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Ren S, Wang K, Jia X, Wang J, Xu J, Yang B, Tian Z, Xia R, Yu D, Jia Y, Yan X. Fibrous MXene Synapse-Based Biomimetic Tactile Nervous System for Multimodal Perception and Memory. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2400165. [PMID: 38329189 DOI: 10.1002/smll.202400165] [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: 01/08/2024] [Revised: 01/19/2024] [Indexed: 02/09/2024]
Abstract
Biomimetic tactile nervous system (BTNS) inspired by organisms has motivated extensive attention in wearable fields due to its biological similarity, low power consumption, and perception-memory integration. Though many works about planar-shape BTNS are developed, few researches could be found in the field of fibrous BTNS (FBTNS) which is superior in terms of strong flexibility, weavability, and high-density integration. Herein, a FBTNS with multimodal sensibility and memory is proposed, by fusing the fibrous poly lactic acid (PLA)/Ag/MXene/Pt artificial synapse and MXene/EMIMBF4 ionic conductive elastomer. The proposed FBTNS can successfully perceive external stimuli and generate synaptic responses. It also exhibits a short response time (23 ms) and low set power consumption (17 nW). Additionally, the proposed device demonstrates outstanding synaptic plasticity under both mechanical and electrical stimuli, which can simulate the memory function. Simultaneously, the fibrous devices are embedded into textiles to construct tactile arrays, by which biomimetic tactile perception and temporary memory functions are successfully implemented. This work demonstrates the as-prepared FBTNS can generate biomimetic synaptic signals to serve as artificial feeling signals, it is thought that it could offer a fabric electronic unit integrating with perception and memory for Human-Computer interaction, and has great potential to build lightweight and comfortable Brain-Computer interfaces.
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Affiliation(s)
- Shuhui Ren
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300071, P. R. China
| | - Kaiyang Wang
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300071, P. R. China
| | - Xiaotong Jia
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300071, P. R. China
| | - Jiuyang Wang
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300071, P. R. China
| | - Jikang Xu
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Biao Yang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Ziwei Tian
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300071, P. R. China
| | - Ruoxuan Xia
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300071, P. R. China
| | - Ding Yu
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300071, P. R. China
| | - Yunfang Jia
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300071, P. R. China
| | - Xiaobing Yan
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
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Fang J, Tang Z, Lai XC, Qiu F, Jiang YP, Liu QX, Tang XG, Sun QJ, Zhou YC, Fan JM, Gao J. New-Style Logic Operation and Neuromorphic Computing Enabled by Optoelectronic Artificial Synapses in an MXene/Y:HfO 2 Ferroelectric Memristor. ACS APPLIED MATERIALS & INTERFACES 2024; 16:31348-31362. [PMID: 38833382 DOI: 10.1021/acsami.4c05316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
Today's computing systems, to meet the enormous demands of information processing, have driven the development of brain-inspired neuromorphic systems. However, there are relatively few optoelectronic devices in most brain-inspired neuromorphic systems that can simultaneously regulate the conductivity through both optical and electrical signals. In this work, the Au/MXene/Y:HfO2/FTO ferroelectric memristor as an optoelectronic artificial synaptic device exhibited both digital and analog resistance switching (RS) behaviors under different voltages with a good switching ratio (>103). Under optoelectronic conditions, optimal weight update parameters and an enhanced algorithm achieved 97.1% recognition accuracy in convolutional neural networks. A new logic gate circuit specifically designed for optoelectronic inputs was established. Furthermore, the device integrates the impact of relative humidity to develop an innovative three-person voting mechanism with a veto power. These results provide a feasible approach for integrating optoelectronic artificial synapses with logic-based computing devices.
