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Xu K, Wang T, Lu C, Song Y, Liu Y, Yu J, Liu Y, Li Z, Meng J, Zhu H, Sun QQ, Zhang DW, Chen L. Novel Two-Terminal Synapse/Neuron Based on an Antiferroelectric Hafnium Zirconium Oxide Device for Neuromorphic Computing. NANO LETTERS 2024; 24:11170-11178. [PMID: 39148056 DOI: 10.1021/acs.nanolett.4c02142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Functionally diverse devices with artificial neuron and synapse properties are critical for neuromorphic systems. We present a two-terminal artificial leaky-integrate-fire (LIF) neuron based on 6 nm Hf0.1Zr0.9O2 (HZO) antiferroelectric (AFE) thin films and develop a synaptic device through work function (WF) engineering. LIF neuron characteristics, including integration, firing, and leakage, are achieved in W/HZO/W devices due to the accumulated polarization and spontaneous depolarization of AFE HZO films. By engineering the top electrode with asymmetric WFs, we found that Au/Ti/HZO/W devices exhibit synaptic weight plasticity, such as paired-pulse facilitation and long-term potentiation/depression, achieving >90% accuracy in digit recognition within constructed artificial neural network systems. These findings suggest that AFE HZO capacitor-based neurons and WF-engineered artificial synapses hold promise for constructing efficient spiking neuron networks and artificial neural networks, thereby advancing neuromorphic computing applications based on emerging AFE HZO devices.
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Affiliation(s)
- Kangli Xu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Tianyu Wang
- School of Integrated Circuits, Shandong University, Jinan 250100, China
- Suzhou Research Institute of Shandong University, Suzhou 215123, China
- State Key Laboratory of Materials for Integrated Circuits, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, 865 Changning Road, Shanghai 200050, China
| | - Chen Lu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Yifan Song
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Yongkai Liu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Jiajie Yu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Yinchi Liu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Zhenhai Li
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Jialin Meng
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Hao Zhu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Qing-Qing Sun
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - David Wei Zhang
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
- Jiashan Fudan Institute, Jiaxing 314110, China
| | - Lin Chen
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
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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; 36:e2311472. [PMID: 38421081 DOI: 10.1002/adma.202311472] [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: 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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Xu J, Luo Z, Chen L, Zhou X, Zhang H, Zheng Y, Wei L. Recent advances in flexible memristors for advanced computing and sensing. MATERIALS HORIZONS 2024; 11:4015-4036. [PMID: 38919028 DOI: 10.1039/d4mh00291a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
Conventional computing systems based on von Neumann architecture face challenges such as high power consumption and limited data processing capability. Improving device performance via scaling guided by Moore's Law becomes increasingly difficult. Emerging memristors can provide a promising solution for achieving high-performance computing systems with low power consumption. In particular, the development of flexible memristors is an important topic for wearable electronics, which can lead to intelligent systems in daily life with high computing capacity and efficiency. Here, recent advances in flexible memristors are reviewed, from operating mechanisms and typical materials to representative applications. Potential directions and challenges for future study in this area are also discussed.
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Affiliation(s)
- Jiaming Xu
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore.
| | - Ziwang Luo
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore.
| | - Long Chen
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore.
| | - Xuhui Zhou
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore.
| | - Haozhe Zhang
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore.
| | - Yuanjin Zheng
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore.
| | - Lei Wei
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore.
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Zhao X, Zou H, Wang M, Wang J, Wang T, Wang L, Chen X. Conformal Neuromorphic Bioelectronics for Sense Digitalization. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2403444. [PMID: 38934554 DOI: 10.1002/adma.202403444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 06/03/2024] [Indexed: 06/28/2024]
Abstract
Sense digitalization, the process of transforming sensory experiences into digital data, is an emerging research frontier that links the physical world with human perception and interaction. Inspired by the adaptability, fault tolerance, robustness, and energy efficiency of biological senses, this field drives the development of numerous innovative digitalization techniques. Neuromorphic bioelectronics, characterized by biomimetic adaptability, stand out for their seamless bidirectional interactions with biological entities through stimulus-response and feedback loops, incorporating bio-neuromorphic intelligence for information exchange. This review illustrates recent progress in sensory digitalization, encompassing not only the digital representation of physical sensations such as touch, light, and temperature, correlating to tactile, visual, and thermal perceptions, but also the detection of biochemical stimuli such as gases, ions, and neurotransmitters, mirroring olfactory, gustatory, and neural processes. It thoroughly examines the material design, device manufacturing, and system integration, offering detailed insights. However, the field faces significant challenges, including the development of new device/system paradigms, forging genuine connections with biological systems, ensuring compatibility with the semiconductor industry and overcoming the absence of standardization. Future ambition includes realization of biocompatible neural prosthetics, exoskeletons, soft humanoid robots, and cybernetic devices that integrate smoothly with both biological tissues and artificial components.
