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Li Y, Qiu Z, Kan H, Yang Y, Liu J, Liu Z, Yue W, Du G, Wang C, Kim N. A Human-Computer Interaction Strategy for An FPGA Platform Boosted Integrated "Perception-Memory" System Based on Electronic Tattoos and Memristors. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2402582. [PMID: 39049180 PMCID: PMC11497050 DOI: 10.1002/advs.202402582] [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: 03/13/2024] [Revised: 06/12/2024] [Indexed: 07/27/2024]
Abstract
The integrated "perception-memory" system is receiving increasing attention due to its crucial applications in humanoid robots, as well as in the simulation of the human retina and brain. Here, a Field Programmable Gate Array (FPGA) platform-boosted system that enables the sensing, recognition, and memory for human-computer interaction is reported by the combination of ultra-thin Ag/Al/Paster-based electronic tattoos (AAP) and Tantalum Oxide/Indium Gallium Zinc Oxide (Ta2O5/IGZO)-based memristors. Notably, the AAP demonstrates exceptional capabilities in accommodating the strain caused by skin deformation, thanks to its unique structural design, which ensures a secure fit to the skin and enables the prolonged monitoring of physiological signals. By utilizing Ta2O5/IGZO as the functional layer, a high switching ratio is conferred to the memristor, and an integrated system for sensing, distinguishing, storing, and controlling the machine hand of multiple human physiological signals is constructed together with the AAP. Further, the proposed system implements emergency calls and smart homes using facial electromyogram signals and utilizing logical entailment to realize the control of the music interface. This innovative "perception-memory" integrated system not only serves the disabled, enhancing human-computer interaction but also provides an alternative avenue to enhance the quality of life and autonomy of individuals with disabilities.
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Affiliation(s)
- Yang Li
- Shandong Provincial Key Laboratory of Network Based Intelligent ComputingSchool of Information Science and EngineeringUniversity of JinanJinan250022China
- School of Integrated CircuitsShandong UniversityJinan250101China
| | - Zhicheng Qiu
- Shandong Provincial Key Laboratory of Network Based Intelligent ComputingSchool of Information Science and EngineeringUniversity of JinanJinan250022China
| | - Hao Kan
- Shandong Provincial Key Laboratory of Network Based Intelligent ComputingSchool of Information Science and EngineeringUniversity of JinanJinan250022China
| | - Yang Yang
- Shandong Provincial Key Laboratory of Network Based Intelligent ComputingSchool of Information Science and EngineeringUniversity of JinanJinan250022China
| | - Jianwen Liu
- Shandong Provincial Key Laboratory of Network Based Intelligent ComputingSchool of Information Science and EngineeringUniversity of JinanJinan250022China
| | - Zhaorui Liu
- Shandong Provincial Key Laboratory of Network Based Intelligent ComputingSchool of Information Science and EngineeringUniversity of JinanJinan250022China
| | - Wenjing Yue
- Shandong Provincial Key Laboratory of Network Based Intelligent ComputingSchool of Information Science and EngineeringUniversity of JinanJinan250022China
| | - Guiqiang Du
- School of Space Science and PhysicsShandong UniversityWeihai264209China
| | - Cong Wang
- School of Electronics and Information EngineeringHarbin Institute of TechnologyHarbin150001China
| | - Nam‐Young Kim
- RFIC CentreDepartment of Electronics EngineeringNDAC CentreKwangwoon UniversitySeoul01897South Korea
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Gao P, Duan M, Yang G, Zhang W, Jia C. Ultralow Energy Consumption and Fast Neuromorphic Computing Based on La 0.1Bi 0.9FeO 3 Ferroelectric Tunnel Junctions. NANO LETTERS 2024; 24:10767-10775. [PMID: 39172999 DOI: 10.1021/acs.nanolett.4c01924] [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
Low-power and fast artificial neural network devices represent the direction in developing analogue neural networks. Here, an ultralow power consumption (0.8 fJ) and rapid (100 ns) La0.1Bi0.9FeO3/La0.7Sr0.3MnO3 ferroelectric tunnel junction artificial synapse has been developed to emulate the biological neural networks. The visual memory and forgetting functionalities have been emulated based on long-term potentiation and depression with good linearity. Moreover, with a single device, logical operations of "AND" and "OR" are implemented, and an artificial neural network was constructed with a recognition accuracy of 96%. Especially for noisy data sets, the recognition speed is faster after preprocessing by the device in the present work. This sets the stage for highly reliable and repeatable unsupervised learning.
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Affiliation(s)
- Pan Gao
- Henan Key Laboratory of Quantum Materials and Quantum Energy, School of Quantum Information Future Technology, Henan University, Kaifeng 475004, China
| | - Mengyuan Duan
- Henan Key Laboratory of Quantum Materials and Quantum Energy, School of Quantum Information Future Technology, Henan University, Kaifeng 475004, China
| | - Guanghong Yang
- Key Lab for Special Functional Materials of Ministry of Education, School of Materials, Henan University, Kaifeng 475004, P. R. China
| | - Weifeng Zhang
- Henan Key Laboratory of Quantum Materials and Quantum Energy, School of Quantum Information Future Technology, Henan University, Kaifeng 475004, China
- Institute of Quantum Materials and Physics, Henan Academy of Sciences, Zhengzhou 450046, China
| | - Caihong Jia
- Henan Key Laboratory of Quantum Materials and Quantum Energy, School of Quantum Information Future Technology, Henan University, Kaifeng 475004, China
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Wang D, Hao S, Dkhil B, Tian B, Duan C. Ferroelectric materials for neuroinspired computing applications. FUNDAMENTAL RESEARCH 2024; 4:1272-1291. [PMID: 39431127 PMCID: PMC11489484 DOI: 10.1016/j.fmre.2023.04.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 04/04/2023] [Accepted: 04/09/2023] [Indexed: 10/22/2024] Open
Abstract
In recent years, the emergence of numerous applications of artificial intelligence (AI) has sparked a new technological revolution. These applications include facial recognition, autonomous driving, intelligent robotics, and image restoration. However, the data processing and storage procedures in the conventional von Neumann architecture are discrete, which leads to the "memory wall" problem. As a result, such architecture is incompatible with AI requirements for efficient and sustainable processing. Exploring new computing architectures and material bases is therefore imperative. Inspired by neurobiological systems, in-memory and in-sensor computing techniques provide a new means of overcoming the limitations inherent in the von Neumann architecture. The basis of neural morphological computation is a crossbar array of high-density, high-efficiency non-volatile memory devices. Among the numerous candidate memory devices, ferroelectric memory devices with non-volatile polarization states, low power consumption and strong endurance are expected to be ideal candidates for neuromorphic computing. Further research on the complementary metal-oxide-semiconductor (CMOS) compatibility for these devices is underway and has yielded favorable results. Herein, we first introduce the development of ferroelectric materials as well as their mechanisms of polarization reversal and detail the applications of ferroelectric synaptic devices in artificial neural networks. Subsequently, we introduce the latest developments in ferroelectrics-based in-memory and in-sensor computing. Finally, we review recent works on hafnium-based ferroelectric memory devices with CMOS process compatibility and give a perspective for future developments.
