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Zhao B, Xu L, Peng R, Xin Z, Shi R, Wu Y, Wang B, Chen J, Pan T, Liu K. High-Performance 2D Ambipolar MoTe 2 Lateral Memristors by Mild Oxidation. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2402727. [PMID: 38958086 DOI: 10.1002/smll.202402727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 06/10/2024] [Indexed: 07/04/2024]
Abstract
2D transition metal dichalcogenides (TMDCs) have been intensively explored in memristors for brain-inspired computing. Oxidation, which is usually unavoidable and harmful in 2D TMDCs, could also be used to enhance their memristive performances. However, it is still unclear how oxidation affects the resistive switching behaviors of 2D ambipolar TMDCs. In this work, a mild oxidation strategy is developed to greatly enhance the resistive switching ratio of ambipolar 2H-MoTe2 lateral memristors by more than 10 times. Such an enhancement results from the amplified doping due to O2 and H2O adsorption and the optimization of effective gate voltage distribution by mild oxidation. Moreover, the ambipolarity of 2H-MoTe2 also enables a change of resistive switching direction, which is uncommon in 2D memristors. Consequently, as an artificial synapse, the MoTe2 device exhibits a large dynamic range (≈200) and a good linearity (1.01) in long-term potentiation and depression, as well as a high-accuracy handwritten digit recognition (>96%). This work not only provides a feasible and effective way to enhance the memristive performance of 2D ambipolar materials, but also deepens the understanding of hidden mechanisms for RS behaviors in oxidized 2D materials.
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Affiliation(s)
- Bochen Zhao
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing, 100084, P. R. China
| | - Longlong Xu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing, 100084, P. R. China
| | - Ruixuan Peng
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing, 100084, P. R. China
| | - Zeqin Xin
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing, 100084, P. R. China
| | - Run Shi
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing, 100084, P. R. China
| | - Yonghuang Wu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing, 100084, P. R. China
| | - Bolun Wang
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing, 100084, P. R. China
| | - Jiayuan Chen
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing, 100084, P. R. China
| | - Ting Pan
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing, 100084, P. R. China
| | - Kai Liu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing, 100084, P. R. China
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2
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Du S, Song Y, Yuan J, Hao R, Wu L, Lei S, Hu W. An Artificial Universal Tactile Nociceptor Based on 2D Polymer Film Memristor Arrays with Tunable Resistance Switching Behaviors. ACS APPLIED MATERIALS & INTERFACES 2024; 16:33907-33916. [PMID: 38889049 DOI: 10.1021/acsami.4c05112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Nociceptor is an important receptor in the organism's sensory system; it can perceive harmful stimuli and send signals to the brain in order to protect the body in time. The injury degree of nociceptor can be divided into three stages: self-healing injury, treatable injury, and permanent injury. However, the current studies on nociceptor simulation are limited to the self-healing stage due to the limitation of the untunable resistance switching behavior of memristors. In this study, we constructed Al/2DPTPAK+TAPB/Ag memristor arrays with adjustable memory behaviors to emulate the nociceptor of biological neural network of all three stages. For this purpose, a PDMS/AgNWs/ITO/PET pressure sensor was assembled to mimic the tactile perception of the skin. The memristor arrays can not only simulate all the response of nociceptor, i.e., the threshold, relaxation, no adaptation, and sensitization with the self-healing injury, but can also simulate the treatable injury and the permanent injury. These behaviors are both demonstrated with a single memristor and in the form of pattern mapping of the memristor array.
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Affiliation(s)
- Shaolin Du
- Key Laboratory of Organic Integrated Circuits, Ministry of Education & Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science & Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin University, Tianjin 300072, China
| | - Yaru Song
- Key Laboratory of Organic Integrated Circuits, Ministry of Education & Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science & Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin University, Tianjin 300072, China
- State Key Laboratory of Fluorinated Functional Membrane Materials, Shandong Dongyue Polymer Material Co., Ltd., Zibo 256401, China
| | - Jiangyan Yuan
- Key Laboratory of Organic Integrated Circuits, Ministry of Education & Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science & Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin University, Tianjin 300072, China
| | - Ruisha Hao
- Key Laboratory of Organic Integrated Circuits, Ministry of Education & Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science & Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin University, Tianjin 300072, China
| | - Lingli Wu
- Medical College, Northwest Minzu University, Lanzhou 730000, China
| | - Shengbin Lei
- Key Laboratory of Organic Integrated Circuits, Ministry of Education & Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science & Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin University, Tianjin 300072, China
- School of Chemistry and Chemical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
| | - Wenping Hu
- Key Laboratory of Organic Integrated Circuits, Ministry of Education & Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science & Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin University, Tianjin 300072, China
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3
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Ojha D, Huang YH, Lin YL, Chatterjee R, Chang WY, Tseng YC. Neuromorphic Computing with Emerging Antiferromagnetic Ordering in Spin-Orbit Torque Devices. NANO LETTERS 2024; 24:7706-7715. [PMID: 38869369 PMCID: PMC11212055 DOI: 10.1021/acs.nanolett.4c01712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 06/05/2024] [Accepted: 06/07/2024] [Indexed: 06/14/2024]
Abstract
Field-free switching (FFS) and spin-orbit torque (SOT)-based neuromorphic characteristics were realized in a W/Pt/Co/NiO/Pt heterostructure with a perpendicular exchange bias (HEB) for brain-inspired neuromorphic computing (NC). Experimental results using NiO-based SOT devices guided the development of fully spin-based artificial synapses and sigmoidal neurons for implementation in a three-layer artificial neural network. This system achieved impressive accuracies of 91-96% when applied to the Modified National Institute of Standards and Technology (MNIST) image data set and 78.85-81.25% when applied to Fashion MNIST images, due presumably to the emergence of robust NiO antiferromagnetic (AFM) ordering. The emergence of AFM ordering favored the FFS with an enhanced HEB, which suppressed the memristivity and reduced the recognition accuracy. This indicates a trade-off between the requirements for solid-state memory and those required for brain-inspired NC devices. Nonetheless, our findings revealed opportunities by which the two technologies could be aligned via controllable exchange coupling.
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Affiliation(s)
- Durgesh
Kumar Ojha
- International
College of Semiconductor Technology, National
Yang-Ming Chiao Tung University, Hsinchu 30010, Taiwan, ROC
- Magnetics
and Advance Ceramics Lab, Department of Physics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
- Department
of Materials Science & Engineering, National Yang-Ming Chiao Tung University, Hsinchu 30010, Taiwan, ROC
| | - Yu-Hsin Huang
- Department
of Materials Science & Engineering, National Yang-Ming Chiao Tung University, Hsinchu 30010, Taiwan, ROC
- Industry
Academia Innovation School, National Yang-Ming
Chiao Tung University, Hsinchu 30010, Taiwan, ROC
| | - Yu-Lon Lin
- Department
of Materials Science & Engineering, National Yang-Ming Chiao Tung University, Hsinchu 30010, Taiwan, ROC
| | - Ratnamala Chatterjee
- Magnetics
and Advance Ceramics Lab, Department of Physics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
- National
University of Science and Technology MISiS, Leninskiy Prospect 4, 119991 Moscow, Russia
| | - Wen-Yueh Chang
- Powerchip
Semiconductor Manufacturing Corporation, Hsinchu 30010, Taiwan, ROC
| | - Yuan-Chieh Tseng
- International
College of Semiconductor Technology, National
Yang-Ming Chiao Tung University, Hsinchu 30010, Taiwan, ROC
- Department
of Materials Science & Engineering, National Yang-Ming Chiao Tung University, Hsinchu 30010, Taiwan, ROC
- Industry
Academia Innovation School, National Yang-Ming
Chiao Tung University, Hsinchu 30010, Taiwan, ROC
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4
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Dong L, Xue B, Wei G, Yuan S, Chen M, Liu Y, Su Y, Niu Y, Xu B, Wang P. Highly Promising 2D/1D BP-C/CNT Bionic Opto-Olfactory Co-Sensory Artificial Synapses for Multisensory Integration. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2403665. [PMID: 38828870 DOI: 10.1002/advs.202403665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/08/2024] [Indexed: 06/05/2024]
Abstract
The development of high-performance artificial synaptic neuromorphic devices poses a significant challenge in the creation of biomimetic sensing neural systems that seamlessly integrate both sensory and computational functionalities. In pursuit of this objective, promising bionic opto-olfactory co-sensory artificial synapse devices are constructed utilizing the BP-C/CNT (2D/1D) hybrid filter membrane as the resistive layer. Experimental results demonstrated that the devices seamlessly integrated the light modulation, gas detection, and biological synaptic functions into a single device while addressing the challenge with separating artificial synaptic devices from sensors. These devices offered the following advantages: 1) Simulating visual synapses, they can effectively replicate fundamental synaptic functions under both electrical and optical stimulation. 2) By emulating olfactory synapse responses to specific gases, they can achieve ultra-low detection limits and rapid identification of ethanol and acetone gases. 3) They enable photo-olfactory co-sensing simulations that mimic synaptic function under light-modulated pulse conditions in distinct gas environments, facilitating the study of synaptic learning rules and Pavlovian responses. This work provides a pioneering approach for exploring highly stable 2D BP-based optoelectronics and advancing the development of biomimetic neural systems.
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Affiliation(s)
- Liyan Dong
- Xi 'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, 710021, P. R. China
| | - Baojing Xue
- Xi 'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, 710021, P. R. China
| | - Guodong Wei
- Xi 'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, 710021, P. R. China
- Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering, Taiyuan, 030024, P. R. China
| | - Shuai Yuan
- Xi 'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, 710021, P. R. China
| | - Mi Chen
- Xi 'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, 710021, P. R. China
| | - Yue Liu
- Xi 'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, 710021, P. R. China
| | - Ying Su
- Xi 'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, 710021, P. R. China
| | - Yong Niu
- Xi 'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, 710021, P. R. China
| | - Bingshe Xu
- Xi 'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, 710021, P. R. China
- Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering, Taiyuan, 030024, P. R. China
| | - Pan Wang
- Xi 'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, 710021, P. R. China
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5
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Bag A, Ghosh G, Sultan MJ, Chouhdry HH, Hong SJ, Trung TQ, Kang GY, Lee NE. Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2403150. [PMID: 38699932 DOI: 10.1002/adma.202403150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 04/16/2024] [Indexed: 05/05/2024]
Abstract
In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.
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Affiliation(s)
- Atanu Bag
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Research Centre for Advanced Materials Technology, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Gargi Ghosh
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - M Junaid Sultan
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Hamna Haq Chouhdry
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Seok Ju Hong
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Tran Quang Trung
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Geun-Young Kang
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Nae-Eung Lee
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Research Centre for Advanced Materials Technology, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Institute of Quantum Biophysics (IQB) and Biomedical Institute for Convergence at SKKU (BICS), Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
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6
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Chen K, Pan K, He S, Liu R, Zhou Z, Zhu D, Liu Z, He Z, Sun H, Wang M, Wang K, Tang M, Liu J. Mimicking Bidirectional Inhibitory Synapse Using a Porous-Confined Ionic Memristor with Electrolyte/Tris(4-aminophenyl)amine Neurotransmitter. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400966. [PMID: 38483027 PMCID: PMC11109647 DOI: 10.1002/advs.202400966] [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: 01/26/2024] [Indexed: 05/23/2024]
Abstract
Ionic memristors can emulate brain-like functions of biological synapses for neuromorphic technologies. Apart from the widely studied excitatory-excitatory and excitatory-inhibitory synapses, reports on memristors with the inhibitory-inhibitory synaptic behaviors remain a challenge. Here, the first biaxially inhibited artificial synapse is demonstrated, consisting of a solid electrolyte and conjugated microporous polymers bilayer as neurotransmitter, with the former serving as an ion reservoir and the latter acting as a confined transport. Due to the migration, trapping, and de-trapping of ions within the nanoslits, the device poses inhibitory synaptic plasticity under both positive and negative stimuli. Remarkably, the artificial synapse is able to maintain a low level of stable nonvolatile memory over a long period of time (≈60 min) after multiple stimuli, with feature-inferencing/-training capabilities of neural node in neuromorphic computing. This work paves a reliable strategy for constructing nanochannel ionic memristive materials toward fully inhibitory synaptic devices.
