent Computing Hardware, Zhejiang Laboratory, Hangzhou 311122, China
| | - Shiyu Cui
- Research Center for Intelligent Computing Hardware, Zhejiang Laboratory, Hangzhou 311122, China
| | - Jinchang Liu
- Research Center for Intelligent Computing Hardware, Zhejiang Laboratory, Hangzhou 311122, China
| | - Zixi Gu
- Research Center for Intelligent Computing Hardware, Zhejiang Laboratory, Hangzhou 311122, China
| | - Zhanfeng Wang
- Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding 071002, P. R. China
| | - Xiaobing Yan
- Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding 071002, P. R. China
| | - Qi Liu
- Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
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41
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Bag SP, Lee S, Song J, Kim J. Hydrogel-Gated FETs in Neuromorphic Computing to Mimic Biological Signal: A Review. BIOSENSORS 2024; 14:150. [PMID: 38534257 DOI: 10.3390/bios14030150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/13/2024] [Accepted: 03/13/2024] [Indexed: 03/28/2024]
Abstract
Hydrogel-gated synaptic transistors offer unique advantages, including biocompatibility, tunable electrical properties, being biodegradable, and having an ability to mimic biological synaptic plasticity. For processing massive data with ultralow power consumption due to high parallelism and human brain-like processing abilities, synaptic transistors have been widely considered for replacing von Neumann architecture-based traditional computers due to the parting of memory and control units. The crucial components mimic the complex biological signal, synaptic, and sensing systems. Hydrogel, as a gate dielectric, is the key factor for ionotropic devices owing to the excellent stability, ultra-high linearity, and extremely low operating voltage of the biodegradable and biocompatible polymers. Moreover, hydrogel exhibits ionotronic functions through a hybrid circuit of mobile ions and mobile electrons that can easily interface between machines and humans. To determine the high-efficiency neuromorphic chips, the development of synaptic devices based on organic field effect transistors (OFETs) with ultra-low power dissipation and very large-scale integration, including bio-friendly devices, is needed. This review highlights the latest advancements in neuromorphic computing by exploring synaptic transistor developments. Here, we focus on hydrogel-based ionic-gated three-terminal (3T) synaptic devices, their essential components, and their working principle, and summarize the essential neurodegenerative applications published recently. In addition, because hydrogel-gated FETs are the crucial members of neuromorphic devices in terms of cutting-edge synaptic progress and performances, the review will also summarize the biodegradable and biocompatible polymers with which such devices can be implemented. It is expected that neuromorphic devices might provide potential solutions for the future generation of interactive sensation, memory, and computation to facilitate the development of multimodal, large-scale, ultralow-power intelligent systems.
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Affiliation(s)
- Sankar Prasad Bag
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Suyoung Lee
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Jaeyoon Song
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
| | - Jinsink Kim
- Department of Biomedical Engineering, College of Life Science and Biotechnology, Dongguk University, Seoul 04620, Republic of Korea
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42
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Zhang T, Guo X, Wang P, Fan X, Wang Z, Tong Y, Wang D, Tong L, Li L. High performance artificial visual perception and recognition with a plasmon-enhanced 2D material neural network. Nat Commun 2024; 15:2471. [PMID: 38503787 PMCID: PMC10951348 DOI: 10.1038/s41467-024-46867-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 03/13/2024] [Indexed: 03/21/2024] Open
Abstract
The development of neuromorphic visual systems has recently gained momentum due to their potential in areas such as autonomous vehicles and robotics. However, current machine visual systems based on silicon technology usually contain photosensor arrays, format conversion, memory and processing modules. As a result, the redundant data shuttling between each unit, resulting in large latency and high-power consumption, seriously limits the performance of neuromorphic vision chips. Here, we demonstrate an artificial neural network (ANN) architecture based on an integrated 2D MoS2/Ag nanograting phototransistor array, which can simultaneously sense, pre-process and recognize optical images without latency. The pre-processing function of the device under photoelectric synergy ensures considerable improvement of efficiency and accuracy of subsequent image recognition. The comprehensive performance of the proof-of-concept device demonstrates great potential for machine vision applications in terms of large dynamic range (180 dB), high speed (500 ns) and low energy consumption per spike (2.4 × 10-17 J).