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Affiliation(s)
- Xiao Zhao
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory of Smart Biomaterials and Theranostic Technology, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Haochen Zou
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory of Smart Biomaterials and Theranostic Technology, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Ming Wang
- Frontier Institute of Chip and System, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai, 200433, China
| | - Jianwu Wang
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore
- 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
| | - Ting Wang
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory of Smart Biomaterials and Theranostic Technology, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Lianhui Wang
- State Key Laboratory of Organic Electronics and Information Displays, Jiangsu Key Laboratory of Smart Biomaterials and Theranostic Technology, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Xiaodong Chen
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore, 636921, Singapore
- 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
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Jiang H, Dai C, Shen B, Jiang J. High-Performance LiNbO 3 Domain Wall Memory Devices with Enhanced Selectivity via Optimized Metal-Semiconductor Contact. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:1031. [PMID: 38921907 PMCID: PMC11206281 DOI: 10.3390/nano14121031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Revised: 05/24/2024] [Accepted: 05/31/2024] [Indexed: 06/27/2024]
Abstract
Lithium niobate (LiNbO3) single-crystal nanodevices featuring elevated readout domain wall currents exhibit significant potential for integrated circuits in memory computing applications. Nevertheless, challenges stem from suboptimal electrode-LiNbO3 single crystal contact characteristics, which impact the stability of high currents within these devices. In this work, we concentrate on augmenting the domain wall current by refining the fabrication processes of domain wall random access memory (DWRAM). Each LiNbO3 domain wall nanodevice was fabricated using a self-aligned process. Device performance was significantly enhanced by introducing a 10 nm interlayer between the LiNbO3 and Cu electrodes. A comparative analysis of electrical properties was conducted on devices with interlayers made of chromium (Cr) and titanium (Ti), as well as devices without interlayers. After the introduction of the Ti interlayer, the device's coercive voltage demonstrated an 82% reduction, while the current density showed a remarkable 94-fold increase. A 100 nm sized device with the Ti interlayer underwent positive down-negative up pulse testing, demonstrating a writing time of 82 ns at 8 V and an erasing time of 12 μs at -9 V. These operating speeds are significantly faster than those of devices without interlayers. Moreover, the enhanced devices exhibited symmetrical domain switching hysteresis loops with retention times exceeding 106 s. Notably, the coercive voltage (Vc) dispersion remained narrow after more than 1000 switching cycles. At an elevated temperature of 400 K, the device's on/off ratio was maintained at 105. The device's embedded selector demonstrated an ultrahigh selectivity (>106) across various reading voltages. These results underscore the viability of high-density nanoscale integration of ferroelectric domain wall memory.
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Affiliation(s)
| | | | | | - Jun Jiang
- School of Microelectronics, Fudan University, Shanghai 200433, China; (H.J.); (C.D.); (B.S.)
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Li X, Fang Z, Guo X, Wang R, Zhao Y, Zhu W, Wang L, Zhang L. Light-Induced Conductance Potentiation and Depression in an All-Optically Controlled Memristor. ACS APPLIED MATERIALS & INTERFACES 2024; 16:27866-27874. [PMID: 38747412 DOI: 10.1021/acsami.4c02092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]
Abstract
Optoelectronic memristors are new multifunctional devices with both electrically tunable and light-tunable synaptic plasticity, attracting great attention as key promising devices for optoelectronic neuromorphic computing systems. At present, the conductance modulation in most optoelectronic memristors is conducted in a hybrid photoelectric mode, suffering some problems such as heat generation and control complexity. Here, an optoelectronic memristor based on the p+-Si/n-ZnO heterojunction is proposed where the conductance can be reversibly modulated in an all-optically controlled mode. The electron detrapping/trapping mechanism at the p+-Si/n-ZnO interface barrier region is presented to explain the light-induced conductance potentiation/depression behavior. Furthermore, some synaptic functions, including excitatory postsynaptic current (EPSC), inhibitory postsynaptic current (IPSC), and paired-pulse facilitation (PPF), are successfully mimicked in the p+-Si/n-ZnO heterojunction memristor, instructing its application potential for optoelectronic neuromorphic computing.