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Affiliation(s)
- Junlin Fang
- School of Physics and Optoelectric Engineering, Guangzhou Higher Education Mega Center, Guangdong University of Technology, Guangzhou 510006, P. R. China
| | - Zhenhua Tang
- School of Physics and Optoelectric Engineering, Guangzhou Higher Education Mega Center, Guangdong University of Technology, Guangzhou 510006, P. R. China
| | - Xi-Cai Lai
- School of Physics and Optoelectric Engineering, Guangzhou Higher Education Mega Center, Guangdong University of Technology, Guangzhou 510006, P. R. China
| | - Fan Qiu
- School of Physics and Optoelectric Engineering, Guangzhou Higher Education Mega Center, Guangdong University of Technology, Guangzhou 510006, P. R. China
| | - Yan-Ping Jiang
- School of Physics and Optoelectric Engineering, Guangzhou Higher Education Mega Center, Guangdong University of Technology, Guangzhou 510006, P. R. China
| | - Qiu-Xiang Liu
- School of Physics and Optoelectric Engineering, Guangzhou Higher Education Mega Center, Guangdong University of Technology, Guangzhou 510006, P. R. China
| | - Xin-Gui Tang
- School of Physics and Optoelectric Engineering, Guangzhou Higher Education Mega Center, Guangdong University of Technology, Guangzhou 510006, P. R. China
| | - Qi-Jun Sun
- School of Physics and Optoelectric Engineering, Guangzhou Higher Education Mega Center, Guangdong University of Technology, Guangzhou 510006, P. R. China
| | - Yi-Chun Zhou
- School of Advanced Materials and Nanotechnology, Xidian University, Xian 710126, China
| | - Jing-Min Fan
- School of Automation, Guangdong University of Technology, Guangzhou 510006, China
| | - Ju Gao
- Department of Physics, The University of Hong Kong, Hong Kong 999077, P. R. China
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Yue J, Zou L, Bai N, Zhu C, Yi Y, Xue F, Sun H, Hu S, Cheng W, He Q, Lu H, Ye L, Miao X. Synapse Neurotransmitter Channel-Inspired AlO x Memristor with "V" Type Oxygen Vacancy Distribution. SMALL METHODS 2024:e2301657. [PMID: 38708670 DOI: 10.1002/smtd.202301657] [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: 04/18/2024] [Indexed: 05/07/2024]
Abstract
Memristor possesses great potential and advantages in neuromorphic computing, while consistency and power consumption issues have been hindering its commercialization. Low cost and accuracy are the advantages of human brain, so memristors can be used to construct brain-like synaptic devices to solve these problems. In this work, a five-layer AlOx device with a V-shaped oxygen distribution is used to simulate biological synapses. The device simulates synapse structurally. Further, under electrical stimulation, O2- moves to the Ti electrode and oxygen vacancy (Vo) moves to the Pt electrode, thus forming a conductive filament (CF), which simulates the Ca2+ flow and releases neurotransmitters to the postsynaptic membrane, thus realizing the transmission of information. By controlling applied voltage, the regulation of Ca2+ gated pathway is realized to control the Ca2+ internal flow and achieve different degrees of information transmission. Long-term Potentiation (LTP)/Long-term Depression (LTD), Spike Timing Dependent Plasticity (STDP), these basic synaptic performances can be simulated. The AlOx device realizes low power consumption (56.7 pJ/392 fJ), high switching speed (25 ns/60 ns), and by adjusting the window value, the nonlinearity is improved (0.133/0.084), a high recognition accuracy (98.18%) is obtained in neuromorphic simulation. It shows a great prospect in multi-value storage and neuromorphic computing.
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Affiliation(s)
- Junlin Yue
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Lanqing Zou
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Na Bai
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Chuqian Zhu
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yunhui Yi
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Fan Xue
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Huajun Sun
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan, 430205, China
| | - Shane Hu
- Wuhan Xinxin Semiconductor Manufacturing Corporation, Wuhan, 430205, China
| | - Weiming Cheng
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan, 430205, China
| | - Qiang He
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Hong Lu
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Lei Ye
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan, 430205, China
| | - Xiangshui Miao
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan, 430205, China
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Zhang H, Qiu P, Lu Y, Ju X, Chi D, Yew KS, Zhu M, Wang S, Wei R, Hu W. In-Sensor Computing Realization Using Fully CMOS-Compatible TiN/HfO x-Based Neuristor Array. ACS Sens 2023; 8:3873-3881. [PMID: 37707324 DOI: 10.1021/acssensors.3c01418] [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: 09/15/2023]
Abstract
With the evolution of artificial intelligence, the explosive growth of data from sensory terminals gives rise to severe energy-efficiency bottleneck issues due to cumbersome data interactions among sensory, memory, and computing modules. Heterogeneous integration methods such as chiplet technology can significantly reduce unnecessary data movement; however, they fail to address the fundamental issue of the substantial time and energy overheads resulting from the physical separation of computing and sensory components. Brain-inspired in-sensor neuromorphic computing (ISNC) has plenty of room for such data-intensive applications. However, one key obstacle in developing ISNC systems is the lack of compatibility between material systems and manufacturing processes deployed in sensors and computing units. This study successfully addresses this challenge by implementing fully CMOS-compatible TiN/HfOx-based neuristor array. The developed ISNC system demonstrates several advantageous features, including multilevel analogue modulation, minimal dispersion, and no significant degradation in conductance (@125 °C). These characteristics enable stable and reproducible neuromorphic computing. Additionally, the device exhibits modulatable sensory and multi-store memory processes. Furthermore, the system achieves information recognition with a high accuracy rate of 93%, along with frequency selectivity and notable activity-dependent plasticity. This work provides a promising route to affordable and highly efficient sensory neuromorphic systems.