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Affiliation(s)
- Tian Zhang
- State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Xin Guo
- State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China
- Intelligent Optics and Photonics Research Center, Jiaxing Institute Zhejiang University, Jiaxing, China
| | - Pan Wang
- State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China
- Intelligent Optics and Photonics Research Center, Jiaxing Institute Zhejiang University, Jiaxing, China
| | - Xinyi Fan
- State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Zichen Wang
- State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Yan Tong
- State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Decheng Wang
- State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Limin Tong
- State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China
- Intelligent Optics and Photonics Research Center, Jiaxing Institute Zhejiang University, Jiaxing, China
| | - Linjun Li
- State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China.
- Intelligent Optics and Photonics Research Center, Jiaxing Institute Zhejiang University, Jiaxing, China.
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43
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Aguirre F, Sebastian A, Le Gallo M, Song W, Wang T, Yang JJ, Lu W, Chang MF, Ielmini D, Yang Y, Mehonic A, Kenyon A, Villena MA, Roldán JB, Wu Y, Hsu HH, Raghavan N, Suñé J, Miranda E, Eltawil A, Setti G, Smagulova K, Salama KN, Krestinskaya O, Yan X, Ang KW, Jain S, Li S, Alharbi O, Pazos S, Lanza M. Hardware implementation of memristor-based artificial neural networks. Nat Commun 2024; 15:1974. [PMID: 38438350 PMCID: PMC10912231 DOI: 10.1038/s41467-024-45670-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 02/01/2024] [Indexed: 03/06/2024] Open
Abstract
Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.
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Affiliation(s)
- Fernando Aguirre
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193, Barcelona, Spain
| | | | | | - Wenhao Song
- Department of Electrical and Computer Engineering, University of Southern California (USC), Los Angeles, CA, 90089, USA
| | - Tong Wang
- Department of Electrical and Computer Engineering, University of Southern California (USC), Los Angeles, CA, 90089, USA
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Southern California (USC), Los Angeles, CA, 90089, USA
| | - Wei Lu
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Meng-Fan Chang
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 30013, Taiwan
| | - Daniele Ielmini
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IUNET, Piazza L. da Vinci 32, 20133, Milano, Italy
| | - Yuchao Yang
- School of Electronic and Computer Engineering, Peking University, Shenzhen, China
| | - Adnan Mehonic
- Department of Electronic and Electrical Engineering, University College London (UCL), Torrington Place, WC1E 7JE, London, UK
| | - Anthony Kenyon
- Department of Electronic and Electrical Engineering, University College London (UCL), Torrington Place, WC1E 7JE, London, UK
| | - Marco A Villena
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Juan B Roldán
- Departamento de Electrónica y Tecnología de Computadores, Facultad de Ciencias, Universidad de Granada, Avenida Fuentenueva s/n, 18071, Granada, Spain
| | - Yuting Wu
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Hung-Hsi Hsu
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 30013, Taiwan
| | - Nagarajan Raghavan
- Engineering Product Development (EPD) Pillar, Singapore University of Technology & Design, 8 Somapah Road, 487372, Singapore, Singapore
| | - Jordi Suñé
- Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193, Barcelona, Spain
| | - Enrique Miranda
- Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193, Barcelona, Spain
| | - Ahmed Eltawil
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Gianluca Setti
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Kamilya Smagulova
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Khaled N Salama
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Olga Krestinskaya
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Xiaobing Yan
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding, 071002, China
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, College of Design and Engineering, National University of Singapore (NUS), Singapore, Singapore
| | - Samarth Jain
- Department of Electrical and Computer Engineering, College of Design and Engineering, National University of Singapore (NUS), Singapore, Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, College of Design and Engineering, National University of Singapore (NUS), Singapore, Singapore
| | - Osamah Alharbi
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Sebastian Pazos
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Mario Lanza
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
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Yu T, Wang D, Liu M, Lei W, Shafie S, Mohtar MN, Jindapetch N, van Paphavee D, Zhao Z. A carbon conductive filament-induced robust resistance switching behavior for brain-inspired computing. MATERIALS HORIZONS 2024; 11:1334-1343. [PMID: 38175571 DOI: 10.1039/d3mh01762a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Memristors have revolutionized the path forward for brain-inspired computing. However, the instability of the nucleation process of conductive filaments based on active metal electrodes leads to the discrete distribution of switching parameters, which hinders the realization of high-performance and low-power devices for neuromorphic computing. In response, a carbon conductive filament-induced robust memristor is demonstrated with variation coefficients as low as 3.9%/-1.18%, a threshold power as low as 10-9 W, and 3 × 106 s retention and 107 cycle endurance behaviors can be maintained. The recognition accuracy for Modified National Institute of Standards and Technology (MNIST) handwriting is as high as 96.87%, attributed to the high linearity of the iterative updating of synaptic weights. The demodulation and storage functions of the American Standard Code for Information Interchange (ASCII) are demonstrated by programmable pulse modulation. Notably, the transmission electron microscopy (TEM) images allow the observation of carbon conductive filament paths formed in the low resistance state. First-principles calculations analyze the energetics of defects involved in the diffusion of carbon atoms into MoTe2 films. This work presents a novel guideline for studying memristor-based neuromorphic computing.