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Affiliation(s)
- Dong Wang
- Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
- Zhejiang Lab, Hangzhou 310000, China
| | - Shenglan Hao
- Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
| | - Brahim Dkhil
- Laboratoire Structures, Propriétés et Modélisation des Solides, CentraleSupélec, CNRS-UMR8580, Université Paris-Saclay, Paris 91190, France
| | - Bobo Tian
- Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
- Zhejiang Lab, Hangzhou 310000, China
| | - Chungang Duan
- Key Laboratory of Polar Materials and Devices (MOE), Ministry of Education, Shanghai Center of Brain-inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Shanxi 030006, China
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4
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Kwon JY, Kim JE, Kim JS, Chun SY, Soh K, Yoon JH. Artificial sensory system based on memristive devices. EXPLORATION (BEIJING, CHINA) 2024; 4:20220162. [PMID: 38854486 PMCID: PMC10867403 DOI: 10.1002/exp.20220162] [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: 07/20/2023] [Accepted: 10/16/2023] [Indexed: 06/11/2024]
Abstract
In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real-time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal-processing functions such as selective adaption in receptors, leaky integrate-and-fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by "artificial receptors," encoded into spike signals via "artificial neurons," and integrated and stored through "artificial synapses." The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high-performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real-time detection and processing of environmental information. This review explores the recent advances in memristor-based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in-depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor-based artificial sensory systems are discussed.
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Affiliation(s)
- Ju Young Kwon
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
| | - Ji Eun Kim
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- Department of Materials Science and EngineeringKorea UniversitySeoulRepublic of Korea
| | - Jong Sung Kim
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- Department of Materials Science and EngineeringKorea UniversitySeoulRepublic of Korea
| | - Suk Yeop Chun
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- KU‐KIST Graduate School of Converging Science and TechnologyKorea UniversitySeoulRepublic of Korea
| | - Keunho Soh
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
- Department of Materials Science and EngineeringKorea UniversitySeoulRepublic of Korea
| | - Jung Ho Yoon
- Electronic Materials Research CenterKorea Institute of Science and Technology (KIST)SeoulRepublic of Korea
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Kim S, Yoon C, Oh G, Lee YW, Shin M, Kee EH, Park BH, Lee JH, Park S, Kang BS, Kim YH. Progressive and Stable Synaptic Plasticity with Femtojoule Energy Consumption by the Interface Engineering of a Metal/Ferroelectric/Semiconductor. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2201502. [PMID: 35611436 PMCID: PMC9353489 DOI: 10.1002/advs.202201502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/13/2022] [Indexed: 06/01/2023]
Abstract
In the era of "big data," the cognitive system of the human brain is being mimicked through hardware implementation of highly accurate neuromorphic computing by progressive weight update in synaptic electronics. Low-energy synaptic operation requires both low reading current and short operation time to be applicable to large-scale neuromorphic computing systems. In this study, an energy-efficient synaptic device is implemented comprising a Ni/Pb(Zr0.52 Ti0.48 )O3 (PZT)/0.5 wt.% Nb-doped SrTiO3 (Nb:STO) heterojunction with a low reading current of 10 nA and short operation time of 20-100 ns. Ultralow femtojoule operation below 9 fJ at a synaptic event, which is comparable to the energy required for synaptic events in the human brain (10 fJ), is achieved by adjusting the Schottky barrier between the top electrode and ferroelectric film. Moreover, progressive domain switching in ferroelectric PZT successfully induces both low nonlinearity/asymmetry and good stability of the weight update. The synaptic device developed here can facilitate the development of large-scale neuromorphic arrays for artificial neural networks with low energy consumption and high accuracy.
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Affiliation(s)
- Sohwi Kim
- Division of Quantum Phases & DevicesDepartment of PhysicsKonkuk UniversitySeoul05029South Korea
| | - Chansoo Yoon
- Division of Quantum Phases & DevicesDepartment of PhysicsKonkuk UniversitySeoul05029South Korea
| | - Gwangtaek Oh
- Division of Quantum Phases & DevicesDepartment of PhysicsKonkuk UniversitySeoul05029South Korea
| | - Young Woong Lee
- Division of Quantum Phases & DevicesDepartment of PhysicsKonkuk UniversitySeoul05029South Korea
| | - Minjeong Shin
- Division of Quantum Phases & DevicesDepartment of PhysicsKonkuk UniversitySeoul05029South Korea
| | - Eun Hee Kee
- Division of Quantum Phases & DevicesDepartment of PhysicsKonkuk UniversitySeoul05029South Korea
| | - Bae Ho Park
- Division of Quantum Phases & DevicesDepartment of PhysicsKonkuk UniversitySeoul05029South Korea
| | - Ji Hye Lee
- Center for Correlated Electron Systems (CCES)Institute of Basic Science (IBS)Seoul08826South Korea
- Department of Physics and AstronomySeoul National UniversitySeoul08826South Korea
| | - Sanghyun Park
- Department of Applied PhysicsHanyang UniversityGyeonggi‐do15588South Korea
| | - Bo Soo Kang
- Department of Applied PhysicsHanyang UniversityGyeonggi‐do15588South Korea
| | - Young Heon Kim
- Graduate School of Analytical Science and TechnologyChungnam National UniversityDaejoen34134South Korea
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Jia T, Chen Y, Cai Y, Dai W, Zhang C, Yu L, Yue W, Kimura H, Yao Y, Yu S, Guo Q, Cheng Z. Ferroelectricity and Piezoelectricity in 2D Van der Waals CuInP2S6 Ferroelectric Tunnel Junctions. NANOMATERIALS 2022; 12:nano12152516. [PMID: 35893484 PMCID: PMC9332483 DOI: 10.3390/nano12152516] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 07/15/2022] [Accepted: 07/15/2022] [Indexed: 11/16/2022]
Abstract
CuInP2S6 (CIPS) is a novel two-dimensional (2D) van der Waals (vdW) ferroelectric layered material with a Curie temperature of TC~315 K, making it promising for great potential applications in electronic and photoelectric devices. Herein, the ferroelectric and electric properties of CIPS at different thicknesses are carefully evaluated by scanning probe microscopy techniques. Some defects in some local regions due to Cu deficiency lead to a CuInP2S6–In4/3P2S6 (CIPS–IPS) paraelectric phase coexisting with the CIPS ferroelectric phase. An electrochemical strain microscopy (ESM) study reveals that the relaxation times corresponding to the Cu ions and the IPS ionospheres are not the same, with a significant difference in their response to DC voltage, related to the rectification effect of the ferroelectric tunnel junction (FTJ). The electric properties of the FTJ indicate Cu+ ion migration and propose that the current flow and device performance are dynamically controlled by an interfacial Schottky barrier. The addition of the ferroelectricity of CIPS opens up applications in memories and sensors, actuators, and even spin-orbit devices based on 2D vdW heterostructures.
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Affiliation(s)
- Tingting Jia
- Institute of Advanced Materials Science and Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (Y.C.); (Y.C.); (W.D.); (C.Z.); (L.Y.); (W.Y.); (S.Y.)
- School of Materials Science and Engineering, Hubei University, Wuhan 430062, China
- Correspondence: (T.J.); (Q.G.); (Z.C.)
| | - Yanrong Chen
- Institute of Advanced Materials Science and Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (Y.C.); (Y.C.); (W.D.); (C.Z.); (L.Y.); (W.Y.); (S.Y.)
- School of Materials and Energy, Guangdong University of Technology, Guangzhou 510006, China;
| | - Yali Cai
- Institute of Advanced Materials Science and Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (Y.C.); (Y.C.); (W.D.); (C.Z.); (L.Y.); (W.Y.); (S.Y.)
- School of Materials Science and Engineering, Hubei University, Wuhan 430062, China
| | - Wenbin Dai
- Institute of Advanced Materials Science and Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (Y.C.); (Y.C.); (W.D.); (C.Z.); (L.Y.); (W.Y.); (S.Y.)