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Affiliation(s)
- Xinmiao Li
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha 410000, China
| | - Zijing Fang
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha 410000, China
| | - Xing Guo
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha 410000, China
| | - Ruixiao Wang
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha 410000, China
| | - Yinxi Zhao
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha 410000, China
| | - Wenhui Zhu
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha 410000, China
| | - Liancheng Wang
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha 410000, China
| | - Lei Zhang
- State Key Laboratory of High Performance Complex Manufacturing, College of Mechanical and Electrical Engineering, Central South University, Changsha 410000, China
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Meng J, Song J, Fang Y, Wang T, Zhu H, Ji L, Sun QQ, Zhang DW, Chen L. Ionic Diffusive Nanomemristors with Dendritic Competition and Cooperation Functions for Ultralow Voltage Neuromorphic Computing. ACS NANO 2024; 18:9150-9159. [PMID: 38477708 DOI: 10.1021/acsnano.4c00424] [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: 03/14/2024]
Abstract
Realization of dendric signal processing in the human brain is of great significance for spatiotemporal neuromorphic engineering. Here, we proposed an ionic dendrite device with multichannel communication, which could realize synaptic behaviors even under an ultralow action potential of 80 mV. The device not only could simulate one-to-one information transfer of axons but also achieve a many-to-one modulation mode of dendrites. By the adjustment of two presynapses, Pavlov's dog conditioning experiment was learned successfully. Furthermore, the device also could emulate the biological synaptic competition and synaptic cooperation phenomenon through the comodulation of three presynapses, which are crucial for artificial neural network (ANN) implementation. Finally, an ANN was further constructed to realize highly efficient and anti-interference recognition of fashion patterns. By introducing the cooperative device, synaptic weight updates could be improved for higher linearity and larger dynamic regulation range in neuromorphic computing, resulting in higher recognition accuracy and efficiency. Such an artificial dendric device has great application prospects in the processing of more complex information and the construction of an ANN system with more functions.
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Affiliation(s)
- Jialin Meng
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Jieru Song
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Yuqing Fang
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Tianyu Wang
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Hao Zhu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Li Ji
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Qing-Qing Sun
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - David Wei Zhang
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Lin Chen
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
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Chen W, Mou Z, Xin Y, Li H, Wang T, Chen Y, Chen L, Yang BR, Chen Z, Luo Y, Liu GS. Self-Assembled Monolayer and Nanoparticles Coenhanced Fragmented Silver Nanowire Network Memristor. ACS APPLIED MATERIALS & INTERFACES 2024; 16:6057-6067. [PMID: 38285926 DOI: 10.1021/acsami.3c15351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
Silver nanowire (AgNW) networks with self-assembled structures and synaptic connectivity have been recently reported for constructing neuromorphic memristors. However, resistive switching at the cross-point junctions of the network is unstable due to locally enhanced Joule heating and the Gibbs-Thomson effect, which poses an obstacle to the integration of threshold switching and memory function in the same AgNW memristor. Here, fragmented AgNW networks combined with Ag nanoparticles (AgNPs) and mercapto self-assembled monolayers (SAMs) are devised to construct memristors with stable threshold switching and memory behavior. In the above design, the planar gaps between NW segments are for resistive switching, the AgNPs act as metal islands in the gaps to reduce threshold voltage (Vth) and holding voltage (Vhold), and the SAMs suppress surface atom diffusion to avoid Oswald ripening of the AgNPs, which improves switching stability. The fragmented NW-NP/SAM memristors not only circumvent the side effects of conventional NW-stacked junctions to provide durable threshold switching at >Vth but also exhibit synaptic characteristics such as long-term potentiation at ultralow voltage (≪Vth). The combination of NW segments, nanoparticles, and SAMs blazes a new trail for integrating artificial neurons and synapses in AgNW network memristors.