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Affiliation(s)
- Kang Chen
- School of Materials Science and EngineeringXiangtan UniversityNorth Second Ring Road, YuhuXiangtanHunan411105China
| | - Keyuan Pan
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Shang He
- School of Materials Science and EngineeringXiangtan UniversityNorth Second Ring Road, YuhuXiangtanHunan411105China
| | - Rui Liu
- School of Materials Science and EngineeringXiangtan UniversityNorth Second Ring Road, YuhuXiangtanHunan411105China
| | - Zhe Zhou
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Duoyi Zhu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Zhengdong Liu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Zixi He
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Hongchao Sun
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Min Wang
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Kaili Wang
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
| | - Minghua Tang
- School of Materials Science and EngineeringXiangtan UniversityNorth Second Ring Road, YuhuXiangtanHunan411105China
| | - Juqing Liu
- Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM)Nanjing Tech University (NanjingTech)30 South Puzhu RoadNanjing211816China
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7
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Kunwar S, Cucciniello N, Mazza AR, Zhang D, Santillan L, Freiman B, Roy P, Jia Q, MacManus-Driscoll JL, Wang H, Nie W, Chen A. Reconfigurable Resistive Switching in VO 2/La 0.7Sr 0.3MnO 3/Al 2O 3 (0001) Memristive Devices for Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2024; 16:19103-19111. [PMID: 38578811 DOI: 10.1021/acsami.3c19032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/07/2024]
Abstract
The coexistence of nonvolatile and volatile switching modes in a single memristive device provides flexibility to emulate both neuronal and synaptic functions in the brain. Furthermore, such a device structure may eliminate the need for additional circuit elements such as transistor-based selectors, enabling low-power consumption and high-density device integration in fully memristive spiking neural networks. In this work, we report dual resistive switching (RS) modes in VO2/La0.7Sr0.3MnO3 (LSMO) bilayer memristive devices. Specifically, the nonvolatile RS is driven by the movement of oxygen vacancies (Vo) at the VO2/LSMO interface and requires a higher biasing voltage, whereas the volatile RS is controlled by the metal-insulator transition (MIT) of VO2 under a lower biasing voltage. The simple device structure is electrically driven between the two RS modes and thus can operate as a one selector-one resistor (1S1R) cell, which is a desirable feature in memristive crossbar arrays to avoid the sneak-path current issue. The RS modes are found to be stable and repeatable and can be reconfigured by exploiting the interfacial and phase transition properties, and thus, they hold great promise for applications in memristive neural networks and neuromorphic computing.
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Affiliation(s)
- Sundar Kunwar
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Nicholas Cucciniello
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Department of Materials Design and Innovation, University at Buffalo - The State University of New York, Buffalo, New York 14260, United States
| | - Alessandro R Mazza
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Materials Science and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Di Zhang
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Luis Santillan
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Ben Freiman
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Pinku Roy
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Quanxi Jia
- Department of Materials Design and Innovation, University at Buffalo - The State University of New York, Buffalo, New York 14260, United States
| | - Judith L MacManus-Driscoll
- Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, U.K
| | - Haiyan Wang
- School of Materials Engineering, Purdue University, West Lafayette, Indiana 47907, United States
| | - Wanyi Nie
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Aiping Chen
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
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8
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Choi S, Shin J, Park G, Eo JS, Jang J, Yang JJ, Wang G. 3D-integrated multilayered physical reservoir array for learning and forecasting time-series information. Nat Commun 2024; 15:2044. [PMID: 38448419 PMCID: PMC10917743 DOI: 10.1038/s41467-024-46323-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/22/2024] [Indexed: 03/08/2024] Open
Abstract
A wide reservoir computing system is an advanced architecture composed of multiple reservoir layers in parallel, which enables more complex and diverse internal dynamics for multiple time-series information processing. However, its hardware implementation has not yet been realized due to the lack of a high-performance physical reservoir and the complexity of fabricating multiple stacks. Here, we achieve a proof-of-principle demonstration of such hardware made of a multilayered three-dimensional stacked 3 × 10 × 10 tungsten oxide memristive crossbar array, with which we further realize a wide physical reservoir computing for efficient learning and forecasting of multiple time-series data. Because a three-layer structure allows the seamless and effective extraction of intricate three-dimensional local features produced by various temporal inputs, it can readily outperform two-dimensional based approaches extensively studied previously. Our demonstration paves the way for wide physical reservoir computing systems capable of efficiently processing multiple dynamic time-series information.
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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
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, 93106, USA
| | - Jaeho Shin
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Department of Chemistry, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Gwanyeong Park
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jung Sun Eo
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jingon Jang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- School of Computer and Information Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul, 01897, Republic of Korea
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
| | - Gunuk Wang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
- Department of Integrative Energy Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea.
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9
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Hwang TG, Park H, Cho WJ. Organic-Inorganic Hybrid Synaptic Transistors: Methyl-Silsesquioxanes-Based Electric Double Layer for Enhanced Synaptic Functionality and CMOS Compatibility. Biomimetics (Basel) 2024; 9:157. [PMID: 38534842 DOI: 10.3390/biomimetics9030157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 02/29/2024] [Accepted: 02/29/2024] [Indexed: 03/28/2024] Open
Abstract
Electrical double-layer (EDL) synaptic transistors based on organic materials exhibit low thermal and chemical stability and are thus incompatible with complementary metal oxide semiconductor (CMOS) processes involving high-temperature operations. This paper proposes organic-inorganic hybrid synaptic transistors using methyl silsesquioxane (MSQ) as the electrolyte. MSQ, derived from the combination of inorganic silsesquioxanes and the organic methyl (-CH3) group, exhibits exceptional thermal and chemical stability, thus ensuring compatibility with CMOS processes. We fabricated Al/MSQ electrolyte/Pt capacitors, exhibiting a substantial capacitance of 1.89 µF/cm2 at 10 Hz. MSQ-based EDL synaptic transistors demonstrated various synaptic behaviors, such as excitatory post-synaptic current, paired-pulse facilitation, signal pass filtering, and spike-number-dependent plasticity. Additionally, we validated synaptic functions such as information storage and synapse weight adjustment, simulating brain synaptic operations through potentiation and depression. Notably, these synaptic operations demonstrated stability over five continuous operation cycles. Lastly, we trained a multi-layer artificial deep neural network (DNN) using a handwritten Modified National Institute of Standards and Technology image dataset. The DNN achieved an impressive recognition rate of 92.28%. The prepared MSQ-based EDL synaptic transistors, with excellent thermal/chemical stability, synaptic functionality, and compatibility with CMOS processes, harbor tremendous potential as materials for next-generation artificial synapse components.
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Affiliation(s)
- Tae-Gyu Hwang
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea
| | - Hamin Park
- Department of Electronic Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea
| | - Won-Ju Cho
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea
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10
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Liu X, Sun C, Ye X, Zhu X, Hu C, Tan H, He S, Shao M, Li RW. Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2311472. [PMID: 38421081 DOI: 10.1002/adma.202311472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/06/2024] [Indexed: 03/02/2024]
Abstract
Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.
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Affiliation(s)
- Xuerong Liu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Cui Sun
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Xiaoyu Ye
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Xiaojian Zhu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Cong Hu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Hongwei Tan
- Department of Applied Physics, Aalto University, Aalto, FI-00076, Finland
| | - Shang He
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Mengjie Shao
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Run-Wei Li
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
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11
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Attri R, Mondal I, Yadav B, Kulkarni GU, Rao CNR. Neuromorphic devices realised using self-forming hierarchical Al and Ag nanostructures: towards energy-efficient and wide ranging synaptic plasticity. MATERIALS HORIZONS 2024; 11:737-746. [PMID: 38018415 DOI: 10.1039/d3mh01367g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Closely mimicking the hierarchical structural topology with emerging behavioral functionalities of biological neural networks in neuromorphic devices is considered of prime importance for the realization of energy-efficient intelligent systems. In this article, we report an artificial synaptic network (ASN) comprising of hierarchical structures of isolated Al and Ag micro-nano structures developed via the utilization of a desiccated crack pattern, anisotropic dewetting, and self-formation. The strategically designed ASN, despite having multiple synaptic junctions between electrodes, exhibits a threshold switching (Vth ∼ 1-2 V) with an ultra-low energy requirement of ∼1.3 fJ per synaptic event. Several configurations of the order of hierarchy in the device architecture are studied comprehensively to identify the importance of the individual metallic components in contributing to the threshold switching and energy-minimization. The emerging potentiation behavior of the conductance (G) profile under electrical stimulation and its permanence beyond are realized over a wide current compliance range of 0.25 to 300 μA, broadly classifying the short- and long-term potentiation grounded on the characteristics of filamentary structures. The scale-free correlation of potentiation in the device hosting metallic filaments of diverse shapes and strengths could provide an ideal platform for understanding and replicating the complex behavior of the brain for neuromorphic computing.
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Affiliation(s)
- Rohit Attri
- New Chemistry Unit and School of Advanced Materials (SAMat), Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India.
| | - Indrajit Mondal
- Chemistry and Physics of Materials Unit and School of Advanced Materials (SAMat), Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India
| | - Bhupesh Yadav
- Chemistry and Physics of Materials Unit and School of Advanced Materials (SAMat), Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India
| | - Giridhar U Kulkarni
- Chemistry and Physics of Materials Unit and School of Advanced Materials (SAMat), Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India
| | - C N R Rao
- New Chemistry Unit and School of Advanced Materials (SAMat), Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India.
- Chemistry and Physics of Materials Unit and School of Advanced Materials (SAMat), Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India
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12
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Weng Z, Zheng H, Li L, Lei W, Jiang H, Ang KW, Zhao Z. Reliable Memristor Crossbar Array Based on 2D Layered Nickel Phosphorus Trisulfide for Energy-Efficient Neuromorphic Hardware. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2304518. [PMID: 37752744 DOI: 10.1002/smll.202304518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/04/2023] [Indexed: 09/28/2023]
Abstract
Designing reliable and energy-efficient memristors for artificial synaptic arrays in neuromorphic computing beyond von Neumann architecture remains a challenge. Here, memristors based on emerging layered nickel phosphorus trisulfide (NiPS3 ) are reported that exhibit several favorable characteristics, including uniform bipolar nonvolatile switching with small operating voltage (<1 V), fast switching speed (< 20 ns), high On/Off ratio (>102 ), and the ability to achieve programmable multilevel resistance states. Through direct experimental evidence using transmission electron microscopy and energy dispersive X-ray spectroscopy, it is revealed that the resistive switching mechanism in the Ti/NiPS3 /Au device is related to the formation and dissolution of Ti conductive filaments. Intriguingly, further investigation into the microstructural and chemical properties of NiPS3 suggests that the penetration of Ti ions is accompanied by the drift of phosphorus-sulfur ions, leading to induced P/S vacancies that facilitate the formation of conductive filaments. Furthermore, it is demonstrated that the memristor, when operating in quasi-reset mode, effectively emulates long-term synaptic weight plasticity. By utilizing a crossbar array, multipattern memorization and multiply-and-accumulate (MAC) operations are successfully implemented. Moreover, owing to the highly linear and symmetric multiple conductance states, a high pattern recognition accuracy of ≈96.4% is demonstrated in artificial neural network simulation for neuromorphic systems.