- School of Materials and Energy, Guangdong University of Technology, Guangzhou 510006, China;
| | - Chong Zhang
- Institute of Advanced Materials Science and Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (Y.C.); (Y.C.); (W.D.); (C.Z.); (L.Y.); (W.Y.); (S.Y.)
- School of Materials Science and Engineering, Hubei University, Wuhan 430062, China
| | - Liang Yu
- Institute of Advanced Materials Science and Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (Y.C.); (Y.C.); (W.D.); (C.Z.); (L.Y.); (W.Y.); (S.Y.)
- Nano Science and Technology Institute, University of Science and Technology of China, Suzhou 215125, China
| | - Wenfeng Yue
- Institute of Advanced Materials Science and Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (Y.C.); (Y.C.); (W.D.); (C.Z.); (L.Y.); (W.Y.); (S.Y.)
- Nano Science and Technology Institute, University of Science and Technology of China, Suzhou 215125, China
| | - Hideo Kimura
- School of Environmental and Material Engineering, Yantai University, Yantai 264005, China;
| | - Yingbang Yao
- School of Materials and Energy, Guangdong University of Technology, Guangzhou 510006, China;
| | - Shuhui Yu
- Institute of Advanced Materials Science and Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (Y.C.); (Y.C.); (W.D.); (C.Z.); (L.Y.); (W.Y.); (S.Y.)
| | - Quansheng Guo
- School of Materials Science and Engineering, Hubei University, Wuhan 430062, China
- Correspondence: (T.J.); (Q.G.); (Z.C.)
| | - Zhenxiang Cheng
- Institute for Superconducting & Electronic Materials, University of Wollongong, Innovation Campus, North Wollongong, NSW 2500, Australia
- Correspondence: (T.J.); (Q.G.); (Z.C.)
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Wang J, Zhu Y, Zhu L, Chen C, Wan Q. Emerging Memristive Devices for Brain-Inspired Computing and Artificial Perception. FRONTIERS IN NANOTECHNOLOGY 2022. [DOI: 10.3389/fnano.2022.940825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Brain-inspired computing is an emerging field that aims at building a compact and massively parallel architecture, to reduce power consumption in conventional Von Neumann Architecture. Recently, memristive devices have gained great attention due to their immense potential in implementing brain-inspired computing and perception. The conductance of a memristor can be modulated by a voltage pulse, enabling emulations of both essential synaptic and neuronal functions, which are considered as the important building blocks for artificial neural networks. As a result, it is critical to review recent developments of memristive devices in terms of neuromorphic computing and perception applications, waiting for new thoughts and breakthroughs. The device structures, operation mechanisms, and materials are introduced sequentially in this review; additionally, late advances in emergent neuromorphic computing and perception based on memristive devices are summed up. Finally, the challenges that memristive devices toward high-performance brain-inspired computing and perception are also briefly discussed. We believe that the advances and challenges will lead to significant advancements in artificial neural networks and intelligent humanoid robots.
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Chi X, Guo R, Xiong J, Ren L, Peng X, Tay BK, Chen J. Enhanced Tunneling Magnetoresistance Effect via Ferroelectric Control of Interface Electronic/Magnetic Reconstructions. ACS APPLIED MATERIALS & INTERFACES 2021; 13:56638-56644. [PMID: 34786928 DOI: 10.1021/acsami.1c15836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Magnetic tunnel junctions (MTJs) with tunable tunneling magnetoresistances (TMR) have already been proven to have great potential for spintronics. Especially, when ferroelectric materials are used as insulating barriers, more novel functions of MTJs can be realized due to interface magnetoelectric coupling. Here, we demonstrate a very large ferroelectric modulation of TMR (as high as 570% in low-resistance state) in the ferroelectric/magnetic La0.5Sr0.5MnO3/BaTiO3 (LSMO/BTO) junctions and find robust interfacial electronic and magnetic reconstructions via ferroelectric polarization switching. Through electrical, magnetic, and optical measurements combined with X-ray absorption and magnetic circular dichroism, we reveal that the interfacial electronic and magnetic (ferromagnetic/antiferromagnetic phase transition) reconstructions originate from strong electromagnetic coupling between BTO and LSMO at the interface and are driven by the modulation of hole/electron doping at the interface of LSMO/BTO through ferroelectric polarization switching. As a result, the ferroelectrically controlled interface barrier height and width and spin filter effect enable a giant electrical modulation of TMR. Our results shed new light on the intrinsic mechanisms governing magnetoelectric coupling and offering a new route to enhance magnetoelectric coupling for spin control in spintronic devices.
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Affiliation(s)
- Xiao Chi
- State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou 510641, China
| | - Rui Guo
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
- Centre for Micro- and Nano-Electronics (CMNE), School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Singapore
- UMI 3288 CINTRA (CNRS-NTU-THALES Research Alliances), Nanyang Technological University, Research Techno Plaza, 50 Nanyang Drive, 637553 Singapore
| | - Juxia Xiong
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, P.R. China
| | - Lizhu Ren
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117583 Singapore
| | - Xinwen Peng
- State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou 510641, China
| | - Beng Kang Tay
- Centre for Micro- and Nano-Electronics (CMNE), School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Singapore
- UMI 3288 CINTRA (CNRS-NTU-THALES Research Alliances), Nanyang Technological University, Research Techno Plaza, 50 Nanyang Drive, 637553 Singapore
| | - Jingsheng Chen
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
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Effect of Oxygen Vacancy on the Conduction Modulation Linearity and Classification Accuracy of Pr 0.7Ca 0.3MnO 3 Memristor. NANOMATERIALS 2021; 11:nano11102684. [PMID: 34685125 PMCID: PMC8538184 DOI: 10.3390/nano11102684] [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: 09/03/2021] [Revised: 10/05/2021] [Accepted: 10/08/2021] [Indexed: 11/21/2022]
Abstract
An amorphous Pr0.7Ca0.3MnO3 (PCMO) film was grown on a TiN/SiO2/Si (TiN–Si) substrate at 300 °C and at an oxygen pressure (OP) of 100 mTorr. This PCMO memristor showed typical bipolar switching characteristics, which were attributed to the generation and disruption of oxygen vacancy (OV) filaments. Fabrication of the PCMO memristor at a high OP resulted in nonlinear conduction modulation with the application of equivalent pulses. However, the memristor fabricated at a low OP of 100 mTorr exhibited linear conduction modulation. The linearity of this memristor improved because the growth and disruption of the OV filaments were mostly determined by the redox reaction of OV owing to the presence of numerous OVs in this PCMO film. Furthermore, simulation using a convolutional neural network revealed that this PCMO memristor has enhanced classification performance owing to its linear conduction modulation. This memristor also exhibited several biological synaptic characteristics, indicating that an amorphous PCMO thin film fabricated at a low OP would be a suitable candidate for artificial synapses.
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Zhang Q, Li X, Zhu J. Direct Observation of Interface-Dependent Multidomain State in the BaTiO 3 Tunnel Barrier of a Multiferroic Tunnel Junction Memristor. ACS APPLIED MATERIALS & INTERFACES 2021; 13:43641-43647. [PMID: 34473930 DOI: 10.1021/acsami.1c11661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Multiferroic tunnel junctions (MFTJs), normally consisting of a four-state resistance, have been studied extensively as a potential candidate for nonvolatile memory devices. More interestingly, the MFTJs whose resistance can be tuned continuously with applied voltage were also reported recently. Since the performance of MFTJs is closely related to their interfacial structures, it is necessary to investigate MFTJs at the atomic scale. In this work, atomic-resolution HAADF, ABF, and EELS of the La0.7Sr0.3MnO3/BaTiO3/La0.7Sr0.3MnO3 MFTJ memristor have been obtained with aberration-corrected scanning transmission electron microscopy (STEM). These results demonstrate varied degree of interfacial cation intermixing at the bottom BTO/LSMO interface, which has a direct influence on the polarization of the ferroelectric barrier BTO and the electronic structure of Mn near the interfaces. We also took advantage of a simplified model to explain the relation between the interfacial behavior and polarization states, which could be a contributing factor to the transport properties of this MFTJ.