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Affiliation(s)
- Haizhong Zhang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Peng Qiu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
| | - Yaoping Lu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
| | - Xin Ju
- Institute of Materials Research and Engineering, 2 Fusionopolis Way, Innovis, #08-03, Agency for Science, Technology and Research, Singapore 138634, Singapore
| | - Dongzhi Chi
- Institute of Materials Research and Engineering, 2 Fusionopolis Way, Innovis, #08-03, Agency for Science, Technology and Research, Singapore 138634, Singapore
| | - Kwang Sing Yew
- Global Foundries, 60 Woodlands Industrial Park D Street 2, Singapore 738406, Singapore
| | - Minmin Zhu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Shaohao Wang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Rongshan Wei
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
- FZU-Jinjiang Joint Institute of Microelectronics, Jinjiang Science and Education Park, Fuzhou University, Jinjiang 362200, China
| | - Wei Hu
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
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Li J, Abbas H, Ang DS, Ali A, Ju X. Emerging memristive artificial neuron and synapse devices for the neuromorphic electronics era. NANOSCALE HORIZONS 2023; 8:1456-1484. [PMID: 37615055 DOI: 10.1039/d3nh00180f] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Abstract
Growth of data eases the way to access the world but requires increasing amounts of energy to store and process. Neuromorphic electronics has emerged in the last decade, inspired by biological neurons and synapses, with in-memory computing ability, extenuating the 'von Neumann bottleneck' between the memory and processor and offering a promising solution to reduce the efforts both in data storage and processing, thanks to their multi-bit non-volatility, biology-emulated characteristics, and silicon compatibility. This work reviews the recent advances in emerging memristive devices for artificial neuron and synapse applications, including memory and data-processing ability: the physics and characteristics are discussed first, i.e., valence changing, electrochemical metallization, phase changing, interfaced-controlling, charge-trapping, ferroelectric tunnelling, and spin-transfer torquing. Next, we propose a universal benchmark for the artificial synapse and neuron devices on spiking energy consumption, standby power consumption, and spike timing. Based on the benchmark, we address the challenges, suggest the guidelines for intra-device and inter-device design, and provide an outlook for the neuromorphic applications of resistive switching-based artificial neuron and synapse devices.
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Affiliation(s)
- Jiayi Li
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
| | - Haider Abbas
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
| | - Diing Shenp Ang
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
| | - Asif Ali
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
| | - Xin Ju
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore 138634
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Xie D, Yin K, Yang ZJ, Huang H, Li X, Shu Z, Duan H, He J, Jiang J. Polarization-perceptual anisotropic two-dimensional ReS 2 neuro-transistor with reconfigurable neuromorphic vision. MATERIALS HORIZONS 2022; 9:1448-1459. [PMID: 35234765 DOI: 10.1039/d1mh02036f] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Polarization is a common and unique phenomenon in nature, which reveals more camouflage features of objects. However, current polarization-perceptual devices based on conventional physical architectures face enormous challenges for high-performance computation due to the traditional von Neumann bottleneck. In this work, a novel polarization-perceptual neuro-transistor with reconfigurable anisotropic vision is proposed based on a two-dimensional ReS2 phototransistor. The device exhibits excellent photodetection ability and superior polarization sensitivity due to its direct band gap semiconductor property and strong anisotropic crystal structure, respectively. The fascinating polarization-sensitive neuromorphic behavior, such as polarization memory consolidation and reconfigurable visual imaging, are successfully realized. In particular, the regulated polarization responsivity and dichroic ratio are successfully emulated through our artificial compound eyes. More importantly, two intriguing polarization-perceptual applications for polarized navigation with reconfigurable adaptive learning abilities and three-dimensional visual polarization imaging are also experimentally demonstrated. The proposed device may provide a promising opportunity for future polarization perception systems in intelligent humanoid robots and autonomous vehicles.