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Affiliation(s)
- Zhengjin Weng
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Haofei Zheng
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
| | - Lingqi Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
| | - Wei Lei
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Helong Jiang
- State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology Chinese Academy of Sciences, Nanjing, 210008, China
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
- Institute of Materials Research and Engineering, A*STAR, Singapore, 138634, Singapore
| | - Zhiwei Zhao
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing, 210096, China
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13
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Fang H, Wang J, Nie F, Zhang N, Yu T, Zhao L, Shi C, Zhang P, He B, Lü W, Zheng L. Giant Electroresistance in Ferroelectric Tunnel Junctions via High-Throughput Designs: Toward High-Performance Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2024; 16:1015-1024. [PMID: 38156871 DOI: 10.1021/acsami.3c13171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Ferroelectric tunnel junctions (FTJs) have been regarded as one of the most promising candidates for next-generation devices for data storage and neuromorphic computing owing to their advantages such as fast operation speed, low energy consumption, convenient 3D stack ability, etc. Here, dramatically different from the conventional engineering approaches, we have developed a tunnel barrier decoration strategy to improve the ON/OFF ratio, where the ultrathin SrTiO3 (STO) dielectric layers are periodically mounted onto the BaTiO3 (BTO) ferroelectric tunnel layer using the high-throughput technique. The inserted STO enhances the local tetragonality of the BTO, resulting in a strengthened ferroelectricity in the tunnel layer, which greatly improves the OFF state and reduces the ON state. Combined with the optimized oxygen migration, which can further manipulate the tunneling barrier, a record-high ON/OFF ratio of ∼108 has been achieved. Furthermore, utilizing these FTJ-based artificial synapses, an artificial neural network has been simulated via back-propagation algorithms, and a classification accuracy as high as 92% has been achieved. This study screens out the prominent FTJ by the high-throughput technique, advancing the tunnel layer decoration at the atomic level in the FTJ design and offering a fundamental understanding of the multimechanisms in the tunnel barrier.
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Affiliation(s)
- Hong Fang
- Functional Materials and Acousto-Optic Instruments Institute, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150080, China
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Jie Wang
- Functional Materials and Acousto-Optic Instruments Institute, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150080, China
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Fang Nie
- School of Physics, State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China
| | - Nana Zhang
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Tongliang Yu
- School of Physics, State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China
| | - Le Zhao
- School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Chaoqun Shi
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Peng Zhang
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Bin He
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Weiming Lü
- Functional Materials and Acousto-Optic Instruments Institute, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150080, China
- Spintronics Institute, University of Jinan, Jinan 250022, China
| | - Limei Zheng
- School of Physics, State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China
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14
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Sun C, Liu X, Yao Q, Jiang Q, Xia X, Shen Y, Ye X, Tan H, Gao R, Zhu X, Li RW. A Discolorable Flexible Synaptic Transistor for Wearable Health Monitoring. ACS NANO 2024; 18:515-525. [PMID: 38126328 DOI: 10.1021/acsnano.3c08357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Multifunctional intelligent wearable electronics, providing integrated physiological signal analysis, storage, and display for real-time and on-site health status diagnosis, have great potential to revolutionize health monitoring technologies. Advanced wearable systems combine isolated digital processor, memory, and display modules for function integration; however, they suffer from compatibility and reliability issues. Here, we introduce a flexible multifunctional electrolyte-gated transistor (EGT) that integrates synaptic learning, memory, and autonomous discoloration functionalities for intelligent wearable application. This device exhibits synergistic light absorption coefficient changes during voltage-gated ion doping that modulate the electrical conductance changes for synaptic function implementation. By adaptively changing color, the EGT can differentiate voltage pulse inputs with different frequency, amplitude, and duration parameters, exhibiting excellent reversibility and reliability. We developed a smart wearable monitoring system that incorporates EGT devices and sensors for respiratory and electrocardiogram signal analysis, providing health warnings through real-time and on-site discoloration. This study represents a significant step toward smart wearable technologies for health management, offering health evaluation through intelligent displays.
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Affiliation(s)
- Cui Sun
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Xuerong Liu
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Quanxing Yao
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Qian Jiang
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- College of Materials Sciences and Optoelectronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiangling Xia
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- College of Materials Sciences and Optoelectronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Youfeng Shen
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- College of Materials Sciences and Optoelectronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoyu Ye
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- College of Materials Sciences and Optoelectronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hongwei Tan
- Department of Applied Physics, Aalto University, Aalto FI-00076, Finland
| | - Runsheng Gao
- National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan
| | - Xiaojian Zhu
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Run-Wei Li
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
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15
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Li J, Lei Y, Wang Z, Meng H, Zhang W, Li M, Tan Q, Li Z, Guo W, Wen S, Zhang J. High-Density Artificial Synapse Array Consisting of Homogeneous Electrolyte-Gated Transistors. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305430. [PMID: 38018350 PMCID: PMC10797465 DOI: 10.1002/advs.202305430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 10/25/2023] [Indexed: 11/30/2023]
Abstract
The artificial synapse array with an electrolyte-gated transistor (EGT) as an array unit presents considerable potential for neuromorphic computation. However, the integration of EGTs faces the drawback of the conflict between the polymer electrolytes and photo-lithography. This study presents a scheme based on a lateral-gate structure to realize high-density integration of EGTs and proposes the integration of 100 × 100 EGTs into a 2.5 × 2.5 cm2 glass, with a unit density of up to 1600 devices cm-2 . Furthermore, an electrolyte framework is developed to enhance the array performance, with ionic conductivity of up to 2.87 × 10-3 S cm-1 owing to the porosity of zeolitic imidazolate frameworks-67. The artificial synapse array realizes image processing functions, and exhibits high performance and homogeneity. The handwriting recognition accuracy of a representative device reaches 92.80%, with the standard deviation of all the devices being limited to 9.69%. The integrated array and its high performance demonstrate the feasibility of the scheme and provide a solid reference for the integration of EGTs.
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Affiliation(s)
- Jun Li
- School of Material Science and EngineeringShanghai UniversityJiadingShanghai201800P. R. China
- Key Laboratory of Advanced Display and System ApplicationsMinistry of EducationShanghai UniversityShanghai200072P. R. China
- School of MicroelectronicsShanghai UniversityJiadingShanghai201800P. R. China
| | - Yuxing Lei
- School of Material Science and EngineeringShanghai UniversityJiadingShanghai201800P. R. China
| | - Zexin Wang
- School of Material Science and EngineeringShanghai UniversityJiadingShanghai201800P. R. China
| | - Hu Meng
- Central Research InstituteBOE Technology Group Company, Ltd.Beijing100176P. R. China
| | - Wenkui Zhang
- School of MicroelectronicsShanghai UniversityJiadingShanghai201800P. R. China
| | - Mengjiao Li
- School of MicroelectronicsShanghai UniversityJiadingShanghai201800P. R. China
| | - Qiuyun Tan
- Central Research InstituteBOE Technology Group Company, Ltd.Beijing100176P. R. China
| | - Zeyuan Li
- Central Research InstituteBOE Technology Group Company, Ltd.Beijing100176P. R. China
| | - Wei Guo
- Central Research InstituteBOE Technology Group Company, Ltd.Beijing100176P. R. China
| | - Shengkai Wen
- School of Material Science and EngineeringShanghai UniversityJiadingShanghai201800P. R. China
| | - Jianhua Zhang
- Key Laboratory of Advanced Display and System ApplicationsMinistry of EducationShanghai UniversityShanghai200072P. R. China
- School of MicroelectronicsShanghai UniversityJiadingShanghai201800P. R. China
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16
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Choi S, Moon T, Wang G, Yang JJ. Filament-free memristors for computing. NANO CONVERGENCE 2023; 10:58. [PMID: 38110639 PMCID: PMC10728429 DOI: 10.1186/s40580-023-00407-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 12/06/2023] [Indexed: 12/20/2023]
Abstract
Memristors have attracted increasing attention due to their tremendous potential to accelerate data-centric computing systems. The dynamic reconfiguration of memristive devices in response to external electrical stimuli can provide highly desirable novel functionalities for computing applications when compared with conventional complementary-metal-oxide-semiconductor (CMOS)-based devices. Those most intensively studied and extensively reviewed memristors in the literature so far have been filamentary type memristors, which typically exhibit a relatively large variability from device to device and from switching cycle to cycle. On the other hand, filament-free switching memristors have shown a better uniformity and attractive dynamical properties, which can enable a variety of new computing paradigms but have rarely been reviewed. In this article, a wide range of filament-free switching memristors and their corresponding computing applications are reviewed. Various junction structures, switching properties, and switching principles of filament-free memristors are surveyed and discussed. Furthermore, we introduce recent advances in different computing schemes and their demonstrations based on non-filamentary memristors. This Review aims to present valuable insights and guidelines regarding the key computational primitives and implementations enabled by these filament-free switching memristors.
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Affiliation(s)
- Sanghyeon Choi
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, 93106, USA
| | - Taehwan Moon
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Gunuk Wang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Department of Integrative Energy Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
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17
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Abstract
Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.
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Affiliation(s)
- Ke He
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Cong Wang
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Yongli He
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Jiangtao Su
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Xiaodong Chen
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore 636921, Singapore
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18
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Jeon YR, Kim D, Ku B, Chung C, Choi C. Synaptic Characteristics of Atomic Layer-Deposited Ferroelectric Lanthanum-Doped HfO 2 (La:HfO 2) and TaN-Based Artificial Synapses. ACS APPLIED MATERIALS & INTERFACES 2023. [PMID: 38041654 DOI: 10.1021/acsami.3c13159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2023]
Abstract
Analog synaptic devices have made significant advances based on various electronic materials that can realize the biological synapse properties of neuromorphic computing. Ferroelectric (FE) HfO2-based materials with nonvolatile and low power consumption characteristics are being studied as promising materials for application to analog synaptic devices. The gradual reversal of FE multilevel polarization results in precise changes in the channel conductance and allows analogue synaptic weight updates. However, there have been few studies of FE synaptic devices doped with La, Y, and Gd. Furthermore, an investigation of interface quality is also crucial to enhance the remnant polarization (Pr), synaptic conductance linearity, and reliability characteristics. In this study, we demonstrate improved FE and artificial synaptic characteristics using an atomic layer-deposited (ALD) lanthanum-doped HfO2 (La:HfO2) and TaN electrode in the structure of an FE thin-film transistor (ITO/IGZO/La:HfO2/TaN), where indium-tin oxide (ITO) and indium-gallium-zinc oxide (IGZO) were used as source/drain and channel materials, respectively. Improved Pr and lower surface roughness were achieved by doped HfO2 and ALD TaN thin films. This synaptic transistor shows long-term potentiation and long-term depression with 200 levels of conductance states, high linearity (Ap, 0.97; Ad, 0.86), high Gmax/Gmin (∼6.1), and low cycle-to-cycle variability. In addition, a pattern recognition accuracy higher than 90% was achieved in an artificial neural network simulation.
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Affiliation(s)
- Yu-Rim Jeon
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Duho Kim
- Division of Materials Science & Engineering, Hanyang University, Seoul 04763, Korea
| | - Boncheol Ku
- Division of Materials Science & Engineering, Hanyang University, Seoul 04763, Korea
| | - Chulwon Chung
- Department of Energy Engineering, Hanyang University, Seoul 04763, Korea
| | - Changhwan Choi
- Division of Materials Science & Engineering, Hanyang University, Seoul 04763, Korea
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19
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Minnekhanov A, Matsukatova A, Trofimov A, Nesmelov A, Zavyalov S, Demin V, Emelyanov A. Reliable Memristive Synapses Based on Parylene-MoO x Nanocomposites for Neuromorphic Applications. ACS APPLIED MATERIALS & INTERFACES 2023; 15:54996-55008. [PMID: 37962902 DOI: 10.1021/acsami.3c13956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Memristive devices, known for their nonvolatile resistive switching, are promising components for next-generation neuromorphic computing systems, which mimic the brain's neural architecture. Specifically, these devices are well-suited for functioning as artificial synapses due to their analogue tunability and low energy consumption. However, the improvement of their performance and reliability remains a pressing challenge. In this study, we report the development and comprehensive characterization of memristive devices based on a parylene-MoOx (PPX-Mo) nanocomposite layer, which exhibit improved characteristics over their parylene-based counterparts: lower switching voltage and energy, smaller dispersion, and better resistive plasticity. A robust statistical analysis identified the optimal synthesis parameters for these devices, providing valuable insights for future device optimization. The most probable resistive switching mechanism of the devices is proposed. By successfully integrating these memristors into a neuromorphic computing model and showcasing their scalability in crossbar geometry, we demonstrate their potential as functional artificial synapses. The results obtained from this study can be useful for the development of hardware-brain-inspired computational systems.