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Affiliation(s)
- Tianqi Yu
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China.
| | - Dong Wang
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China.
| | - Min Liu
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China.
| | - Wei Lei
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China.
| | - Suhaidi Shafie
- Institute of Nanoscience and Nanotechnology, University Putra Malaysia, Serdang, Selangor, Malaysia
| | - Mohd Nazim Mohtar
- Institute of Nanoscience and Nanotechnology, University Putra Malaysia, Serdang, Selangor, Malaysia
| | - Nattha Jindapetch
- Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla, 90112, Thailand
| | - Dommelen van Paphavee
- Division of Physical Science, Faculty of Science, Prince of Songkla University, Hat Yai Campus 15 Karnjanavanich, Hat Yai, Kohong District Songkhla, 90110, Thailand
| | - Zhiwei Zhao
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China.
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Wang J, Ilyas N, Ren Y, Ji Y, Li S, Li C, Liu F, Gu D, Ang KW. Technology and Integration Roadmap for Optoelectronic Memristor. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307393. [PMID: 37739413 DOI: 10.1002/adma.202307393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/10/2023] [Indexed: 09/24/2023]
Abstract
Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.
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Affiliation(s)
- Jinyong Wang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Nasir Ilyas
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Yujing Ren
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, 117585, Singapore
| | - Yun Ji
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Changcun Li
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Fucai Liu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Deen Gu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
- Institute of Materials Research and Engineering, A*STAR, Singapore, 138634, Singapore
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46
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An YJ, Yan H, Yeom CM, Jeong JK, Eadi SB, Lee HD, Kwon HM. Effects of thermal annealing on analog resistive switching behavior in bilayer HfO 2/ZnO synaptic devices: the role of ZnO grain boundaries. NANOSCALE 2024; 16:4609-4619. [PMID: 38258994 DOI: 10.1039/d3nr04917e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The effects of thermal annealing on analog resistive switching behavior in bilayer HfO2/ZnO synaptic devices were investigated. The annealed active ZnO layer between the top Pd electrode and the HfO2 layer exhibited electroforming-free resistive switching. In particular, the switching uniformity, stability, and reliability of the synaptic devices were dramatically improved via thermal annealing at 600 °C atomic force microscopy and X-ray diffraction analyses revealed that active ZnO films demonstrated increased grain size upon annealing from 400 °C to 700 °C, whereas the ZnO film thickness and the annealing of the HfO2 layer in bilayer HfO2/ZnO synaptic devices did not profoundly affect the analog switching behavior. The optimized thermal annealing at 600 °C in bilayer HfO2/ZnO synaptic devices dramatically improved the nonlinearity of long-term potentiation/depression properties, the relative coefficient of variation of the asymmetry distribution σ/μ, and the asymmetry ratio, which approached 1. The results offer valuable insights into the implementation of highly robust synaptic devices in neural networks.
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Affiliation(s)
- Yeong-Jin An
- Department of Electronics Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea.
| | - Han Yan
- Department of Electronics Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea.
| | - Chae-Min Yeom
- Department of Electronics Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea.
| | - Jun-Kyo Jeong
- Department of Electronics Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea.
| | - Sunil Babu Eadi
- Department of Electronics Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea.
| | - Hi-Deok Lee
- Department of Electronics Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea.
| | - Hyuk-Min Kwon
- Department of Semiconductor Processing Equipment, Semiconductor Convergence Campus of Korea Polytechnic College, Anseong, Kyunggi-Do, 17550, Republic of Korea.