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Affiliation(s)
- Weizhen Chen
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Key Laboratory of Visible Light Communications of Guangzhou, Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Zongxia Mou
- Key Laboratory of Biomaterials of Guangdong Higher Education Institutes, Department of Biomedical Engineering, Jinan University, Guangzhou 510632, China
| | - Yijia Xin
- Department of Physics, Jinan University, Guangzhou 510632, China
| | - Haichuan Li
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Key Laboratory of Visible Light Communications of Guangzhou, Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Tianqi Wang
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Key Laboratory of Visible Light Communications of Guangzhou, Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Yaofei Chen
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Key Laboratory of Visible Light Communications of Guangzhou, Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Lei Chen
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Key Laboratory of Visible Light Communications of Guangzhou, Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Bo-Ru Yang
- State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China
| | - Zhe Chen
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Key Laboratory of Visible Light Communications of Guangzhou, Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Yunhan Luo
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Key Laboratory of Visible Light Communications of Guangzhou, Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
| | - Gui-Shi Liu
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Key Laboratory of Visible Light Communications of Guangzhou, Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
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9
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Xia Y, Zhang C, Xu Z, Lu S, Cheng X, Wei S, Yuan J, Sun Y, Li Y. Organic iontronic memristors for artificial synapses and bionic neuromorphic computing. NANOSCALE 2024; 16:1471-1489. [PMID: 38180037 DOI: 10.1039/d3nr06057h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
To tackle the current crisis of Moore's law, a sophisticated strategy entails the development of multistable memristors, bionic artificial synapses, logic circuits and brain-inspired neuromorphic computing. In comparison with conventional electronic systems, iontronic memristors offer greater potential for the manifestation of artificial intelligence and brain-machine interaction. Organic iontronic memristive materials (OIMs), which possess an organic backbone and exhibit stoichiometric ionic states, have emerged as pivotal contenders for the realization of high-performance bionic iontronic memristors. In this review, a comprehensive analysis of the progress and prospects of OIMs is presented, encompassing their inherent advantages, diverse types, synthesis methodologies, and wide-ranging applications in memristive devices. Predictably, the field of OIMs, as a rapidly developing research subject, presents an exciting opportunity for the development of highly efficient neuro-iontronic systems in areas such as in-sensor computing devices, artificial synapses, and human perception.
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Affiliation(s)
- Yang Xia
- Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application, School of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou, Jiangsu 215009, China.
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou, Jiangsu 215009, China
| | - Cheng Zhang
- Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application, School of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou, Jiangsu 215009, China.
| | - Zheng Xu
- Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application, School of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou, Jiangsu 215009, China.
| | - Shuanglong Lu
- The Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Xinli Cheng
- Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application, School of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou, Jiangsu 215009, China.
| | - Shice Wei
- School of Microelectronics, Fudan University, Shanghai, 200433, China
| | - Junwei Yuan
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou, Jiangsu 215009, China
| | - Yanqiu Sun
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou, Jiangsu 215009, China
| | - Yang Li
- Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application, School of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou, Jiangsu 215009, China.
- The Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, Jiangnan University, Wuxi, Jiangsu 214122, China
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Liu Y, Wang T, Xu K, Li Z, Yu J, Meng J, Zhu H, Sun Q, Zhang DW, Chen L. Low-power and high-speed HfLaO-based FE-TFTs for artificial synapse and reconfigurable logic applications. MATERIALS HORIZONS 2024; 11:490-498. [PMID: 37966103 DOI: 10.1039/d3mh01461d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
Emulating the human nervous system to build next-generation computing architectures is considered a promising way to solve the von Neumann bottleneck. Transistors based on ferroelectric layers are strong contenders for the basic unit of artificial neural systems due to their advantages of high speed and low power consumption. In this work, the potential of Fe-TFTs integrating the HfLaO ferroelectric film and ultra-thin ITO channel for artificial synaptic devices is demonstrated for the first time. The Fe-TFTs can respond significantly to pulses as low as 14 ns with an energy consumption of 93.1 aJ, which is at the leading level for similar devices. In addition, Fe-TFTs exhibit essential synaptic functions and achieve a recognition rate of 93.2% for handwritten digits. Notably, a novel reconfigurable approach involving the combination of two types of electrical pulses to realize Boolean logic operations ("AND", "OR") within a single Fe-TFT has been introduced for the first time. The simulations of array-level operations further demonstrated the potential for parallel computing. These multifunctional Fe-TFTs reveal new hardware options for neuromorphic computing chips.