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Affiliation(s)
| | - Anna Matsukatova
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Lomonosov Moscow State University, Moscow 119991, Russia
| | - Andrey Trofimov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
| | | | - Sergey Zavyalov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
| | - Vyacheslav Demin
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
| | - Andrey Emelyanov
- National Research Centre Kurchatov Institute, Moscow 123182, Russia
- Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow 141701, Russia
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20
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Zhao J, Tong L, Niu J, Fang Z, Pei Y, Zhou Z, Sun Y, Wang Z, Wang H, Lou J, Yan X. A bidirectional thermal sensory leaky integrate-and-fire (LIF) neuron model based on bipolar NbO x volatile threshold devices with ultra-low operating current. NANOSCALE 2023; 15:17599-17608. [PMID: 37874690 DOI: 10.1039/d3nr03034b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2023]
Abstract
Brain-like artificial intelligence (AI) will become the main form and important platform in future computing. It will play an important and unique role in simulating brain functions, efficiently implementing AI algorithms, and improving computing power. Developing artificial neurons that can send facilitation/depression signals to artificial synapses, sense, and process temperature information is of great significance for achieving more efficient and compact brain-like computing systems. Herein, we have constructed a NbOx bipolar volatile threshold memristor, which could be operated by 1 μA ultra-low current and up to ∼104 switching ratios. By using a leaky integrate-and-fire (LIF) artificial neuron model, a bipolar LIF artificial neuron is constructed, which can realize the conventional threshold-driven firing, all-or-nothing spiking, refractory periods, and intensity-modulated frequency response bidirectionally at the positive/negative voltage stimulation, which will give the artificial synapse facilitation/depression signals. Furthermore, this bipolar LIF neuron can also explore different temperatures to output different signals, which could be constructed as a more compact thermal sensory neuron to avoid external harm to artificial robots. This study is of great significance for improving the computational efficiency of the system more effectively, achieving high integration density and low energy consumption artificial neural networks to meet the needs of brain-like neural computing.
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Affiliation(s)
- Jianhui Zhao
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
| | - Liang Tong
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
| | - Jiangzhen Niu
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
| | - Ziliang Fang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
| | - Yifei Pei
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Physics Science and Technology, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China
| | - Zhenyu Zhou
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
| | - Yong Sun
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
| | - Zhongrong Wang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
| | - Hong Wang
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
| | - Jianzhong Lou
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
| | - Xiaobing Yan
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China.
- Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Physics Science and Technology, Institute of Life Science and Green Development, Hebei University, Baoding 071002, China
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21
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Ding G, Zhao J, Zhou K, Zheng Q, Han ST, Peng X, Zhou Y. Porous crystalline materials for memories and neuromorphic computing systems. Chem Soc Rev 2023; 52:7071-7136. [PMID: 37755573 DOI: 10.1039/d3cs00259d] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
Porous crystalline materials usually include metal-organic frameworks (MOFs), covalent organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs) and zeolites, which exhibit exceptional porosity and structural/composition designability, promoting the increasing attention in memory and neuromorphic computing systems in the last decade. From both the perspective of materials and devices, it is crucial to provide a comprehensive and timely summary of the applications of porous crystalline materials in memory and neuromorphic computing systems to guide future research endeavors. Moreover, the utilization of porous crystalline materials in electronics necessitates a shift from powder synthesis to high-quality film preparation to ensure high device performance. This review highlights the strategies for preparing porous crystalline materials films and discusses their advancements in memory and neuromorphic electronics. It also provides a detailed comparative analysis and presents the existing challenges and future research directions, which can attract the experts from various fields (e.g., materials scientists, chemists, and engineers) with the aim of promoting the applications of porous crystalline materials in memory and neuromorphic computing systems.
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Affiliation(s)
- Guanglong Ding
- Institute for Advanced Study, Shenzhen University, Shenzhen, China.
| | - JiYu Zhao
- Institute for Advanced Study, Shenzhen University, Shenzhen, China.
- State Key Laboratory of Fine Chemicals, Frontiers Science Center for Smart Materials, Dalian University of Technology, Dalian 116024, China
- State Key Laboratory of Fine Chemicals, College of Materials Science and Engineering, Shenzhen University, Shenzhen 518060, China
| | - Kui Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen, China.
| | - Qi Zheng
- Institute for Advanced Study, Shenzhen University, Shenzhen, China.
| | - Su-Ting Han
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Xiaojun Peng
- State Key Laboratory of Fine Chemicals, Frontiers Science Center for Smart Materials, Dalian University of Technology, Dalian 116024, China
- State Key Laboratory of Fine Chemicals, College of Materials Science and Engineering, Shenzhen University, Shenzhen 518060, China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen, China.
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22
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Yang F, Wei W, Dong X, Zhao Y, Chen J, Chen J, Zhang X, Li Y. Optoelectronic bio-synaptic plasticity on neotype kesterite memristor with switching ratio >104. J Chem Phys 2023; 159:114701. [PMID: 37712793 DOI: 10.1063/5.0167187] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 08/28/2023] [Indexed: 09/16/2023] Open
Abstract
Optoelectronic memristors hold the most potential for realizing next-generation neuromorphic computation; however, memristive devices that can integrate excellent resistive switching and both electrical-/light-induced bio-synaptic behaviors are still challenging to develop. In this study, an artificial optoelectronic synapse is proposed and realized using a kesterite-based memristor with Cu2ZnSn(S,Se)4 (CZTSSe) as the switching material and Mo/Ag as the back/top electrode. Benefiting from unique electrical features and a bi-layered structure of CZTSSe, the memristor exhibits highly stable nonvolatile resistive switching with excellent spatial uniformity, concentrated Set/Reset voltage distribution (variation <0.08/0.02 V), high On/Off ratio (>104), and long retention time (>104 s). A possible mechanism of the switching behavior in such a device is proposed. Furthermore, these memristors successfully achieve essential bio-synaptic functions under both electrical and various visible light (470-655 nm) stimulations, including electrical-induced excitatory postsynaptic current, paired pulse facilitation, long-term potentiation, long-term depression, spike-timing-dependent plasticity, as well as light-stimulated short-/long-term plasticity and learning-forgetting-relearning process. As such, the proposed neotype kesterite-based memristor demonstrates significant potential in facilitating artificial optoelectronic synapses and enabling neuromorphic computation.
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Affiliation(s)
- Fengxia Yang
- Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Wenbin Wei
- Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Xiaofei Dong
- Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Yun Zhao
- Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Jiangtao Chen
- Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Jianbiao Chen
- Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Xuqiang Zhang
- Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
| | - Yan Li
- Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province, College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China
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23
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Chun SY, Song YG, Kim JE, Kwon JU, Soh K, Kwon JY, Kang CY, Yoon JH. An Artificial Olfactory System Based on a Chemi-Memristive Device. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2302219. [PMID: 37116944 DOI: 10.1002/adma.202302219] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/18/2023] [Indexed: 06/19/2023]
Abstract
Technologies based on the fusion of gas sensors and neuromorphic computing to mimic the olfactory system have immense potential. However, the implementation of neuromorphic olfactory systems remains in a state of infancy because conventional gas sensors lack the necessary functions. Therefore, this study proposes a hysteretic "chemi-memristive gas sensor" based on oxygen vacancy chemi-memristive dynamics that differ from that of conventional gas sensors. After the memristive switching operation, the redox reaction with the external gas molecules is enhanced, resulting in the generation and elimination of oxygen vacancies that induce rapid current changes. In addition, the pre-generated oxygen vacancies enhance the post-sensing properties. Therefore, fast responses, short recovery times, and hysteretic gas response are achieved by the proposed sensor at room temperature. Based on the advantageous functionality of the sensor, device-level olfactory systems that can monitor the history of input gas stimuli are experimentally demonstrated as a potential application. Moreover, analog conductance modulation induced by oxidizing and reducing gases enables the conversion of external gas stimuli into synaptic weights and hence the realization of typical synaptic functionalities without an additional device or circuit. The proposed chemi-memristive device represents an advance in the bioinspired technology adopted in creating artificial intelligence systems.
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Affiliation(s)
- Suk Yeop Chun
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, 02841, Republic of Korea
| | - Young Geun Song
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
| | - Ji Eun Kim
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
- Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Jae Uk Kwon
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
- Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Keunho Soh
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
- Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Ju Young Kwon
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
| | - Chong-Yun Kang
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, 02841, Republic of Korea
| | - Jung Ho Yoon
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
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24
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Liu H, Qin Y, Chen HY, Wu J, Ma J, Du Z, Wang N, Zou J, Lin S, Zhang X, Zhang Y, Wang H. Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205047. [PMID: 36609920 DOI: 10.1002/adma.202205047] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 12/02/2022] [Indexed: 06/17/2023]
Abstract
Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed.
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Affiliation(s)
- Hefei Liu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Yuan Qin
- Center for Power Electronics Systems, Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA
| | - Hung-Yu Chen
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jiangbin Wu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jiahui Ma
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Zhonghao Du
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Nan Wang
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jingyi Zou
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Sen Lin
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Xu Zhang
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Yuhao Zhang
- Center for Power Electronics Systems, Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA
| | - Han Wang
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
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25
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Chen S, Zhang T, Tappertzhofen S, Yang Y, Valov I. Electrochemical-Memristor-Based Artificial Neurons and Synapses-Fundamentals, Applications, and Challenges. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2301924. [PMID: 37199224 DOI: 10.1002/adma.202301924] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/22/2023] [Indexed: 05/19/2023]
Abstract
Artificial neurons and synapses are considered essential for the progress of the future brain-inspired computing, based on beyond von Neumann architectures. Here, a discussion on the common electrochemical fundamentals of biological and artificial cells is provided, focusing on their similarities with the redox-based memristive devices. The driving forces behind the functionalities and the ways to control them by an electrochemical-materials approach are presented. Factors such as the chemical symmetry of the electrodes, doping of the solid electrolyte, concentration gradients, and excess surface energy are discussed as essential to understand, predict, and design artificial neurons and synapses. A variety of two- and three-terminal memristive devices and memristive architectures are presented and their application for solving various problems is shown. The work provides an overview of the current understandings on the complex processes of neural signal generation and transmission in both biological and artificial cells and presents the state-of-the-art applications, including signal transmission between biological and artificial cells. This example is showcasing the possibility for creating bioelectronic interfaces and integrating artificial circuits in biological systems. Prospectives and challenges of the modern technology toward low-power, high-information-density circuits are highlighted.