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Affiliation(s)
- Qiqi Zhang
- National Center for Electron Microscopy in Beijing, School of Materials Science and Engineering, The State Key Laboratory of New Ceramics and Fine Processing, Key Laboratory of Advanced Materials (MOE), Tsinghua University, Beijing 100084, People's Republic of China
- Ji Hua Laboratory, Foshan 528000, People's Republic of China
| | - Xiaoguang Li
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Physics, and CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei 230000, People's Republic of China
| | - Jing Zhu
- National Center for Electron Microscopy in Beijing, School of Materials Science and Engineering, The State Key Laboratory of New Ceramics and Fine Processing, Key Laboratory of Advanced Materials (MOE), Tsinghua University, Beijing 100084, People's Republic of China
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Chen L, Zhou J, Zhang X, Ding K, Ding J, Sun Z, Wang X. Low-Temperature Tunneling Electroresistance in Ferromagnetic Metal/Ferroelectric/Semiconductor Tunnel Junctions. ACS APPLIED MATERIALS & INTERFACES 2021; 13:23282-23288. [PMID: 33944549 DOI: 10.1021/acsami.1c05366] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Ferroelectric tunnel junctions (FTJs) as artificial synaptic devices are promising candidates for the building block of nonvolatile data storage devices. However, a small ON/OFF ratio of FTJs limits their application in low-temperature operations. In this work, the influence of quantum interference effects on tunneling electroresistance in the La0.7Sr0.3MnO3/BaTiO3/Nb:SrTiO3 (ferromagnetic metal/ferroelectric/semiconductor) FTJ at low temperatures is investigated. The Current-voltage curves are observed in the tunnel junction from 300 to 10 K with a six-unit-cell thick BaTiO3 film by the ferroelectric polarization effect. First, the ON/OFF current ratio increases from 300 to 30 K due to the increase of polarization in the ferroelectric barrier, and then, it gradually decreases when the temperature drops below 30 K. An anomalous ON/OFF current ratio of ∼105 is obtained at 30 K. The low-temperature tunneling properties in the FTJ are associated with a low-temperature resistivity minimum in the ferromagnetic metal layer by the electron-electron interaction, which increases the La0.7Sr0.3MnO3/BaTiO3 interface resistance, leading to a higher resistance state and lower IOFF for the OFF state. As a result, the ON/OFF current ratio is abruptly enhanced at 30 K. Our results emphasize the crucial role of transport properties of La0.7Sr0.3MnO3 in FTJs and pave the way for the design and application of FTJs at low temperatures.
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Affiliation(s)
- Liming Chen
- School of Materials Science and Engineering, Anhui University of Technology, Ma'anshan, Anhui 243002, P. R. China
- Jiangsu Provincial Key Laboratory of Advanced Photonic and Electronic Materials, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, P. R. China
| | - Jian Zhou
- School of Materials Science and Engineering, Anhui University of Technology, Ma'anshan, Anhui 243002, P. R. China
| | - Xiao Zhang
- School of Materials Science and Engineering, Anhui University of Technology, Ma'anshan, Anhui 243002, P. R. China
| | - Kuankuan Ding
- School of Materials Science and Engineering, Anhui University of Technology, Ma'anshan, Anhui 243002, P. R. China
| | - Jianxiang Ding
- School of Materials Science and Engineering, Anhui University of Technology, Ma'anshan, Anhui 243002, P. R. China
- Jiangsu Key Laboratory of Advanced Metallic Materials, School of Materials Science and Engineering, Southeast University, Nanjing 211189, P. R. China
| | - Zhengming Sun
- School of Materials Science and Engineering, Anhui University of Technology, Ma'anshan, Anhui 243002, P. R. China
- Jiangsu Key Laboratory of Advanced Metallic Materials, School of Materials Science and Engineering, Southeast University, Nanjing 211189, P. R. China
| | - Xuefeng Wang
- Jiangsu Provincial Key Laboratory of Advanced Photonic and Electronic Materials, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, P. R. China
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12
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Zhao T, Qing L, Long T, Xu X, Zhao S, Lu X. Dynamical coupling of ion adsorption with fluid flow in nanopores. AIChE J 2021. [DOI: 10.1002/aic.17266] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Teng Zhao
- State Key laboratory of Chemical Engineering and School of Chemical Engineering East China University of Science and Technology Shanghai China
| | - Leying Qing
- State Key laboratory of Chemical Engineering and School of Chemical Engineering East China University of Science and Technology Shanghai China
| | - Ting Long
- State Key laboratory of Chemical Engineering and School of Chemical Engineering East China University of Science and Technology Shanghai China
| | - Xiaofei Xu
- State Key laboratory of Chemical Engineering and School of Chemical Engineering East China University of Science and Technology Shanghai China
| | - Shuangliang Zhao
- State Key laboratory of Chemical Engineering and School of Chemical Engineering East China University of Science and Technology Shanghai China
- Guangxi Key Laboratory of Petrochemical Resource Processing and Process Intensification Technology and School of Chemistry and Chemical Engineering Guangxi University Nanning China
| | - Xiaohua Lu
- College of Chemical Engineering, State Key Laboratory of Materials‐oriented Chemical Engineering Nanjing Tech University Nanjing China
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13
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Yang Y, Xi Z, Dong Y, Zheng C, Hu H, Li X, Jiang Z, Lu WC, Wu D, Wen Z. Spin-Filtering Ferroelectric Tunnel Junctions as Multiferroic Synapses for Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2020; 12:56300-56309. [PMID: 33287535 DOI: 10.1021/acsami.0c16385] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
As nanoelectronic synapses, memristive ferroelectric tunnel junctions (FTJs) have triggered great interest due to the potential applications in neuromorphic computing for emulating biological brains. Here, we demonstrate multiferroic FTJ synapses based on the ferroelectric modulation of spin-filtering BaTiO3/CoFe2O4 composite barriers. Continuous conductance change with an ON/OFF current ratio of ∼54 400% and long-term memory with the spike-timing-dependent plasticity (STDP) of synaptic weight for Hebbian learning are achieved by controlling the polarization switching of BaTiO3. Supervised learning simulations adopting the STDP results as database for weight training are performed on a crossbar neural network and exhibit a high accuracy rate above 97% for recognition. The polarization switching also alters the band alignment of CoFe2O4 barrier relative to the electrodes, giving rise to the change of tunneling magnetoresistance ratio by about 10 times and even the reversal of its sign depending upon the resistance states. These results, especially the electrically switchable spin polarization, provide a new approach toward multiferroic neuromorphic devices with energy-efficient electrical manipulations through potential barrier design. In addition, the availability of spinel ferrite barriers epitaxially grown with ferroelectric oxides also expends the playground of FTJ devices for a broad scope of applications.