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Affiliation(s)
- Dingdong Xie
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, 932 South Lushan Road, Changsha, Hunan 410083, P. R. China.
| | - Kai Yin
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, 932 South Lushan Road, Changsha, Hunan 410083, P. R. China.
| | - Zhong-Jian Yang
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, 932 South Lushan Road, Changsha, Hunan 410083, P. R. China.
| | - Han Huang
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, 932 South Lushan Road, Changsha, Hunan 410083, P. R. China.
| | - Xiaohui Li
- School of Physics and Information Technology, Shanxi Normal University, Xi'an 710119, P. R. China
| | - Zhiwen Shu
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, P. R. China
| | - Huigao Duan
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, P. R. China
| | - Jun He
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, 932 South Lushan Road, Changsha, Hunan 410083, P. R. China.
| | - Jie Jiang
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, 932 South Lushan Road, Changsha, Hunan 410083, P. R. China.
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Wang L, Wang Y, Wen D. Tunable biological nonvolatile multilevel data storage devices. Phys Chem Chem Phys 2021; 23:24834-24841. [PMID: 34719695 DOI: 10.1039/d1cp04622e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The speed with which electronic products are updated is continuously increasing. Consequently, since waste electronic products can cause serious environmental pollution, the demand for electronic products made of biological materials is becoming increasingly urgent. Although biological memristors have significant advantages, their electrical characteristics still do not meet the requirements to be used in future nonvolatile memories. Therefore, how to control their electrical characteristics has become a popular topic of research. In this study, tunable biomemristors with an Al/tussah blood (TB)-carbon nanotube (CNT)/indium tin oxide (ITO)/glass structure were fabricated. Such a device exhibits stable bipolar resistance switching behavior and good retention characteristics (104 s). Experimental results show that the ON/OFF current ratio can be effectively controlled by modifying the CNT concentration in the TB-CNT composite film. Multilevel (8 levels, 3 bits per cell) storage capabilities can be achieved in the device by controlling its compliance current in order to achieve high-density storage. The resistance switching behavior originates from the formation and rupture of conductive oxygen vacancy filaments. TB is a promising natural biomaterial in the field of green electronics, and this research could blaze a new trail for the development of biological memory devices. Biomemristors with multilevel resistance states can be used as electronic synapses and are one of the choices for simulating biological synapses.
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Affiliation(s)
- Lu Wang
- School of Electronic Engineering, Heilongjiang University, Harbin, 150080, China. .,HLJ Province Key Laboratory of Senior-Education for Electronic Engineering, Heilongjiang University, Harbin, 150080, China
| | - Yuting Wang
- School of Electronic Engineering, Heilongjiang University, Harbin, 150080, China. .,HLJ Province Key Laboratory of Senior-Education for Electronic Engineering, Heilongjiang University, Harbin, 150080, China
| | - Dianzhong Wen
- School of Electronic Engineering, Heilongjiang University, Harbin, 150080, China. .,HLJ Province Key Laboratory of Senior-Education for Electronic Engineering, Heilongjiang University, Harbin, 150080, China
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Wang L, Zhu H, Wen D. Bioresistive Random-Access Memory with Gold Nanoparticles that Generate the Coulomb Blocking Effect Can Realize Multilevel Data Storage and Synapse Simulation. J Phys Chem Lett 2021; 12:8956-8962. [PMID: 34505773 DOI: 10.1021/acs.jpclett.1c02815] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Gold nanoparticles (Au NPs) have good biocompatibility and special quantum effects. In this Letter, we embedded Au NPs into silkworm hemolymph (SH) to improve the performance of the device and fabricated Al/SH:Au NPs/indium tin oxide (ITO)/glass resistive random access memory. The device exhibits a bipolar switching behavior with a retention time of 104 s. Compared with the Al/SH/ITO device without Au NPs, the device has a higher ON/OFF current ratio (>105) and a smaller Vreset. The improvement in device performance is attributed to the fact that Au NPs act as the electron-trapping center in the device; a Coulomb blockade occurs after electrons are trapped, thereby increasing the resistance of the device in the high-resistance state. Using optimized devices can realize multilevel data storage and can also simulate synaptic characteristics such as potentiation and depression. The device is expected to be applied to high-density, low-cost, degradable, and biocompatible storage devices and neuromorphic computing in the future.