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Affiliation(s)
- Shaochuan Chen
- Institute of Materials in Electrical Engineering 2 (IWE2), RWTH Aachen University, Sommerfeldstraße 24, 52074, Aachen, Germany
| | - Teng Zhang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Stefan Tappertzhofen
- Chair for Micro- and Nanoelectronics, Department of Electrical Engineering and Information Technology, TU Dortmund University, Martin-Schmeisser-Weg 4-6, D-44227, Dortmund, Germany
| | - Yuchao Yang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), School of Integrated Circuits, Peking University, Beijing, 100871, China
- School of Electronic and Computer Engineering, Peking University, Shenzhen, 518055, China
- Center for Brain Inspired Intelligence, Chinese Institute for Brain Research (CIBR), Beijing, 102206, China
| | - Ilia Valov
- Peter Grünberg Institute (PGI-7), Forschungszentrum Jülich, Wilhelm-Johnen-Straße, 52425, Jülich, Germany
- Institute of Electrochemistry and Energy Systems "Acad. E. Budewski", Bulgarian Academy of Sciences, Acad. G. Bonchev 10, 1113, Sofia, Bulgaria
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Li X, Zhong Y, Chen H, Tang J, Zheng X, Sun W, Li Y, Wu D, Gao B, Hu X, Qian H, Wu H. A Memristors-Based Dendritic Neuron for High-Efficiency Spatial-Temporal Information Processing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2203684. [PMID: 35735048 DOI: 10.1002/adma.202203684] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 06/06/2022] [Indexed: 06/15/2023]
Abstract
Diverse microscopic ionic dynamics help mediate the ability of a biological neural network to handle complex tasks with low energy consumption. Thus, rich internal ionic dynamics in memristors based on transition metal oxide are expected to provide a unique and useful platform for implementing energy-efficient neuromorphic computing. To this end, a titanium oxide (TiOx )-based interface-type dynamic memristor and an niobium oxide (NbOx )-based Mott memristor are integrated as an artificial dendrite and spike-firing soma, respectively, to construct a dendritic neuron unit for realizing high-efficiency spatial-temporal information processing. Further, a dendritic neural network is hardware-implemented for spatial-temporal information processing to highlight the computational advantages achieved by incorporating dendritic functions in the network. Human motion recognition is demonstrated using the Nanyang Technological University-Red Green Blue (NTU-RGB) dataset as a benchmark spatial-temporal task; it shows a nearly 20% improvement in accuracy for the memristors-based hardware incorporating dendrites and a 1000× advantage in power efficiency compared to that of the graphics processing unit (GPU). The dendritic neuron developed in this study can be considered a critical building block for implementing more bio-plausible neural networks that can manage complex spatial-temporal tasks with high efficiency.
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Affiliation(s)
- Xinyi Li
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
| | - Yanan Zhong
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu, 215123, China
| | - Hang Chen
- Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China
| | - Jianshi Tang
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
| | - Xiaojian Zheng
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
| | - Wen Sun
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
| | - Yang Li
- Department of Internet of Things Technology and Application, China Mobile Research Institute, Beijing, 100053, China
| | - Dong Wu
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
| | - Bin Gao
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
| | - Xiaolin Hu
- Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China
| | - He Qian
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
| | - Huaqiang Wu
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
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27
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Tabatabaian F, Vora SR, Mirabbasi S. Applications, functions, and accuracy of artificial intelligence in restorative dentistry: A literature review. J ESTHET RESTOR DENT 2023; 35:842-859. [PMID: 37522291 DOI: 10.1111/jerd.13079] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 08/01/2023]
Abstract
OBJECTIVE The applications of artificial intelligence (AI) are increasing in restorative dentistry; however, the AI performance is unclear for dental professionals. The purpose of this narrative review was to evaluate the applications, functions, and accuracy of AI in diverse aspects of restorative dentistry including caries detection, tooth preparation margin detection, tooth restoration design, metal structure casting, dental restoration/implant detection, removable partial denture design, and tooth shade determination. OVERVIEW An electronic search was performed on Medline/PubMed, Embase, Web of Science, Cochrane, Scopus, and Google Scholar databases. English-language articles, published from January 1, 2000, to March 1, 2022, relevant to the aforementioned aspects were selected using the key terms of artificial intelligence, machine learning, deep learning, artificial neural networks, convolutional neural networks, clustering, soft computing, automated planning, computational learning, computer vision, and automated reasoning as inclusion criteria. A manual search was also performed. Therefore, 157 articles were included, reviewed, and discussed. CONCLUSIONS Based on the current literature, the AI models have shown promising performance in the mentioned aspects when being compared with traditional approaches in terms of accuracy; however, as these models are still in development, more studies are required to validate their accuracy and apply them to routine clinical practice. CLINICAL SIGNIFICANCE AI with its specific functions has shown successful applications with acceptable accuracy in diverse aspects of restorative dentistry. The understanding of these functions may lead to novel applications with optimal accuracy for AI in restorative dentistry.
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Affiliation(s)
- Farhad Tabatabaian
- Department of Oral Health Sciences, Faculty of Dentistry, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Siddharth R Vora
- Department of Oral Health Sciences, Faculty of Dentistry, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Shahriar Mirabbasi
- Department of Electrical and Computer Engineering, Faculty of Applied Science, The University of British Columbia, Vancouver, British Columbia, Canada
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Huh W, Lee D, Jang S, Kang JH, Yoon TH, So JP, Kim YH, Kim JC, Park HG, Jeong HY, Wang G, Lee CH. Heterosynaptic MoS 2 Memtransistors Emulating Biological Neuromodulation for Energy-Efficient Neuromorphic Electronics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2211525. [PMID: 36930856 DOI: 10.1002/adma.202211525] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 03/04/2023] [Indexed: 06/16/2023]
Abstract
Heterosynaptic neuromodulation is a key enabler for energy-efficient and high-level biological neural processing. However, such manifold synaptic modulation cannot be emulated using conventional memristors and synaptic transistors. Thus, reported herein is a three-terminal heterosynaptic memtransistor using an intentional-defect-generated molybdenum disulfide channel. Particularly, the defect-mediated space-charge-limited conduction in the ultrathin channel results in memristive switching characteristics between the source and drain terminals, which are further modulated using a gate terminal according to the gate-tuned filling of trap states. The device acts as an artificial synapse controlled by sub-femtojoule impulses from both the source and gate terminals, consuming lower energy than its biological counterpart. In particular, electrostatic gate modulation, corresponding to biological neuromodulation, additionally regulates the dynamic range and tuning rate of the synaptic weight, independent of the programming (source) impulses. Notably, this heterosynaptic modulation not only improves the learning accuracy and efficiency but also reduces energy consumption in the pattern recognition. Thus, the study presents a new route leading toward the realization of highly networked and energy-efficient neuromorphic electronics.
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Affiliation(s)
- Woong Huh
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Donghun Lee
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Seonghoon Jang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jung Hoon Kang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Tae Hyun Yoon
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jae-Pil So
- Department of Physics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Yeon Ho Kim
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jong Chan Kim
- School of Materials Science and Engineering, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Ulsan, 44919, Republic of Korea
| | - Hong-Gyu Park
- Department of Physics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Hu Young Jeong
- UNIST Central Research Facilities (UCRF), Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Ulsan, 44919, 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
- Department of Integrative Energy Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology, 5 Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, Republic of Korea
| | - Chul-Ho Lee
- Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
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Li J, Qian Y, Li W, Yu S, Ke Y, Qian H, Lin YH, Hou CH, Shyue JJ, Zhou J, Chen Y, Xu J, Zhu J, Yi M, Huang W. Polymeric Memristor Based Artificial Synapses with Ultra-Wide Operating Temperature. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2209728. [PMID: 36972150 DOI: 10.1002/adma.202209728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 03/12/2023] [Indexed: 06/09/2023]
Abstract
Neuromorphic electronics, being inspired by how the brain works, hold great promise to the successful implementation of smart artificial systems. Among several neuromorphic hardware issues, a robust device functionality under extreme temperature is of particular importance for practical applications. Given that the organic memristors for artificial synapse applications are demonstrated under room temperature, achieving a robust device performance at extremely low or high temperature is still utterly challenging. In this work, the temperature issue is addressed by tuning the functionality of the solution-based organic polymeric memristor. The optimized memristor demonstrates a reliable performance under both the cryogenic and high-temperature environments. The unencapsulated organic polymeric memristor shows a robust memristive response under test temperature ranging from 77 to 573 K. Utilizing X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary-ion mass spectrometry (ToF-SIMS) depth profiling, the device working mechanism is unveiled by comparing the compositional profiles of the fresh and written organic polymeric memristors. A reversible ion migration induced by an applied voltage contributes to the characteristic switching behavior of the memristor. Herein, both the robust memristive response achieved at extreme temperatures and the verified device working mechanism will remarkably accelerate the development of memristors in neuromorphic systems.
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Affiliation(s)
- Jiayu Li
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
| | - Yangzhou Qian
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
| | - Wen Li
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
| | - Songcheng Yu
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
| | - Yunxin Ke
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
| | - Haowen Qian
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
| | - Yen-Hung Lin
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, 999077, P. R. China
| | - Cheng-Hung Hou
- Research Center for Applied Sciences, Academia Sinica, Taipei, 11529, Taiwan
| | - Jing-Jong Shyue
- Research Center for Applied Sciences, Academia Sinica, Taipei, 11529, Taiwan
| | - Jia Zhou
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
| | - Ye Chen
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
| | - Jiangping Xu
- Key Lab of Materials Chemistry for Energy Conversion & Storage of Ministry of Education, School of Chemistry & Chemical Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, P. R. China
| | - Jintao Zhu
- Key Lab of Materials Chemistry for Energy Conversion & Storage of Ministry of Education, School of Chemistry & Chemical Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, P. R. China
| | - Mingdong Yi
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
| | - Wei Huang
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NUPT), Nanjing, 210023, P. R. China
- Institute of Flexible Electronics (IFE), Northwestern Polytechnical University, Xi'an, 710072, P. R. China
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30
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El Hage R, Humbert V, Rouco V, Sánchez-Santolino G, Lagarrigue A, Seurre K, Carreira SJ, Sander A, Charliac J, Mesoraca S, Trastoy J, Briatico J, Santamaría J, Villegas JE. Bimodal ionic photomemristor based on a high-temperature oxide superconductor/semiconductor junction. Nat Commun 2023; 14:3010. [PMID: 37230971 DOI: 10.1038/s41467-023-38608-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 05/10/2023] [Indexed: 05/27/2023] Open
Abstract
Memristors, a cornerstone for neuromorphic electronics, respond to the history of electrical stimuli by varying their electrical resistance across a continuum of states. Much effort has been recently devoted to developing an analogous response to optical excitation. Here we realize a novel tunnelling photo-memristor whose behaviour is bimodal: its resistance is determined by the dual electrical-optical history. This is obtained in a device of ultimate simplicity: an interface between a high-temperature superconductor and a transparent semiconductor. The exploited mechanism is a reversible nanoscale redox reaction between both materials, whose oxygen content determines the electron tunnelling rate across their interface. The redox reaction is optically driven via an interplay between electrochemistry, photovoltaic effects and photo-assisted ion migration. Besides their fundamental interest, the unveiled electro-optic memory effects have considerable technological potential. Especially in combination with high-temperature superconductivity which, in addition to facilitating low-dissipation connectivity, brings photo-memristive effects to the realm of superconducting electronics.
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Affiliation(s)
- Ralph El Hage
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Vincent Humbert
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Victor Rouco
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Gabriel Sánchez-Santolino
- GFMC, Dpto. Física de Materiales. Universidad de Ciencias Físicas, Universidad Complutense de Madrid, 28040, Madrid, Spain
| | - Aurelien Lagarrigue
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Kevin Seurre
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Santiago J Carreira
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Anke Sander
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Jérôme Charliac
- Laboratoire de Physique des Interfaces et des Couches Minces (UMR7647), CNRS, Ecole Polytechnique, 91128, Palaiseau Cedex, France
| | - Salvatore Mesoraca
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Juan Trastoy
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Javier Briatico
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Jacobo Santamaría
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
- GFMC, Dpto. Física de Materiales. Universidad de Ciencias Físicas, Universidad Complutense de Madrid, 28040, Madrid, Spain
| | - Javier E Villegas
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France.
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31
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Liu X, Wang S, Di Z, Wu H, Liu C, Zhou P. An Optoelectronic Synapse Based on Two-Dimensional Violet Phosphorus Heterostructure. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023:e2301851. [PMID: 37229772 PMCID: PMC10401094 DOI: 10.1002/advs.202301851] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/26/2023] [Indexed: 05/27/2023]
Abstract
Neuromorphic computing can efficiently handle data-intensive tasks and address the redundant interaction required by von Neumann architectures. Synaptic devices are essential components for neuromorphic computation. 2D phosphorene, such as violet phosphorene, show great potential in optoelectronics due to their strong light-matter interactions, while current research is mainly focused on synthesis and characterization, its application in photoelectric devices is vacant. Here, the authors combined violet phosphorene and molybdenum disulfide to demonstrate an optoelectronic synapse with a light-to-dark ratio of 106 , benefiting from a significant threshold shift due to charge transfer and trapping in the heterostructure. Remarkable synaptic properties are demonstrated, including a dynamic range (DR) of > 60 dB, 128 (7-bit) distinguishable conductance states, electro-optical dependent plasticity, short-term paired-pulse facilitation, and long-term potentiation/depression. Thanks to the excellent DR and multi-states, high-precision image classification with accuracies of 95.23% and 79.65% is achieved for the MNIST and complex Fashion-MNIST datasets, which is close to the ideal device (95.47%, 79.95%). This work opens the way for the use of emerging phosphorene in optoelectronics and provides a new strategy for building synaptic devices for high-precision neuromorphic computing.