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Affiliation(s)
- Yongkai Liu
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Tianyu Wang
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Kangli Xu
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Zhenhai Li
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jiajie Yu
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jialin Meng
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Hao Zhu
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Qingqing Sun
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - David Wei Zhang
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Lin Chen
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
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11
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Guo F, Io WF, Dang Z, Ding R, Pang SY, Zhao Y, Hao J. Achieving reinforcement learning in a three-active-terminal neuromorphic device based on a 2D vdW ferroelectric material. MATERIALS HORIZONS 2023; 10:3719-3728. [PMID: 37403831 DOI: 10.1039/d3mh00714f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/06/2023]
Abstract
Currently, for most three-terminal neuromorphic devices, only the gate terminal is active. The inadequate modes and freedom of modulation in such devices greatly hinder the implementation of complex neural behaviors and brain-like thinking strategies in hardware systems. Taking advantage of the unique feature of co-existing in-plane (IP) and out-of-plane (OOP) ferroelectricity in two-dimensional (2D) ferroelectric α-In2Se3, we construct a three-active-terminal neuromorphic device where any terminal can modulate the conductance state. Based on the co-operation mode, controlling food intake as a complex nervous system-level behavior is achieved to carry out positive and negative feedback. Specifically, reinforcement learning as a brain-like thinking strategy is implemented due to the coupling between polarizations in different directions. Compared to the single modulation mode, the chance of the agent successfully obtaining the reward in the Markov decision process is increased from 68% to 82% under the co-operation mode through the coupling effect between IP and OOP ferroelectricity in 2D α-In2Se3 layers. Our work demonstrates the practicability of three-active-terminal neuromorphic devices in handling complex tasks and advances a significant step towards implementing brain-like learning strategies based on neuromorphic devices for dealing with real-world challenges.
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Affiliation(s)
- Feng Guo
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong 999077, P. R. China.
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen 518057, P. R. China
| | - Weng Fu Io
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong 999077, P. R. China.
| | - Zhaoying Dang
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong 999077, P. R. China.
| | - Ran Ding
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong 999077, P. R. China.
| | - Sin-Yi Pang
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong 999077, P. R. China.
| | - Yuqian Zhao
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong 999077, P. R. China.
| | - Jianhua Hao
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong 999077, P. R. China.
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen 518057, P. R. China
- Photonics Research Institute and Research Institute for Smart Energy, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, P. R. China
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12
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Zhou K, Jia Z, Zhou Y, Ding G, Ma XQ, Niu W, Han ST, Zhao J, Zhou Y. Covalent Organic Frameworks for Neuromorphic Devices. J Phys Chem Lett 2023; 14:7173-7192. [PMID: 37540588 DOI: 10.1021/acs.jpclett.3c01711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2023]
Abstract
Neuromorphic computing could enable the potential to break the inherent limitations of conventional von Neumann architectures, which has led to widespread research interest in developing novel neuromorphic memory devices, such as memristors and bioinspired artificial synaptic devices. Covalent organic frameworks (COFs), as crystalline porous polymers, have tailorable skeletons and pores, providing unique platforms for the interplay with photons, excitons, electrons, holes, ions, spins, and molecules. Such features encourage the rising research interest in COF materials in neuromorphic electronics. To develop high-performance COF-based neuromorphic memory devices, it is necessary to comprehensively understand materials, devices, and applications. Therefore, this Perspective focuses on discussing the use of COF materials for neuromorphic memory devices in terms of molecular design, thin-film processing, and neuromorphic applications. Finally, we provide an outlook for future directions and potential applications of COF-based neuromorphic electronics.
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Affiliation(s)
- Kui Zhou
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Ziqi Jia
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Yao Zhou
- College of Materials Science and Engineering, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Guanglong Ding
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Xin-Qi Ma
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Wenbiao Niu
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Su-Ting Han
- College of Electronics and Information Engineering, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
| | - Jiyu Zhao
- State Key Laboratory of Fine Chemicals, Dalian University of Technology, 2 Linggong Road, Dalian 116024, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, 3688 Nanhai Avenue, Shenzhen 518060, P. R. China
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13
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Cao Z, Sun B, Zhou G, Mao S, Zhu S, Zhang J, Ke C, Zhao Y, Shao J. Memristor-based neural networks: a bridge from device to artificial intelligence. NANOSCALE HORIZONS 2023; 8:716-745. [PMID: 36946082 DOI: 10.1039/d2nh00536k] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.