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Affiliation(s)
- Yihao Yang
- College of Physics and Center for Marine Observation and Communications, Qingdao University, Qingdao 266071, China
| | - Zhongnan Xi
- National Laboratory of Solid State Microstructures, Department of Materials Science and Engineering, Jiangsu Key Laboratory of Artificial Functional Materials and Collaborative Innovation Center for Advanced Materials, Nanjing University, Nanjing 210093, China
| | - Yuehang Dong
- School of Data Science and Software Engineering, Qingdao University, Qingdao 266071, China
| | - Chunyan Zheng
- College of Physics and Center for Marine Observation and Communications, Qingdao University, Qingdao 266071, China
| | - Haihua Hu
- College of Physics and Center for Marine Observation and Communications, Qingdao University, Qingdao 266071, China
| | - Xiaofei Li
- College of Physics and Center for Marine Observation and Communications, Qingdao University, Qingdao 266071, China
| | - Zhizheng Jiang
- College of Physics and Center for Marine Observation and Communications, Qingdao University, Qingdao 266071, China
| | - Wen-Cai Lu
- College of Physics and Center for Marine Observation and Communications, Qingdao University, Qingdao 266071, China
| | - Di Wu
- National Laboratory of Solid State Microstructures, Department of Materials Science and Engineering, Jiangsu Key Laboratory of Artificial Functional Materials and Collaborative Innovation Center for Advanced Materials, Nanjing University, Nanjing 210093, China
| | - Zheng Wen
- College of Physics and Center for Marine Observation and Communications, Qingdao University, Qingdao 266071, China
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14
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Choi S, Yang J, Wang G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e2004659. [PMID: 33006204 DOI: 10.1002/adma.202004659] [Citation(s) in RCA: 104] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 08/12/2020] [Indexed: 06/11/2023]
Abstract
Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the memristor is necessary for achieving memristor-based neuromorphic hardware systems. Herein, a wide range of memristors and memristive-related devices for artificial synapses and neurons is highlighted. The device structures, switching principles, and the applications of essential synaptic and neuronal functionalities are sequentially presented. Moreover, recent advances in memristive artificial neural networks and their hardware implementations are introduced along with an overview of the various learning algorithms. Finally, the main challenges of the memristive synapses and neurons toward high-performance and energy-efficient neuromorphic computing are briefly discussed. This progress report aims to be an insightful guide for the research on memristors and neuromorphic-based computing.
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Affiliation(s)
- Sanghyeon Choi
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jehyeon Yang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Gunuk Wang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
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15
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Cheng S, Fan Z, Rao J, Hong L, Huang Q, Tao R, Hou Z, Qin M, Zeng M, Lu X, Zhou G, Yuan G, Gao X, Liu JM. Highly Controllable and Silicon-Compatible Ferroelectric Photovoltaic Synapses for Neuromorphic Computing. iScience 2020; 23:101874. [PMID: 33344918 PMCID: PMC7736912 DOI: 10.1016/j.isci.2020.101874] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 09/29/2020] [Accepted: 11/24/2020] [Indexed: 11/19/2022] Open
Abstract
Ferroelectric synapses using polarization switching (a purely electronic switching process) to induce analog conductance change have attracted considerable interest. Here, we propose ferroelectric photovoltaic (FePV) synapses that use polarization-controlled photocurrent as the readout and thus have no limitations on the forms and thicknesses of the constituent ferroelectric and electrode materials. This not only makes FePV synapses easy to fabricate but also reduces the depolarization effect and hence enhances the polarization controllability. As a proof-of-concept implementation, a Pt/Pb(Zr0.2Ti0.8)O3/LaNiO3 FePV synapse is facilely grown on a silicon substrate, which demonstrates continuous photovoltaic response modulation with good controllability (small nonlinearity and write noise) enabled by gradual polarization switching. Using photovoltaic response as synaptic weight, this device exhibits versatile synaptic functions including long-term potentiation/depression and spike-timing-dependent plasticity. A simulated FePV synapse-based neural network achieves high accuracies (>93%) for image recognition. This study paves a new way toward highly controllable and silicon-compatible synapses for neuromorphic computing.
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Affiliation(s)
- Shengliang Cheng
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Zhen Fan
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Corresponding author
| | - Jingjing Rao
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Lanqing Hong
- Department of Industrial Systems Engineering and Management, National University of Singapore, 117576, Singapore
| | - Qicheng Huang
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Ruiqiang Tao
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Zhipeng Hou
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Minghui Qin
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Min Zeng
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Xubing Lu
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Guofu Zhou
- Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- National Center for International Research on Green Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Guoliang Yuan
- School of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Xingsen Gao
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Jun-Ming Liu
- Institute for Advanced Materials, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
- Laboratory of Solid State Microstructures and Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
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16
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Wu J, Wu T. A Bright New World of Ferroelectrics: Magic of Spontaneous Polarization. ACS APPLIED MATERIALS & INTERFACES 2020; 12:52231-52233. [PMID: 33238359 DOI: 10.1021/acsami.0c18276] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
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17
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Khan SA, Kim S. Comparison of diverse resistive switching characteristics and demonstration of transitions among them in Al-incorporated HfO 2-based resistive switching memory for neuromorphic applications. RSC Adv 2020; 10:31342-31347. [PMID: 35520690 PMCID: PMC9056407 DOI: 10.1039/d0ra06389d] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 08/19/2020] [Indexed: 11/30/2022] Open
Abstract
Diverse resistive switching behaviors are observed in the Pt/HfAlOx/TiN memory device depending on the compliance current, the sweep voltage amplitude, and the bias polarity. We extensively compare three types of resistive switching characteristics in a Pt/HfAlOx/TiN device in terms of endurance, ON/OFF ratio, linear conductance update, and read margin in a cross-point array structure for synaptic device applications. The bipolar resistive switching under positive set and negative reset shows better linear synaptic weight updates due to gradual switching than the bipolar resistive switching at the opposite polarity. The complementary resistive switching shows a higher read margin due to the current suppression at a low voltage regime. In addition, the potentiation and the depression can be adjusted at the same voltage polarity for a hardware neuromorphic system. Finally, we demonstrate the transition between bipolar resistive switching and complementary resistive switching, which could provide flexibility for different applications. Diverse resistive switching behaviors are observed in the Pt/HfAlOx/TiN memory device depending on the compliance current, the sweep voltage amplitude, and the bias polarity.![]()
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Affiliation(s)
- Sobia Ali Khan
- School of Electronics Engineering, Chungbuk National University Cheongju 28644 South Korea
| | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University Seoul 04620 South Korea
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18
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Lin W, Yang B, Chen AP, Wu X, Guo R, Chen S, Liu L, Xie Q, Shu X, Hui Y, Chow GM, Feng Y, Carlotti G, Tacchi S, Yang H, Chen J. Perpendicular Magnetic Anisotropy and Dzyaloshinskii-Moriya Interaction at an Oxide/Ferromagnetic Metal Interface. PHYSICAL REVIEW LETTERS 2020; 124:217202. [PMID: 32530667 DOI: 10.1103/physrevlett.124.217202] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 05/01/2020] [Indexed: 06/11/2023]
Abstract
We report on the study of both perpendicular magnetic anisotropy (PMA) and Dzyaloshinskii-Moriya interaction (DMI) at an oxide/ferromagnetic metal (FM) interface, i.e., BaTiO_{3} (BTO)/CoFeB. Thanks to the functional properties of the BTO film and the capability to precisely control its growth, we are able to distinguish the dominant role of the oxide termination (TiO_{2} vs BaO) from the moderate effect of ferroelectric polarization in the BTO film, on the PMA and DMI at an oxide/FM interface. We find that the interfacial magnetic anisotropy energy of the BaO-BTO/CoFeB structure is 2 times larger than that of the TiO_{2}-BTO/CoFeB, while the DMI of the TiO_{2}-BTO/CoFeB interface is larger. We explain the observed phenomena by first principles calculations, which ascribe them to the different electronic states around the Fermi level at oxide/ferromagnetic metal interfaces and the different spin-flip process. This study paves the way for further investigation of the PMA and DMI at various oxide/FM structures and thus their applications in the promising field of energy-efficient devices.