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Affiliation(s)
- Xiaoxian Liu
- Shanghai Key Lab for Future Computing Hardware and System, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Shuiyuan Wang
- Shanghai Key Lab for Future Computing Hardware and System, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Ziye Di
- Shanghai Key Lab for Future Computing Hardware and System, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Haoqi Wu
- Shanghai Key Lab for Future Computing Hardware and System, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Chunsen Liu
- Frontier Institute of Chip and System & Qizhi Institute, Fudan University, Shanghai, 200433, China
| | - Peng Zhou
- Shanghai Key Lab for Future Computing Hardware and System, School of Microelectronics, Fudan University, Shanghai, 200433, China
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32
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Mohan JR, Mathew AJ, Nishimura K, Feng R, Medwal R, Gupta S, Rawat RS, Fukuma Y. Classification tasks using input driven nonlinear magnetization dynamics in spin Hall oscillator. Sci Rep 2023; 13:7909. [PMID: 37193725 DOI: 10.1038/s41598-023-34849-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 05/09/2023] [Indexed: 05/18/2023] Open
Abstract
The inherent nonlinear magnetization dynamics in spintronic devices make them suitable candidates for neuromorphic hardware. Among spintronic devices, spin torque oscillators such as spin transfer torque oscillators and spin Hall oscillators have shown the capability to perform recognition tasks. In this paper, with the help of micromagnetic simulations, we model and demonstrate that the magnetization dynamics of a single spin Hall oscillator can be nonlinearly transformed by harnessing input pulse streams and can be utilized for classification tasks. The spin Hall oscillator utilizes the microwave spectral characteristics of its magnetization dynamics for processing a binary data input. The spectral change due to the nonlinear magnetization dynamics assists in real-time feature extraction and classification of 4-binary digit input patterns. The performance was tested for the classification of the standard MNIST handwritten digit data set and achieved an accuracy of 83.1% in a simple linear regression model. Our results suggest that modulating time-driven input data can generate diverse magnetization dynamics in the spin Hall oscillator that can be suitable for temporal or sequential information processing.
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Affiliation(s)
- John Rex Mohan
- Department of Physics and Information Technology, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, 820-8502, Japan
| | - Arun Jacob Mathew
- Department of Physics and Information Technology, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, 820-8502, Japan
| | - Kazuma Nishimura
- Department of Physics and Information Technology, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, 820-8502, Japan
| | - Ruoyan Feng
- Department of Physics and Information Technology, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, 820-8502, Japan
| | - Rohit Medwal
- Natural Sciences and Science Education, National Institute of Education, Nanyang Technological University, Singapore, 637617, Singapore
- Department of Physics, Indian Institute of Technology, Kanpur, 208016, India
| | - Surbhi Gupta
- Natural Sciences and Science Education, National Institute of Education, Nanyang Technological University, Singapore, 637617, Singapore
- Department of Physics, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, 211004, India
| | - Rajdeep Singh Rawat
- Natural Sciences and Science Education, National Institute of Education, Nanyang Technological University, Singapore, 637617, Singapore
| | - Yasuhiro Fukuma
- Department of Physics and Information Technology, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, 820-8502, Japan.
- Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology, Kitakyushu, 808-0196, Japan.
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Guo Z, Liu J, Han X, Ma F, Rong D, Du J, Yang Y, Wang T, Li G, Huang Y, Xing J. High-Performance Artificial Synapse Based on CVD-Grown WSe 2 Flakes with Intrinsic Defects. ACS APPLIED MATERIALS & INTERFACES 2023; 15:19152-19162. [PMID: 37022796 DOI: 10.1021/acsami.3c00417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
High-performance artificial synaptic devices with rich functions are highly desired for the development of an advanced brain-like neuromorphic system. Here, we prepare synaptic devices based on a CVD-grown WSe2 flake, which has an unusual morphology of nested triangles. The WSe2 transistor exhibits robust synaptic behaviors such as excitatory postsynaptic current, paired-pulse facilitation, short-time plasticity, and long-time plasticity. Furthermore, due to its high sensitivity to light illumination, the WSe2 transistor exhibits excellent light-dosage-dependent and light wavelength-dependent plasticity, which endow the synaptic device with more intelligent learning and memory functions. In addition, WSe2 optoelectronic synapses can mimic "learning experience" behavior and associative learning behavior like the brain. An artificial neural network is simulated for pattern recognition of hand-written digital images in the MNIST data set and the best recognition accuracy could reach 92.9% based on weight updating training of our WSe2 device. Detailed surface potential analysis and PL characterization reveal that the intrinsic defects generated in growth are dominantly responsible for the controllable synaptic plasticity. Our work suggests that the CVD-grown WSe2 flake with intrinsic defects capable of robust trapping/de-trapping charges holds great application prospects in future high-performance neuromorphic computation.
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Affiliation(s)
- Zihao Guo
- School of Science, China University of Geosciences, Beijing 100083, China
| | - Jinhui Liu
- Shenyuan Honor College of Beihang University, Beijing 100191, China
| | - Xu Han
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Fangyuan Ma
- School of Science, China University of Geosciences, Beijing 100083, China
| | - Dongke Rong
- School of Science, China University of Geosciences, Beijing 100083, China
| | - Jianyu Du
- School of Science, Tianjin University of Technology, Tianjin 300000, China
| | - Yehua Yang
- School of Science, China University of Geosciences, Beijing 100083, China
| | - Tianlin Wang
- School of Science, China University of Geosciences, Beijing 100083, China
| | - Gengwei Li
- School of Science, China University of Geosciences, Beijing 100083, China
| | - Yuan Huang
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Jie Xing
- School of Science, China University of Geosciences, Beijing 100083, China
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Gupta GK, Kim IJ, Park Y, Kim MK, Lee JS. Inorganic Perovskite Quantum Dot-Mediated Photonic Multimodal Synapse. ACS APPLIED MATERIALS & INTERFACES 2023; 15:18055-18064. [PMID: 37000192 DOI: 10.1021/acsami.2c23218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Artificial synapse is the basic unit of a neuromorphic computing system. However, there is a need to explore suitable synaptic devices for the emulation of synaptic dynamics. This study demonstrates a photonic multimodal synaptic device by implementing a perovskite quantum dot charge-trapping layer in the organic poly(3-hexylthiophene-2,5-diyl) (P3HT) channel transistor. The proposed device presents favorable band alignment that facilitates spatial separation of photogenerated charge carriers. The band alignment serves as the basis of optically induced charge trapping, which enables nonvolatile memory characteristics in the device. Furthermore, high photoresponse and excellent synaptic characteristics, such as short-term plasticity, long-term plasticity, excitatory postsynaptic current, and paired-pulse facilitation, are obtained through gate voltage regulation. Photosynaptic characteristics obtained from the device showed a multiwavelength response and a large dynamic range (∼103) that is suitable for realizing a highly accurate artificial neural network. Moreover, the device showed nearly linear synaptic weight update characteristics with incremental depression electric gate pulse. The simulation based on the experimental data showed excellent pattern recognition accuracy (∼85%) after 120 epochs. The results of this study demonstrate the feasibility of the device as an optical synapse in the next-generation neuromorphic system.
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Affiliation(s)
- Goutam Kumar Gupta
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
| | - Ik-Jyae Kim
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
| | - Youngjun Park
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
| | - Min-Kyu Kim
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
| | - Jang-Sik Lee
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
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Seok H, Son S, Jathar SB, Lee J, Kim T. Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:3118. [PMID: 36991829 PMCID: PMC10058286 DOI: 10.3390/s23063118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/11/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.
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Affiliation(s)
- Hyunho Seok
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Shihoon Son
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Sagar Bhaurao Jathar
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Jaewon Lee
- School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Taesung Kim
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
- School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
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36
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A flexible artificial chemosensory neuronal synapse based on chemoreceptive ionogel-gated electrochemical transistor. Nat Commun 2023; 14:821. [PMID: 36788242 PMCID: PMC9929093 DOI: 10.1038/s41467-023-36480-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 02/01/2023] [Indexed: 02/16/2023] Open
Abstract
The human olfactory system comprises olfactory receptor neurons, projection neurons, and interneurons that perform remarkably sophisticated functions, including sensing, filtration, memorization, and forgetting of chemical stimuli for perception. Developing an artificial olfactory system that can mimic these functions has proved to be challenging. Herein, inspired by the neuronal network inside the glomerulus of the olfactory bulb, we present an artificial chemosensory neuronal synapse that can sense chemical stimuli and mimic the functions of excitatory and inhibitory neurotransmitter release in the synapses between olfactory receptor neurons, projection neurons, and interneurons. The proposed device is based on a flexible organic electrochemical transistor gated by the potential generated by the interaction of gas molecules with ions in a chemoreceptive ionogel. The combined use of a chemoreceptive ionogel and an organic semiconductor channel allows for a long retentive memory in response to chemical stimuli. Long-term memorization of the excitatory chemical stimulus can be also erased by applying an inhibitory electrical stimulus due to ion dynamics in the chemoresponsive ionogel gate electrolyte. Applying a simple device design, we were able to mimic the excitatory and inhibitory synaptic functions of chemical synapses in the olfactory system, which can further advance the development of artificial neuronal systems for biomimetic chemosensory applications.
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Song YG, Kim JE, Kwon JU, Chun SY, Soh K, Nahm S, Kang CY, Yoon JH. Highly Reliable Threshold Switching Characteristics of Surface-Modulated Diffusive Memristors Immune to Atmospheric Changes. ACS APPLIED MATERIALS & INTERFACES 2023; 15:5495-5503. [PMID: 36691225 DOI: 10.1021/acsami.2c21019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Active cation-based diffusive memristors featuring essentially volatile threshold switching have been proposed for novel applications, such as a selector in a one-selector-and-one-resistor structure and signal generators in neuromorphic computing. However, the high variability of the switching behavior, which results from the high electroforming voltage, external environmental conditions, and transition to the non-volatile switching mode in a high-current range, is considered a major impediment to such applications. Herein, for the first time, we developed a highly reliable threshold switching device immune to atmospheric changes based on an ultraviolet-ozone (UVO)-treated diffusive memristor consisting of Ag and SiO2 nanorods (NRs). UVO treatment forms a stable water reservoir on the surface of SiO2 NRs, facilitating the redox reaction and ion migration of Ag. Consequently, diffusive memristors possess reliable switching characteristics, including electroforming-free, repeatable, and consistent switching with resistance to changes in ambient conditions and compliance levels during operation. We demonstrated that our approach is suitable for various metal oxides and can be used in numerous applications.