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47
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Liu X, Sun C, Ye X, Zhu X, Hu C, Tan H, He S, Shao M, Li RW. Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2311472. [PMID: 38421081 DOI: 10.1002/adma.202311472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/06/2024] [Indexed: 03/02/2024]
Abstract
Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.
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Affiliation(s)
- Xuerong Liu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Cui Sun
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Xiaoyu Ye
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Xiaojian Zhu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Cong Hu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Hongwei Tan
- Department of Applied Physics, Aalto University, Aalto, FI-00076, Finland
| | - Shang He
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Mengjie Shao
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Run-Wei Li
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
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48
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Qiu J, Li J, Li W, Wang K, Xiao T, Su H, Suk CH, Zhou X, Zhang Y, Guo T, Wu C, Ooi PC, Kim TW. Silver Nanowire Networks with Moisture-Enhanced Learning Ability. ACS APPLIED MATERIALS & INTERFACES 2024; 16:10361-10371. [PMID: 38362885 DOI: 10.1021/acsami.3c17438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
The human brain possesses a remarkable ability to memorize information with the assistance of a specific external environment. Therefore, mimicking the human brain's environment-enhanced learning abilities in artificial electronic devices is essential but remains a considerable challenge. Here, a network of Ag nanowires with a moisture-enhanced learning ability, which can mimic long-term potentiation (LTP) synaptic plasticity at an ultralow operating voltage as low as 0.01 V, is presented. To realize a moisture-enhanced learning ability and to adjust the aggregations of Ag ions, we introduced a thin polyvinylpyrrolidone (PVP) coating layer with moisture-sensitive properties to the surfaces of the Ag nanowires of Ag ions. That Ag nanowire network was shown to exhibit, in response to the humidity of its operating environment, different learning speeds during the LTP process. In high-humidity environments, the synaptic plasticity was significantly strengthened with a higher learning speed compared with that in relatively low-humidity environments. Based on experimental and simulation results, we attribute this enhancement to the higher electric mobility of the Ag ions in the water-absorbed PVP layer. Finally, we demonstrated by simulation that the moisture-enhanced synaptic plasticity enabled the device to adjust connection weights and delivery modes based on various input patterns. The recognition rate of a handwritten data set reached 94.5% with fewer epochs in a high-humidity environment. This work shows the feasibility of building our electronic device to achieve artificial adaptive learning abilities.
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Affiliation(s)
- Jiawen Qiu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Junlong Li
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Wenhao Li
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Kun Wang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Tianyu Xiao
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Hao Su
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
| | - Chan Hee Suk
- Department of Electronic and Computer Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Xiongtu Zhou
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350108, China
| | - Yongai Zhang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350108, China
| | - Tailiang Guo
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350108, China
| | - Chaoxing Wu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350108, China
| | - Poh Choon Ooi
- Institute of Microengineering and Nanoelectronics (IMEN), University Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
| | - Tae Whan Kim
- Department of Electronic and Computer Engineering, Hanyang University, Seoul 04763, Republic of Korea
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49
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Roldán JB, Cantudo A, Maldonado D, Aguilera-Pedregosa C, Moreno E, Swoboda T, Jiménez-Molinos F, Yuan Y, Zhu K, Lanza M, Muñoz Rojo M. Thermal Compact Modeling and Resistive Switching Analysis in Titanium Oxide-Based Memristors. ACS APPLIED ELECTRONIC MATERIALS 2024; 6:1424-1433. [PMID: 38435806 PMCID: PMC10903745 DOI: 10.1021/acsaelm.3c01727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/01/2024] [Accepted: 02/01/2024] [Indexed: 03/05/2024]
Abstract
Resistive switching devices based on the Au/Ti/TiO2/Au stack were developed. In addition to standard electrical characterization by means of I-V curves, scanning thermal microscopy was employed to localize the hot spots on the top device surface (linked to conductive nanofilaments, CNFs) and perform in-operando tracking of temperature in such spots. In this way, electrical and thermal responses can be simultaneously recorded and related to each other. In a complementary way, a model for device simulation (based on COMSOL Multiphysics) was implemented in order to link the measured temperature to simulated device temperature maps. The data obtained were employed to calculate the thermal resistance to be used in compact models, such as the Stanford model, for circuit simulation. The thermal resistance extraction technique presented in this work is based on electrical and thermal measurements instead of being indirectly supported by a single fitting of the electrical response (using just I-V curves), as usual. Besides, the set and reset voltages were calculated from the complete I-V curve resistive switching series through different automatic numerical methods to assess the device variability. The series resistance was also obtained from experimental measurements, whose value is also incorporated into a compact model enhanced version.