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Affiliation(s)
- Weinan Lin
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
| | - Baishun Yang
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China and Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Andy Paul Chen
- NUS Graduate School of Integrative Sciences and Engineering, National University of Singapore, 117456, Singapore
| | - Xiaohan Wu
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
| | - Rui Guo
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
| | - Shaohai Chen
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
| | - Liang Liu
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
| | - Qidong Xie
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
| | - Xinyu Shu
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
| | - Yajuan Hui
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
| | - Gan Moog Chow
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
| | - Yuanping Feng
- NUS Graduate School of Integrative Sciences and Engineering, National University of Singapore, 117456, Singapore
- Department of Physics, National University of Singapore, 117576, Singapore
| | - Giovanni Carlotti
- Dipartimento di Fisica e Geologia, Università di Perugia, Via Pascoli, I-06123 Perugia, Italy
| | - Silvia Tacchi
- Istituto Officina dei Materiali del CNR (CNR-IOM), Sede Secondaria di Perugia, c/o Dipartimento di Fisica e Geologia, Università di Perugia, I-06123 Perugia, Italy
| | - Hongxin Yang
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China and Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jingsheng Chen
- Department of Materials Science and Engineering, National University of Singapore, 117575, Singapore
- NUS Graduate School of Integrative Sciences and Engineering, National University of Singapore, 117456, Singapore
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19
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Yu R, Li E, Wu X, Yan Y, He W, He L, Chen J, Chen H, Guo T. Electret-Based Organic Synaptic Transistor for Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2020; 12:15446-15455. [PMID: 32153175 DOI: 10.1021/acsami.9b22925] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Neuromorphic computing inspired by the neural systems in human brain will overcome the issue of independent information processing and storage. An artificial synaptic device as a basic unit of a neuromorphic computing system can perform signal processing with low power consumption, and exploring different types of synaptic transistors is essential to provide suitable artificial synaptic devices for artificial intelligence. Hence, for the first time, an electret-based synaptic transistor (EST) is presented, which successfully shows synaptic behaviors including excitatory/inhibitory postsynaptic current, paired-pulse facilitation/depression, long-term memory, and high-pass filtering. Moreover, a neuromorphic computing simulation based on our EST is performed using the handwritten artificial neural network, which exhibits an excellent recognition accuracy (85.88%) after 120 learning epochs, higher than most reported organic synaptic transistors and close to the ideal accuracy (92.11%). Such a novel synaptic device enriches the diversity of synaptic transistors, laying the foundation for the diversified development of the next generation of neuromorphic computing systems.
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Affiliation(s)
- Rengjian Yu
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
| | - Enlong Li
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
| | - Xiaomin Wu
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
| | - Yujie Yan
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
| | - Weixin He
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
| | - Lihua He
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
| | - Jinwei Chen
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
| | - Huipeng Chen
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350100, China
| | - Tailiang Guo
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou 350100, China
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20
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Ma C, Luo Z, Huang W, Zhao L, Chen Q, Lin Y, Liu X, Chen Z, Liu C, Sun H, Jin X, Yin Y, Li X. Sub-nanosecond memristor based on ferroelectric tunnel junction. Nat Commun 2020; 11:1439. [PMID: 32188861 PMCID: PMC7080735 DOI: 10.1038/s41467-020-15249-1] [Citation(s) in RCA: 81] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 02/25/2020] [Indexed: 12/02/2022] Open
Abstract
Next-generation non-volatile memories with ultrafast speed, low power consumption, and high density are highly desired in the era of big data. Here, we report a high performance memristor based on a Ag/BaTiO3/Nb:SrTiO3 ferroelectric tunnel junction (FTJ) with the fastest operation speed (600 ps) and the highest number of states (32 states or 5 bits) per cell among the reported FTJs. The sub-nanosecond resistive switching maintains up to 358 K, and the write current density is as low as 4 × 103 A cm−2. The functionality of spike-timing-dependent plasticity served as a solid synaptic device is also obtained with ultrafast operation. Furthermore, it is demonstrated that a Nb:SrTiO3 electrode with a higher carrier concentration and a metal electrode with lower work function tend to improve the operation speed. These results may throw light on the way for overcoming the storage performance gap between different levels of the memory hierarchy and developing ultrafast neuromorphic computing systems. Memristor devices based on ferroelectric tunnel junctions are promising, but suffer from quite slow switching times. Here, the authors report on ultrafast switching times at and above room temperature of 600ps in Ag/BaTiO3/Nb:SrTiO3 based ferroelectric tunnel junctions.
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Affiliation(s)
- Chao Ma
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Physics, and CAS key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, China
| | - Zhen Luo
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Physics, and CAS key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, China
| | - Weichuan Huang
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Physics, and CAS key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, China
| | - Letian Zhao
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Physics, and CAS key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, China
| | - Qiaoling Chen
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Physics, and CAS key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, China
| | - Yue Lin
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Physics, and CAS key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, China
| | - Xiang Liu
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Physics, and CAS key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, China
| | - Zhiwei Chen
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Physics, and CAS key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, China
| | - Chuanchuan Liu
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Physics, and CAS key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, China
| | - Haoyang Sun
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Physics, and CAS key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, China
| | - Xi Jin
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Physics, and CAS key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, China
| | - Yuewei Yin
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Physics, and CAS key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, China.
| | - Xiaoguang Li
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Physics, and CAS key Laboratory of Strongly-Coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, China. .,Key Laboratory of Materials Physics, Institute of Solid State Physics, CAS, Hefei, China. .,Collaborative Innovation Center of Advanced Microstructures, Nanjing, China.
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21
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Chi X, Wang H, Guo R, Whitcher TJ, Yu X, Yang P, Yan X, Breese MBH, Loh KP, Chen J, Rusydi A. Unusual Hole and Electron Midgap States and Orbital Reconstructions Induced Huge Ferroelectric Tunneling Electroresistance in BaTiO 3/SrTiO 3. NANO LETTERS 2020; 20:1101-1109. [PMID: 31944125 DOI: 10.1021/acs.nanolett.9b04390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Oxide heterostructures have attracted a lot of interest because of their rich exotic phenomena and potential applications. Recently, a greatly enhanced tunneling electroresistance (TER) of ferroelectric tunnel junctions (FTJs) has been realized in such heterostructures. However, our understanding on the electronic structure of resistance response with polarization reversal and the origin of huge TER is still lacking. Here, we report on electronic structures, particularly at the interface and surface, and the control of the spontaneous polarization of BaTiO3 films by changing the termination of a SrTiO3 substrate. Interestingly, unusual electron and hole midgap states are concurrently formed and accompanied by orbital reconstructions, which determine the ferroelectric polarization orientation in the BaTiO3/SrTiO3. Such unusual midgap states, which yield a strong electronic screening effect, reduce the ferroelectric barrier width and height, and pin the ferroelectric polarization, lead to a dramatic enhancement of the TER effect. The midgap states are also observed in BaTiO3 films on electron-doped Nb/SrTiO3 revealing its universality. Our result provides new insight into the origin of the huge TER effect and opens a new route for designing ferroelectric tunnel junction-based devices with huge TER through interface engineering.