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Affiliation(s)
- Young Geun Song
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul02791, Republic of Korea
| | - Ji Eun Kim
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul02791, Republic of Korea
- Department of Materials Science and Engineering, Korea University, Seoul02841, Republic of Korea
| | - Jae Uk Kwon
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul02791, Republic of Korea
- Department of Materials Science and Engineering, Korea University, Seoul02841, Republic of Korea
| | - Suk Yeop Chun
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul02791, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul02841, Republic of Korea
| | - Keunho Soh
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul02791, Republic of Korea
- Department of Materials Science and Engineering, Korea University, Seoul02841, Republic of Korea
| | - Sahn Nahm
- Department of Materials Science and Engineering, Korea University, Seoul02841, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul02841, Republic of Korea
| | - Chong-Yun Kang
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul02791, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul02841, Republic of Korea
| | - Jung Ho Yoon
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul02791, Republic of Korea
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38
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Sun C, Liu X, Jiang Q, Ye X, Zhu X, Li RW. Emerging electrolyte-gated transistors for neuromorphic perception. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2162325. [PMID: 36684849 PMCID: PMC9848240 DOI: 10.1080/14686996.2022.2162325] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/18/2022] [Accepted: 12/21/2022] [Indexed: 05/31/2023]
Abstract
With the rapid development of intelligent robotics, the Internet of Things, and smart sensor technologies, great enthusiasm has been devoted to developing next-generation intelligent systems for the emulation of advanced perception functions of humans. Neuromorphic devices, capable of emulating the learning, memory, analysis, and recognition functions of biological neural systems, offer solutions to intelligently process sensory information. As one of the most important neuromorphic devices, Electrolyte-gated transistors (EGTs) have shown great promise in implementing various vital neural functions and good compatibility with sensors. This review introduces the materials, operating principle, and performances of EGTs, followed by discussing the recent progress of EGTs for synapse and neuron emulation. Integrating EGTs with sensors that faithfully emulate diverse perception functions of humans such as tactile and visual perception is discussed. The challenges of EGTs for further development are given.
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Affiliation(s)
- Cui Sun
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Xuerong Liu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Qian Jiang
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoyu Ye
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaojian Zhu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Run-Wei Li
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
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39
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Application of artificial synapse based on all-inorganic perovskite memristor in neuromorphic computing. NANO MATERIALS SCIENCE 2023. [DOI: 10.1016/j.nanoms.2023.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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40
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Li P, Kim S, Tian B. Nanoenabled Trainable Systems: From Biointerfaces to Biomimetics. ACS NANO 2022; 16:19651-19664. [PMID: 36516872 PMCID: PMC9798864 DOI: 10.1021/acsnano.2c08042] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 12/09/2022] [Indexed: 05/30/2023]
Abstract
In the dynamic biological system, cells and tissues adapt to diverse environmental conditions and form memories, an essential aspect of training for survival and evolution. An understanding of the biological training principles will inform the design of biomimetic materials whose properties evolve with the environment and offer routes to programmable soft materials, neuromorphic computing, living materials, and biohybrid robotics. In this perspective, we examine the mechanisms by which cells are trained by environmental cues. We outline the artificial platforms that enable biological training and examine the relationship between biological training and biomimetic materials design. We place emphasis on nanoscale material platforms which, given their applicability to chemical, mechanical and electrical stimulation, are critical to bridging natural and synthetic systems.
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Affiliation(s)
- Pengju Li
- Pritzker
School of Molecular Engineering, The University
of Chicago, Chicago, Illinois 60637, United States
| | - Saehyun Kim
- Department
of Chemistry, The University of Chicago, Chicago, Illinois 60637, United States
| | - Bozhi Tian
- Department
of Chemistry, The University of Chicago, Chicago, Illinois 60637, United States
- The
James Franck Institute, The University of
Chicago, Chicago, Illinois 60637, United States
- The
Institute for Biophysical Dynamics, University
of Chicago, Chicago, Illinois 60637, United States
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41
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Wang S, Liu X, Zhou P. The Road for 2D Semiconductors in the Silicon Age. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2106886. [PMID: 34741478 DOI: 10.1002/adma.202106886] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/21/2021] [Indexed: 06/13/2023]
Abstract
Continued reduction in transistor size can improve the performance of silicon integrated circuits (ICs). However, as Moore's law approaches physical limits, high-performance growth in silicon ICs becomes unsustainable, due to challenges of scaling, energy efficiency, and memory limitations. The ultrathin layers, diverse band structures, unique electronic properties, and silicon-compatible processes of 2D materials create the potential to consistently drive advanced performance in ICs. Here, the potential of fusing 2D materials with silicon ICs to minimize the challenges in silicon ICs, and to create technologies beyond the von Neumann architecture, is presented, and the killer applications for 2D materials in logic and memory devices to ease scaling, energy efficiency bottlenecks, and memory dilemmas encountered in silicon ICs are discussed. The fusion of 2D materials allows the creation of all-in-one perception, memory, and computation technologies beyond the von Neumann architecture to enhance system efficiency and remove computing power bottlenecks. Progress on the 2D ICs demonstration is summarized, as well as the technical hurdles it faces in terms of wafer-scale heterostructure growth, transfer, and compatible integration with silicon ICs. Finally, the promising pathways and obstacles to the technological advances in ICs due to the integration of 2D materials with silicon are presented.
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Affiliation(s)
- Shuiyuan Wang
- ASIC & System State Key Lab, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Xiaoxian Liu
- ASIC & System State Key Lab, School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Peng Zhou
- ASIC & System State Key Lab, School of Microelectronics, Fudan University, Shanghai, 200433, China
- Frontier Institute of Chip and System, Shanghai Frontier Base of Intelligent Optoelectronics and Perception, Institute of Optoelectronics, Fudan University, Shanghai, 200433, China
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42
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Chen Y, Li D, Ren H, Tang Y, Liang K, Wang Y, Li F, Song C, Guan J, Chen Z, Lu X, Xu G, Li W, Liu S, Zhu B. Highly Linear and Symmetric Synaptic Memtransistors Based on Polarization Switching in Two-Dimensional Ferroelectric Semiconductors. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2203611. [PMID: 36156393 DOI: 10.1002/smll.202203611] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 09/01/2022] [Indexed: 06/16/2023]
Abstract
Brain-inspired neuromorphic computing hardware based on artificial synapses offers efficient solutions to perform computational tasks. However, the nonlinearity and asymmetry of synaptic weight updates in reported artificial synapses have impeded achieving high accuracy in neural networks. Here, this work develops a synaptic memtransistor based on polarization switching in a two-dimensional (2D) ferroelectric semiconductor (FES) of α-In2 Se3 for neuromorphic computing. The α-In2 Se3 memtransistor exhibits outstanding synaptic characteristics, including near-ideal linearity and symmetry and a large number of programmable conductance states, by taking the advantages of both memtransistor configuration and electrically configurable polarization states in the FES channel. As a result, the α-In2 Se3 memtransistor-type synapse reaches high accuracy of 97.76% for digit patterns recognition task in simulated artificial neural networks. This work opens new opportunities for using multiterminal FES memtransistors in advanced neuromorphic electronics.
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Affiliation(s)
- Yitong Chen
- School of Materials and Engineering, Zhejiang University, Hangzhou, 310027, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Dingwei Li
- School of Materials and Engineering, Zhejiang University, Hangzhou, 310027, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Huihui Ren
- School of Materials and Engineering, Zhejiang University, Hangzhou, 310027, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Yingjie Tang
- School of Materials and Engineering, Zhejiang University, Hangzhou, 310027, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Kun Liang
- School of Materials and Engineering, Zhejiang University, Hangzhou, 310027, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Yan Wang
- School of Materials and Engineering, Zhejiang University, Hangzhou, 310027, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Fanfan Li
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Chunyan Song
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
| | - Jiaqi Guan
- Instrumentation and Service Centre for Physical Sciences, Westlake University, Hangzhou, 310024, China
| | - Zhong Chen
- Instrumentation and Service Centre for Molecular Sciences, Westlake University, Hangzhou, 310024, China
| | - Xingyu Lu
- Instrumentation and Service Centre for Molecular Sciences, Westlake University, Hangzhou, 310024, China
| | - Guangwei Xu
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, China
| | - Wenbin Li
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, 310024, China
| | - Shi Liu
- School of Science, Westlake University, Hangzhou, Zhejiang, 310024, China
| | - Bowen Zhu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, 310024, China
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, 310024, China
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Multilayer redox-based HfO x/Al 2O 3/TiO 2 memristive structures for neuromorphic computing. Sci Rep 2022; 12:18266. [PMID: 36309573 PMCID: PMC9617901 DOI: 10.1038/s41598-022-22907-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 10/20/2022] [Indexed: 12/31/2022] Open
Abstract
Redox-based memristive devices have shown great potential for application in neuromorphic computing systems. However, the demands on the device characteristics depend on the implemented computational scheme and unifying the desired properties in one stable device is still challenging. Understanding how and to what extend the device characteristics can be tuned and stabilized is crucial for developing application specific designs. Here, we present memristive devices with a functional trilayer of HfOx/Al2O3/TiO2 tailored by the stoichiometry of HfOx (x = 1.8, 2) and the operating conditions. The device properties are experimentally analyzed, and a physics-based device model is developed to provide a microscopic interpretation and explain the role of the Al2O3 layer for a stable performance. Our results demonstrate that the resistive switching mechanism can be tuned from area type to filament type in the same device, which is well explained by the model: the Al2O3 layer stabilizes the area-type switching mechanism by controlling the formation of oxygen vacancies at the Al2O3/HfOx interface with an estimated formation energy of ≈ 1.65 ± 0.05 eV. Such stabilized area-type devices combine multi-level analog switching, linear resistance change, and long retention times (≈ 107-108 s) without external current compliance and initial electroforming cycles. This combination is a significant improvement compared to previous bilayer devices and makes the devices potentially interesting for future integration into memristive circuits for neuromorphic applications.
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44
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Zhao X, Xuan J, Li Q, Gao F, Xun X, Liao Q, Zhang Y. Roles of Low-Dimensional Nanomaterials in Pursuing Human-Machine-Thing Natural Interaction. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022:e2207437. [PMID: 36284476 DOI: 10.1002/adma.202207437] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 10/12/2022] [Indexed: 06/16/2023]
Abstract
A wide variety of low-dimensional nanomaterials with excellent properties can meet almost all the requirements of functional materials for information sensing, processing, and feedback devices. Low-dimensional nanomaterials are becoming the star of hope on the road to pursuing human-machine-thing natural interactions, benefiting from the breakthroughs in precise preparation, performance regulation, structural design, and device construction in recent years. This review summarizes several types of low-dimensional nanomaterials commonly used in human-machine-thing natural interactions and outlines the differences in properties and application areas of different materials. According to the sequence of information flow in the human-machine-thing interaction process, the representative research progress of low-dimensional nanomaterials-based information sensing, processing, and feedback devices is reviewed and the key roles played by low-dimensional nanomaterials are discussed. Finally, the development trends and existing challenges of low-dimensional nanomaterials in the field of human-machine-thing natural interaction technology are discussed.
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Affiliation(s)
- Xuan Zhao
- Academy for Advanced Interdisciplinary Science and Technology, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
- Beijing Key Laboratory for Advanced Energy Materials and Technologies, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
| | - Jingyue Xuan
- Academy for Advanced Interdisciplinary Science and Technology, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
- Beijing Key Laboratory for Advanced Energy Materials and Technologies, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
| | - Qi Li
- Academy for Advanced Interdisciplinary Science and Technology, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
- Beijing Key Laboratory for Advanced Energy Materials and Technologies, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
| | - Fangfang Gao
- Academy for Advanced Interdisciplinary Science and Technology, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
- Beijing Key Laboratory for Advanced Energy Materials and Technologies, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
| | - Xiaochen Xun
- Academy for Advanced Interdisciplinary Science and Technology, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
- Beijing Key Laboratory for Advanced Energy Materials and Technologies, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
| | - Qingliang Liao
- Academy for Advanced Interdisciplinary Science and Technology, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
- Beijing Key Laboratory for Advanced Energy Materials and Technologies, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
| | - Yue Zhang
- Academy for Advanced Interdisciplinary Science and Technology, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
- Beijing Key Laboratory for Advanced Energy Materials and Technologies, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, P. R. China
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45
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Cho SW, Jo C, Kim YH, Park SK. Progress of Materials and Devices for Neuromorphic Vision Sensors. NANO-MICRO LETTERS 2022; 14:203. [PMID: 36242681 PMCID: PMC9569410 DOI: 10.1007/s40820-022-00945-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/08/2022] [Indexed: 05/31/2023]
Abstract
The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords: smaller, faster, and smarter. (1) Smaller: Devices are becoming more compact by integrating previously separated components such as sensors, memory, and processing units. As a prime example, the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits, such as simpler circuitry, lower power consumption, and less data redundancy. (2) Swifter: Owing to the nature of physics, smaller and more integrated devices can detect, process, and react to input more quickly. In addition, the methods for sensing and processing optical information using various materials (such as oxide semiconductors) are evolving. (3) Smarter: Owing to these two main research directions, we can expect advanced applications such as adaptive vision sensors, collision sensors, and nociceptive sensors. This review mainly focuses on the recent progress, working mechanisms, image pre-processing techniques, and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.