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Affiliation(s)
- Lu Wang
- School of Electronic Engineering, HLJ Province Key Laboratory of Senior-Education for Electronic Engineering, Heilongjiang University, Harbin 150080, China
| | - Hongyu Zhu
- School of Electronic Engineering, HLJ Province Key Laboratory of Senior-Education for Electronic Engineering, Heilongjiang University, Harbin 150080, China
| | - Dianzhong Wen
- School of Electronic Engineering, HLJ Province Key Laboratory of Senior-Education for Electronic Engineering, Heilongjiang University, Harbin 150080, China
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Wang J, Zhuge X, Zhuge F. Hybrid oxide brain-inspired neuromorphic devices for hardware implementation of artificial intelligence. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2021; 22:326-344. [PMID: 34025215 PMCID: PMC8128179 DOI: 10.1080/14686996.2021.1911277] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
The state-of-the-art artificial intelligence technologies mainly rely on deep learning algorithms based on conventional computers with classical von Neumann computing architectures, where the memory and processing units are separated resulting in an enormous amount of energy and time consumed in the data transfer process. Inspired by the human brain acting like an ultra-highly efficient biological computer, neuromorphic computing is proposed as a technology for hardware implementation of artificial intelligence. Artificial synapses are the main component of a neuromorphic computing architecture. Memristors are considered to be a relatively ideal candidate for artificial synapse applications due to their high scalability and low power consumption. Oxides are most widely used in memristors due to the ease of fabrication and high compatibility with complementary metal-oxide-semiconductor processes. However, oxide memristors suffer from unsatisfactory stability and reliability. Oxide-based hybrid structures can effectively improve the device stability and reliability, therefore providing a promising prospect for the application of oxide memristors to neuromorphic computing. This work reviews the recent advances in the development of hybrid oxide memristive synapses. The discussion is organized according to the blending schemes as well as the working mechanisms of hybrid oxide memristors.
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Affiliation(s)
- Jingrui Wang
- School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Xia Zhuge
- School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo, China
| | - Fei Zhuge
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- CONTACT Fei Zhuge Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo315201, China
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Kim M, Yoo K, Jeon SP, Park SK, Kim YH. The Effect of Multi-Layer Stacking Sequence of TiO x Active Layers on the Resistive-Switching Characteristics of Memristor Devices. MICROMACHINES 2020; 11:mi11020154. [PMID: 32019257 PMCID: PMC7074605 DOI: 10.3390/mi11020154] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 01/26/2020] [Accepted: 01/29/2020] [Indexed: 11/24/2022]
Abstract
The oxygen vacancies in the TiOx active layer play the key role in determining the electrical characteristics of TiOx–based memristors such as resistive-switching behaviour. In this paper, we investigated the effect of a multi-layer stacking sequence of TiOx active layers on the resistive-switching characteristics of memristor devices. In particular, the stacking sequence of the multi-layer TiOx sub-layers, which have different oxygen contents, was varied. The optimal stacking sequence condition was confirmed by measuring the current–voltage characteristics, and also the retention test confirmed that the characteristics were maintained for more than 10,000 s. Finally, the simulation using the Modified National Institute of Standards and Technology handwriting recognition data set revealed that the multi-layer TiOx memristors showed a learning accuracy of 89.18%, demonstrating the practical utilization of the multi-layer TiOx memristors in artificial intelligence systems.
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Affiliation(s)
- Minho Kim
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon 16419, Korea; (M.K.); (K.Y.)
| | - Kungsang Yoo
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon 16419, Korea; (M.K.); (K.Y.)
| | - Seong-Pil Jeon
- School of Electrical and Electronic Engineering, Chung-Ang University, Seoul 06980, Korea;
| | - Sung Kyu Park
- School of Electrical and Electronic Engineering, Chung-Ang University, Seoul 06980, Korea;
- Correspondence: (S.K.P.); (Y.-H.K.); Tel.: +82-2-820-5347 (S.K.P.); +82-31-290-7407 (Y.-H.K.)
| | - Yong-Hoon Kim
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon 16419, Korea; (M.K.); (K.Y.)
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Korea
- Correspondence: (S.K.P.); (Y.-H.K.); Tel.: +82-2-820-5347 (S.K.P.); +82-31-290-7407 (Y.-H.K.)
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