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Affiliation(s)
- Xiao Chi
- Singapore Synchrotron Light Source , National University of Singapore , 5 Research Link, 117603 , Singapore
- Department of Chemistry , National University of Singapore , 3 Science Drive 3, 117543 , Singapore
- Center for Advanced 2D Materials and Graphene Research Centre , 2 Science Drive 2, 117526 , Singapore
| | - Han Wang
- Department of Materials Science and Engineering , National University of Singapore , 117575 , Singapore
| | - Rui Guo
- Department of Materials Science and Engineering , National University of Singapore , 117575 , Singapore
| | - Thomas J Whitcher
- Singapore Synchrotron Light Source , National University of Singapore , 5 Research Link, 117603 , Singapore
- Center for Advanced 2D Materials and Graphene Research Centre , 2 Science Drive 2, 117526 , Singapore
| | - Xiaojiang Yu
- Singapore Synchrotron Light Source , National University of Singapore , 5 Research Link, 117603 , Singapore
| | - Ping Yang
- Singapore Synchrotron Light Source , National University of Singapore , 5 Research Link, 117603 , Singapore
| | - Xiaobing Yan
- Department of Materials Science and Engineering , National University of Singapore , 117575 , Singapore
| | - Mark B H Breese
- Singapore Synchrotron Light Source , National University of Singapore , 5 Research Link, 117603 , Singapore
- Department of Physics , National University of Singapore , 2 Science Drive 3, 117542 , Singapore
| | - Kian Ping Loh
- Department of Chemistry , National University of Singapore , 3 Science Drive 3, 117543 , Singapore
- Center for Advanced 2D Materials and Graphene Research Centre , 2 Science Drive 2, 117526 , Singapore
- Solar Energy Research Institute of Singapore (SERIS) , 7 Engineering Drive 1, 117574 , Singapore
| | - Jingsheng Chen
- Department of Chemistry , National University of Singapore , 3 Science Drive 3, 117543 , Singapore
| | - Andrivo Rusydi
- Singapore Synchrotron Light Source , National University of Singapore , 5 Research Link, 117603 , Singapore
- Department of Physics , National University of Singapore , 2 Science Drive 3, 117542 , Singapore
- Center for Advanced 2D Materials and Graphene Research Centre , 2 Science Drive 2, 117526 , Singapore
- Solar Energy Research Institute of Singapore (SERIS) , 7 Engineering Drive 1, 117574 , Singapore
- NUSSNI-NanoCore , National University of Singapore , 117576 , Singapore
- Graduate School for Integrative Sciences and Engineering (NGS) , National University of Singapore , 28 Medical Drive, 117456 , Singapore
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22
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Li J, Ge C, Du J, Wang C, Yang G, Jin K. Reproducible Ultrathin Ferroelectric Domain Switching for High-Performance Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e1905764. [PMID: 31850652 DOI: 10.1002/adma.201905764] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 11/19/2019] [Indexed: 05/22/2023]
Abstract
Neuromorphic computing consisting of artificial synapses and neural network algorithms provides a promising approach for overcoming the inherent limitations of current computing architecture. Developments in electronic devices that can accurately mimic the synaptic plasticity of biological synapses, have promoted the research boom of neuromorphic computing. It is reported that robust ferroelectric tunnel junctions can be employed to design high-performance electronic synapses. These devices show an excellent memristor function with many reproducible states (≈200) through gradual ferroelectric domain switching. Both short- and long-term plasticity can be emulated by finely tuning the applied pulse parameters in the electronic synapse. The analog conductance switching exhibits high linearity and symmetry with small switching variations. A simulated artificial neural network with supervised learning built from these synaptic devices exhibited high classification accuracy (96.4%) for the Mixed National Institute of Standards and Technology (MNIST) handwritten recognition data set.
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Affiliation(s)
- Jiankun Li
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Chen Ge
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai, 200241, China
| | - Jianyu Du
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Can Wang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong, 523808, China
| | - Guozhen Yang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Kuijuan Jin
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong, 523808, China
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23
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Lu PP, Shen JX, Shang DS, Sun Y. Nonvolatile Memory and Artificial Synapse Based on the Cu/P(VDF-TrFE)/Ni Organic Memtranstor. ACS APPLIED MATERIALS & INTERFACES 2020; 12:4673-4677. [PMID: 31898883 DOI: 10.1021/acsami.9b19510] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
We demonstrate a flexible nonvolatile multilevel memory and artificial synaptic devices based on the Cu/P(VDF-TrFE)/Ni memtranstor which exhibits pronounced nonlinear magnetoelectric effects at room temperature. The states of the magnetoelectric voltage coefficient αE of the memtranstor are used to encode binary information. By applying selective electric-field pulses, the states of αE can be switched repeatedly among 2n states (n = 1, 2, 3) in a zero dc bias magnetic field. In addition, the magnetoelectric coefficient is used to act as synaptic weight, and the induced magnetoelectric voltage VME is regarded as postsynaptic potentials (excitatory or inhibitory). The artificial synaptic devices based on the Cu/P(VDF-TrFE)/Ni memtranstor display the long-term potentiation (depression) and spiking-time-dependent plasticity behaviors. The advantages of a simple structure, flexibility, multilevel, and self-biasing make the Cu/P(VDF-TrFE)/Ni organic memtranstor a promising candidate for applications in nonvolatile memory as well as artificial synaptic devices with low energy consumption.
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Affiliation(s)
- Pei-Pei Lu
- Beijing National Laboratory for Condensed Matter Physics and Beijing Advanced Innovation Center for Materials Genome Engineering, Institute of Physics , Chinese Academy of Sciences , Beijing 100190 , China
- School of Physical Sciences , University of Chinese Academy of Sciences , Beijing 100190 , China
| | - Jian-Xin Shen
- Beijing National Laboratory for Condensed Matter Physics and Beijing Advanced Innovation Center for Materials Genome Engineering, Institute of Physics , Chinese Academy of Sciences , Beijing 100190 , China
| | - Da-Shan Shang
- Institute of Microelectronics , Chinese Academy of Sciences , Beijing 100029 , China
| | - Young Sun
- Beijing National Laboratory for Condensed Matter Physics and Beijing Advanced Innovation Center for Materials Genome Engineering, Institute of Physics , Chinese Academy of Sciences , Beijing 100190 , China
- School of Physical Sciences , University of Chinese Academy of Sciences , Beijing 100190 , China
- Songshan Lake Materials Laboratory , Dongguan , Guangdong 523808 , China
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24
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Stoliar P, Yamada H, Toyosaki Y, Sawa A. Spike-shape dependence of the spike-timing dependent synaptic plasticity in ferroelectric-tunnel-junction synapses. Sci Rep 2019; 9:17740. [PMID: 31780729 PMCID: PMC6882828 DOI: 10.1038/s41598-019-54215-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 11/10/2019] [Indexed: 11/09/2022] Open
Abstract
Resistive switching (RS) devices have attracted increasing attention for artificial synapse applications in neural networks because of their nonvolatile and analogue resistance changes. Among the neural networks, a spiking neural network (SNN) based on spike-timing-dependent plasticity (STDP) is highly energy efficient. To implement STDP in resistive switching devices, several types of voltage spikes have been proposed to date, but there have been few reports on the relationship between the STDP characteristics and spike types. Here, we report the STDP characteristics implemented in ferroelectric tunnel junctions (FTJs) by several types of spikes. Based on simulated time evolutions of superimposed spikes and taking the nonlinear current-voltage (I-V) characteristics of FTJs into account, we propose equations for simulating the STDP curve parameters of a magnitude of the conductance change (ΔGmax) and a time window (τC) from the spike parameters of a peak amplitude (Vpeak) and time durations (tp and td) for three spike types: triangle-triangle, rectangular-triangle, and rectangular-rectangular. The power consumption experiments of the STDP revealed that the power consumption under the inactive-synapse condition (spike timing |Δt| > τC) was as large as 50–82% of that under the active-synapse condition (|Δt| < τC). This finding indicates that the power consumption under the inactive-synapse condition should be reduced to minimize the total power consumption of an SNN implemented by using FTJs as synapses.