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Affiliation(s)
- Sung Woon Cho
- Department of Advanced Components and Materials Engineering, Sunchon National University, Sunchŏn, Jeonnam, 57922, Republic of Korea
| | - Chanho Jo
- Department of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea
| | - Yong-Hoon Kim
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, Republic of Korea.
| | - Sung Kyu Park
- Department of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea.
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46
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Kwon JU, Song YG, Kim JE, Chun SY, Kim GH, Noh G, Kwak JY, Hur S, Kang CY, Jeong DS, Oh SJ, Yoon JH. Surface-Dominated HfO 2 Nanorod-Based Memristor Exhibiting Highly Linear and Symmetrical Conductance Modulation for High-Precision Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2022; 14:44550-44560. [PMID: 36149315 DOI: 10.1021/acsami.2c12247] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The switching characteristics and performance of oxide-based memristors are predominately determined by oxygen- or oxygen-vacancy-mediated redox reactions and the consequent formation of conducting filaments (CFs). Devices using oxide thin films as the switching layer usually require an electroforming process for subsequent switching operations, which induces large device-to-device variations. In addition, the hard-to-control redox reaction during repeated switching causes random fluctuations or degradation of each resistance state, hindering reliable switching operations. In this study, an HfO2 nanorod (NR)-based memristor is proposed for simultaneously achieving highly uniform, electroforming-free, fast, and reliable analogue switching properties. The well-controlled redox reaction due to the easy gas exchange with the environment at the surface of the NRs enhances the generation of oxygen or oxygen vacancies during the switching operation, resulting in electroforming-free and reliable switching behavior. In addition, the one-dimensional surface growth of CFs facilitates highly linear conductance modulation with smaller conductance changes compared with the two-dimensional volume growth in thin-film-based memristors, resulting in a high accuracy of >92% in the Modified National Institute of Standards and Technology pattern-recognition test and desirable spike-timing-dependent plasticity.
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Affiliation(s)
- Jae Uk Kwon
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul 02791, Republic of Korea
- Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Young Geun Song
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul 02791, Republic of Korea
| | - Ji Eun Kim
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul 02791, Republic of Korea
- Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Suk Yeop Chun
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul 02791, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul 02841, Republic of Korea
| | - Gu Hyun Kim
- Division of Materials Science and Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Gichang Noh
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology (KIST), Seoul 02791, Republic of Korea
| | - Joon Young Kwak
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology (KIST), Seoul 02791, Republic of Korea
| | - Sunghoon Hur
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul 02791, Republic of Korea
| | - Chong-Yun Kang
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul 02791, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul 02841, Republic of Korea
| | - Doo Seok Jeong
- Division of Materials Science and Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Soong Ju Oh
- Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Jung Ho Yoon
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul 02791, Republic of Korea
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Wang W, Gao S, Wang Y, Li Y, Yue W, Niu H, Yin F, Guo Y, Shen G. Advances in Emerging Photonic Memristive and Memristive-Like Devices. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2105577. [PMID: 35945187 PMCID: PMC9534950 DOI: 10.1002/advs.202105577] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 06/06/2022] [Indexed: 05/19/2023]
Abstract
Possessing the merits of high efficiency, low consumption, and versatility, emerging photonic memristive and memristive-like devices exhibit an attractive future in constructing novel neuromorphic computing and miniaturized bionic electronic system. Recently, the potential of various emerging materials and structures for photonic memristive and memristive-like devices has attracted tremendous research efforts, generating various novel theories, mechanisms, and applications. Limited by the ambiguity of the mechanism and the reliability of the material, the development and commercialization of such devices are still rare and in their infancy. Therefore, a detailed and systematic review of photonic memristive and memristive-like devices is needed to further promote its development. In this review, the resistive switching mechanisms of photonic memristive and memristive-like devices are first elaborated. Then, a systematic investigation of the active materials, which induce a pivotal influence in the overall performance of photonic memristive and memristive-like devices, is highlighted and evaluated in various indicators. Finally, the recent advanced applications are summarized and discussed. In a word, it is believed that this review provides an extensive impact on many fields of photonic memristive and memristive-like devices, and lay a foundation for academic research and commercial applications.
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Affiliation(s)
- Wenxiao Wang
- School of Information Science and EngineeringShandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinan250022China
| | - Song Gao
- School of Information Science and EngineeringShandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinan250022China
| | - Yaqi Wang
- School of Information Science and EngineeringShandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinan250022China
| | - Yang Li
- School of Information Science and EngineeringShandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinan250022China
| | - Wenjing Yue
- School of Information Science and EngineeringShandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinan250022China
| | - Hongsen Niu
- School of Information Science and EngineeringShandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinan250022China
| | - Feifei Yin
- School of Information Science and EngineeringShandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinan250022China
| | - Yunjian Guo
- School of Information Science and EngineeringShandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinan250022China
| | - Guozhen Shen
- School of Integrated Circuits and ElectronicsBeijing Institute of TechnologyBeijing100081China
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Xin X, Sun L, Chen J, Bao Y, Tao Y, Lin Y, Bian J, Wang Z, Zhao X, Xu H, Liu Y. Real-time numerical system convertor via two-dimensional WS2-based memristive device. Front Comput Neurosci 2022; 16:1015945. [PMID: 36185713 PMCID: PMC9517377 DOI: 10.3389/fncom.2022.1015945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
The intriguing properties of two-dimensional (2D) transition metal dichalcogenides (TMDCs) enable the exploration of new electronic device architectures, particularly the emerging memristive devices for in-memory computing applications. Implementation of arithmetic logic operations taking advantage of the non-linear characteristics of memristor can significantly improve the energy efficiency and simplify the complexity of peripheral circuits. Herein, we demonstrate an arithmetic logic unit function using a lateral volatile memristor based on layered 2D tungsten disulfide (WS2) materials and some combinational logic circuits. Removable oxygen ions were introduced into WS2 materials through oxygen plasma treatment process. The resistive switching of the memristive device caused by the thermophoresis-assisted oxygen ions migration has also been revealed. Based on the characteristics of excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and spike rate dependent plasticity (SRDP), a real-time numerical system convertor was successfully accomplished, which is a significant computing function of arithmetic logic unit. This work paves a new way for developing 2D memristive devices for future arithmetic logic applications.
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Rao G, Fang H, Zhou T, Zhao C, Shang N, Huang J, Liu Y, Du X, Li P, Jian X, Ma L, Wang J, Liu K, Wu J, Wang X, Xiong J. Robust Piezoelectricity with Spontaneous Polarization in Monolayer Tellurene and Multilayer Tellurium Film at Room Temperature for Reliable Memory. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2204697. [PMID: 35793515 DOI: 10.1002/adma.202204697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Robust neuromorphic computing in the Big Data era calls for long-term stable crossbar-array memory cells; however, the elemental segregation in the switch unit and memory unit that inevitably occurs upon cycling breaks the compositional and structural stability, making the whole memory cell a failure. Searching for a novel material without segregation that can be used for both switch and memory units is the major concern to fabricate robust and reliable nonvolatile cross-array memory cells. Tellurium (Te) is found recently to be the only peculiar material without segregation for switching, but the memory function has not been demonstrated yet. Herein, apparent piezoelectricity is experimentally confirmed with spontaneous polarization behaviors in elementary 2D Te, even in monolayer tellurene (0.4 nm), due to the highly oriented polarization of the molecular structure and the non-centrosymmetric lattice structure. A large memory window of 7000, a low working voltage of 2 V, and high on switching current up to 36.6 µA µm-1 are achieved in the as-fabricated Te-based memory device, revealing the great promise of Te for both switching and memory units in one cell without segregation. The piezoelectric Te with spontaneous polarization provides a platform to build robust, reliable, and high-density logic-in-memory chips in neuromorphic computing.
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Affiliation(s)
- Gaofeng Rao
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Hui Fang
- School of Physics, Southeast University, Nanjing, 211189, China
| | - Ting Zhou
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Chunlin Zhao
- Department of Materials Science, Sichuan University, Chengdu, 6110064, China
- College of Materials Science and Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Nianze Shang
- State Key Laboratory for Mesoscopic Physics, Frontiers Science Center for Nano-optoelectronics, School of Physics, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Jianwen Huang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Yuqing Liu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Xinchuan Du
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Peng Li
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Xian Jian
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Liang Ma
- School of Physics, Southeast University, Nanjing, 211189, China
| | - Jinlan Wang
- School of Physics, Southeast University, Nanjing, 211189, China
| | - Kaihui Liu
- State Key Laboratory for Mesoscopic Physics, Frontiers Science Center for Nano-optoelectronics, School of Physics, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Jiagang Wu
- Department of Materials Science, Sichuan University, Chengdu, 6110064, China
| | - Xianfu Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Jie Xiong
- State Key Laboratory of Electronic Thin Film and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, China
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Humbert V, El Hage R, Krieger G, Sanchez‐Santolino G, Sander A, Collin S, Trastoy J, Briatico J, Santamaria J, Preziosi D, Villegas JE. An Oxygen Vacancy Memristor Ruled by Electron Correlations. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2201753. [PMID: 35901494 PMCID: PMC9507366 DOI: 10.1002/advs.202201753] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 07/01/2022] [Indexed: 06/15/2023]
Abstract
Resistive switching effects offer new opportunities in the field of conventional memories as well as in the booming area of neuromorphic computing. Here the authors demonstrate memristive switching effects produced by a redox-driven oxygen exchange in tunnel junctions based on NdNiO3 , a strongly correlated electron system characterized by the presence of a metal-to-insulator transition (MIT). Strikingly, a strong interplay exists between the MIT and the redox mechanism, which on the one hand modifies the MIT itself, and on the other hand radically affects the tunnel resistance switching and the resistance states' lifetime. That results in a very unique temperature behavior and endows the junctions with multiple degrees of freedom. The obtained results bring up fundamental questions on the interplay between electronic correlations and the creation and mobility of oxygen vacancies in nickelates, opening a new avenue toward mimicking neuromorphic functions by exploiting the electric-field control of correlated states.
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Affiliation(s)
- Vincent Humbert
- Unité Mixte de PhysiqueCNRSThalesUniversité Paris‐SaclayPalaiseau91767France
| | - Ralph El Hage
- Unité Mixte de PhysiqueCNRSThalesUniversité Paris‐SaclayPalaiseau91767France
| | | | - Gabriel Sanchez‐Santolino
- Grupo de Física de Materiales ComplejosDpt. Física de MaterialesUniversidad Complutense de MadridMadrid28040Spain
| | - Anke Sander
- Unité Mixte de PhysiqueCNRSThalesUniversité Paris‐SaclayPalaiseau91767France
| | - Sophie Collin
- Unité Mixte de PhysiqueCNRSThalesUniversité Paris‐SaclayPalaiseau91767France
| | - Juan Trastoy
- Unité Mixte de PhysiqueCNRSThalesUniversité Paris‐SaclayPalaiseau91767France
| | - Javier Briatico
- Unité Mixte de PhysiqueCNRSThalesUniversité Paris‐SaclayPalaiseau91767France
| | - Jacobo Santamaria
- Grupo de Física de Materiales ComplejosDpt. Física de MaterialesUniversidad Complutense de MadridMadrid28040Spain
| | | | - Javier E. Villegas
- Unité Mixte de PhysiqueCNRSThalesUniversité Paris‐SaclayPalaiseau91767France
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