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Affiliation(s)
- Juan B. Roldán
- Departamento
de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias. Avenida Fuentenueva s/n, 18071 Granada, Spain
| | - Antonio Cantudo
- Departamento
de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias. Avenida Fuentenueva s/n, 18071 Granada, Spain
| | - David Maldonado
- Departamento
de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias. Avenida Fuentenueva s/n, 18071 Granada, Spain
- IHP-Leibniz-Institut
für innovative Mikroelektronik, 15236 Frankfurt (Oder), Germany
| | - Cristina Aguilera-Pedregosa
- Departamento
de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias. Avenida Fuentenueva s/n, 18071 Granada, Spain
| | - Enrique Moreno
- CEMDATIC—E.T.S.I
Telecomunicación, Universidad Politécnica
de Madrid (UPM), 28040 Madrid, Spain
| | - Timm Swoboda
- Department
of Thermal and Fluid Engineering, Faculty of Engineering Technology, University of Twente, 7500 AE Enschede, The Netherlands
| | - Francisco Jiménez-Molinos
- Departamento
de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias. Avenida Fuentenueva s/n, 18071 Granada, Spain
| | - Yue Yuan
- Materials
Science and Engineering Program, Physical Sciences and Engineering
Division, King Abdullah University of Science
and Technology (KAUST), Thuwal 23955-6900, Saudi
Arabia
| | - Kaichen Zhu
- MIND, Department
of Electronic and Biomedical Engineering, Universitat de Barcelona, Martí i Franquès 1, E-08028 Barcelona, Spain
| | - Mario Lanza
- Materials
Science and Engineering Program, Physical Sciences and Engineering
Division, King Abdullah University of Science
and Technology (KAUST), Thuwal 23955-6900, Saudi
Arabia
| | - Miguel Muñoz Rojo
- Department
of Thermal and Fluid Engineering, Faculty of Engineering Technology, University of Twente, 7500 AE Enschede, The Netherlands
- 2D
Foundry, Instituto de Ciencia de Materiales
de Madrid (ICMM), CSIC, Madrid 28049, Spain
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Maldonado D, Cantudo A, Gómez-Campos FM, Yuan Y, Shen Y, Zheng W, Lanza M, Roldán JB. 3D simulation of conductive nanofilaments in multilayer h-BN memristors via a circuit breaker approach. MATERIALS HORIZONS 2024; 11:949-957. [PMID: 38105726 DOI: 10.1039/d3mh01834b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
A 3D simulation of conductive nanofilaments (CNFs) in multilayer hexagonal-BN memristors is performed. To do so, a simulation tool based on circuit breakers is developed including for the first time a 3D resistive network. The circuit breakers employed can be modeled with two, three and four resistance states; in addition, a series resistance and a module to account for quantum effects, by means of the quantum point contact model, are also included. Finally, to describe real dielectric situations, regions with a high defect density are modeled with a great variety of geometrical shapes to consider their influence in the resistive switching (RS) process. The simulator has been tuned with measurements of h-BN memristive devices, fabricated with chemical-vapour-deposition grown h-BN layers, which were electrically and physically characterized. We show the formation of CNFs that produce filamentary charge conduction in our devices. Moreover, the simulation tool is employed to describe partial filament rupture in reset processes and show the low dependence of the set voltage on the device area, which is seen experimentally.
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Affiliation(s)
- D Maldonado
- Departamento de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias, Avd. Fuentenueva s/n, 18071 Granada, Spain.
| | - A Cantudo
- Departamento de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias, Avd. Fuentenueva s/n, 18071 Granada, Spain.
| | - F M Gómez-Campos
- Departamento de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias, Avd. Fuentenueva s/n, 18071 Granada, Spain.
| | - Yue Yuan
- Materials Science and Engineering Program, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
| | - Yaqing Shen
- Materials Science and Engineering Program, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
| | - Wenwen Zheng
- Materials Science and Engineering Program, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
| | - M Lanza
- Materials Science and Engineering Program, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
| | - J B Roldán
- Departamento de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias, Avd. Fuentenueva s/n, 18071 Granada, Spain.
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