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Affiliation(s)
- P Stoliar
- National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8565, Japan.
| | - H Yamada
- National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8565, Japan
| | - Y Toyosaki
- National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8565, Japan
| | - A Sawa
- National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8565, Japan
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25
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Li J, Ge C, Lu H, Guo H, Guo EJ, He M, Wang C, Yang G, Jin K. Energy-Efficient Artificial Synapses Based on Oxide Tunnel Junctions. ACS APPLIED MATERIALS & INTERFACES 2019; 11:43473-43479. [PMID: 31702891 DOI: 10.1021/acsami.9b13434] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The development of artificial synapses has enabled the establishment of brain-inspired computing systems, which provides a promising approach for overcoming the inherent limitations of current computer systems. The two-terminal memristors that faithfully mimic the function of biological synapses have intensive prospects in the neural network field. Here, we propose a high-performance artificial synapse based on oxide tunnel junctions with oxygen vacancy migration. Both short-term and long-term plasticities are mimicked in one device. The oxygen vacancy migration through oxide ultrathin films is utilized to manipulate long-term plasticity. Essential synaptic functions, such as paired pulse facilitation, post-tetanic potentiation, as well as spike-timing-dependent plasticity, are successfully implemented in one device by finely modifying the shape of the pre- and postsynaptic spikes. Ultralow femtojoule energy consumption comparable to that of the human brain indicates its potential application in efficient neuromorphic computing. Oxide tunnel junctions proposed in this work provide an alternative approach for realizing energy-efficient brain-like chips.
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Affiliation(s)
- Jiankun Li
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics , Chinese Academy of Sciences , Beijing 100190 , China
| | - Chen Ge
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics , Chinese Academy of Sciences , Beijing 100190 , China
- School of Physical Sciences , University of Chinese Academy of Science , Beijing 100049 , China
| | - Haotian Lu
- Department of Electrical and Computer Engineering , University of Illinois , Urbana-Champaign 61820 , Illinois , United States
| | - Haizhong Guo
- School of Physical Engineering , Zhengzhou University , Zhengzhou , Henan 450001 , China
| | - Er-Jia Guo
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics , Chinese Academy of Sciences , Beijing 100190 , China
- Center of Materials Science and Optoelectronics Engineering , University of Chinese Academy of Sciences , Beijing 100049 , China
| | - Meng He
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics , Chinese Academy of Sciences , Beijing 100190 , China
| | - Can Wang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics , Chinese Academy of Sciences , Beijing 100190 , China
- School of Physical Sciences , University of Chinese Academy of Science , Beijing 100049 , China
- Songshan Lake Materials Laboratory , Dongguan , Guangdong 523808 , China
| | - Guozhen Yang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics , Chinese Academy of Sciences , Beijing 100190 , China
| | - Kuijuan Jin
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics , Chinese Academy of Sciences , Beijing 100190 , China
- School of Physical Sciences , University of Chinese Academy of Science , Beijing 100049 , China
- Songshan Lake Materials Laboratory , Dongguan , Guangdong 523808 , China
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26
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Li J, Li N, Ge C, Huang H, Sun Y, Gao P, He M, Wang C, Yang G, Jin K. Giant Electroresistance in Ferroionic Tunnel Junctions. iScience 2019; 16:368-377. [PMID: 31220760 PMCID: PMC6584484 DOI: 10.1016/j.isci.2019.05.043] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 05/07/2019] [Accepted: 05/29/2019] [Indexed: 11/24/2022] Open
Abstract
Oxide-based resistive switching devices, including ferroelectric tunnel junctions and resistance random access memory, are promising candidates for the next-generation non-volatile memory technology. In this work, we propose a ferroionic tunnel junction to realize a giant electroresistance. It functions as a ferroelectric tunnel junction at low resistance state and as a Schottky junction at high resistance state, due to interface engineering through the field-induced migration of oxygen vacancies. An extremely large electroresistance with ON/OFF ratios of 5.1×107 at room temperature and 2.1×109 at 10 K is achieved, using an ultrathin BaTiO3-δ layer as the ferroelectric barrier and a semiconducting Nb-doped SrTiO3 substrate as the bottom electrode. The results point toward an appealing way for the design of high-performance resistive switching devices based on ultrathin oxide heterostructures by ionic controlled interface engineering.
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Affiliation(s)
- Jiankun Li
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
| | - Ning Li
- International Center for Quantum Materials and Electron Microscopy Laboratory, School of Physics, Peking University, Beijing 100871, China
| | - Chen Ge
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Polar Materials and Devices, Ministry of Education, Shanghai 200241, China.
| | - Heyi Huang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
| | - Yuanwei Sun
- International Center for Quantum Materials and Electron Microscopy Laboratory, School of Physics, Peking University, Beijing 100871, China
| | - Peng Gao
- International Center for Quantum Materials and Electron Microscopy Laboratory, School of Physics, Peking University, Beijing 100871, China; Collaborative Innovation Centre of Quantum Matter, Beijing 100871, China.
| | - Meng He
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
| | - Can Wang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China; Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Guozhen Yang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
| | - Kuijuan Jin
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China; Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China.
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27
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Yan X, Pei Y, Chen H, Zhao J, Zhou Z, Wang H, Zhang L, Wang J, Li X, Qin C, Wang G, Xiao Z, Zhao Q, Wang K, Li H, Ren D, Liu Q, Zhou H, Chen J, Zhou P. Self-Assembled Networked PbS Distribution Quantum Dots for Resistive Switching and Artificial Synapse Performance Boost of Memristors. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2019; 31:e1805284. [PMID: 30589113 DOI: 10.1002/adma.201805284] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 12/10/2018] [Indexed: 05/22/2023]
Abstract
With the advent of the era of big data, resistive random access memory (RRAM) has become one of the most promising nanoscale memristor devices (MDs) for storing huge amounts of information. However, the switching voltage of the RRAM MDs shows a very broad distribution due to the random formation of the conductive filaments. Here, self-assembled lead sulfide (PbS) quantum dots (QDs) are used to improve the uniformity of switching parameters of RRAM, which is very simple comparing with other methods. The resistive switching (RS) properties of the MD with the self-assembled PbS QDs exhibit better performance than those of MDs with pure-Ga2 O3 and randomly distributed PbS QDs, such as a reduced threshold voltage, uniformly distributed SET and RESET voltages, robust retention, fast response time, and low power consumption. This enhanced performance may be attributed to the ordered arrangement of the PbS QDs in the self-assembled PbS QDs which can efficiently guide the growth direction for the conducting filaments. Moreover, biosynaptic functions and plasticity, are implemented successfully in the MD with the self-assembled PbS QDs. This work offers a new method of improving memristor performance, which can significantly expand existing applications and facilitate the development of artificial neural systems.
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Affiliation(s)
- Xiaobing Yan
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
- Department of Materials Science and Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Yifei Pei
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Huawei Chen
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Jianhui Zhao
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Zhenyu Zhou
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Hong Wang
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Lei Zhang
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Jingjuan Wang
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Xiaoyan Li
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Cuiya Qin
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Gong Wang
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Zuoao Xiao
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Qianlong Zhao
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Kaiyang Wang
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Hui Li
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Deliang Ren
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Qi Liu
- Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Hao Zhou
- Giantec Semiconductor, Inc., Shanghai, 201203, China
| | - Jingsheng Chen
- Department of Materials Science and Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Peng Zhou
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai, 200433, China
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