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Affiliation(s)
- Zelin Cao
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
- Shaanxi International Joint Research Center for Applied Technology of Controllable Neutron Source, School of Science, Xijing University, Xi'an 710123, China
| | - Bai Sun
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
| | - Guangdong Zhou
- College of Artificial Intelligence, Brain-inspired Computing & Intelligent Control of Chongqing Key Lab, Southwest University, Chongqing 400715, China
| | - Shuangsuo Mao
- Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fujian Normal University, Fuzhou, Fujian 350117, China
| | - Shouhui Zhu
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Jie Zhang
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Chuan Ke
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Yong Zhao
- Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fujian Normal University, Fuzhou, Fujian 350117, China
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Jinyou Shao
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
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14
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Patil AR, Dongale TD, Namade LD, Mohite SV, Kim Y, Sutar SS, Kamat RK, Rajpure KY. Sprayed FeWO4 thin film-based memristive device with negative differential resistance effect for non-volatile memory and synaptic learning applications. J Colloid Interface Sci 2023; 642:540-553. [PMID: 37028161 DOI: 10.1016/j.jcis.2023.03.189] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/21/2023] [Accepted: 03/29/2023] [Indexed: 04/04/2023]
Abstract
Resistive switching (RS) memories have attracted great attention as promising solutions to next-generation non-volatile memories and computing technologies because of their simple device configuration, high on/off ratio, low power consumption, fast switching, long retention, and significant cyclic stability. In this work, uniform and adherent iron tungstate (FeWO4) thin films were synthesized by the spray pyrolysis method with various precursor solution volumes, and these were tested as a switching layer for the fabrication of Ag/FWO/FTO memristive devices. The detailed structural investigation was done through various analytical and physio-chemical characterizations viz. X-ray diffraction (XRD) and its Rietveld refinement, Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS) techniques. The results reveal the pure and single-phase FeWO4 compound thin film formation. Surface morphological study shows the spherical particle formation having a diameter in the range of 20 to 40 nm. The RS characteristics of the Ag/FWO/FTO memristive device demonstrate non-volatile memory characteristics with significant endurance and retention properties. Interestingly, the memory devices show stable and reproducible negative differential resistance (NDR) effects. The in-depth statistical analysis suggests the good operational uniformity of the device. Moreover, the switching voltages of the Ag/FWO/FTO memristive device were modeled using the time series analysis technique by utilizing Holt's Winter Exponential Smoothing (HWES) approach. Additionally, the device mimics bio-synaptic properties such as potentiation/depression, excitatory post-synaptic current (EPSC), and spike-timing-dependent plasticity (STDP) learning rules. For the present device, the space-charge-limited current (SCLC) and trap-controlled-SCLC effects dominated during positive and negative bias I-V characteristics, respectively. The RS mechanism dominated in the low resistance state (LRS), and the high resistance state (HRS) was explained based on the formation and rupture of conductive filament composed of Ag ions and oxygen vacancies. This work demonstrates the RS in the metal tungstate-based memristive devices and demonstrates a low-cost approach for fabricating memristive devices.
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Affiliation(s)
- Amitkumar R Patil
- Electrochemical Materials Laboratory, Department of Physics, Shivaji University, Kolhapur 416004, India
| | - Tukaram D Dongale
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur 416004, India
| | - Lahu D Namade
- Electrochemical Materials Laboratory, Department of Physics, Shivaji University, Kolhapur 416004, India
| | - Santosh V Mohite
- Department of Applied Chemistry, Konkuk University, Chungju 27478, Republic of Korea
| | - Yeonho Kim
- Department of Applied Chemistry, Konkuk University, Chungju 27478, Republic of Korea
| | - Santosh S Sutar
- Yashwantrao Chavan School of Rural Development, Shivaji University, Kolhapur 416004, India
| | - Rajanish K Kamat
- Department of Electronics, Shivaji University, Kolhapur 416004, India; Dr. Homi Bhabha State University, 15, Madam Cama Road, Mumbai 400032, India
| | - Keshav Y Rajpure
- Electrochemical Materials Laboratory, Department of Physics, Shivaji University, Kolhapur 416004, India.
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15
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Hwang Y, Park B, Hwang S, Choi SW, Kim HS, Kim AR, Choi JW, Yoon J, Kwon JD, Kim Y. A Bioinspired Ultra Flexible Artificial van der Waals 2D-MoS 2 Channel/LiSiO x Solid Electrolyte Synapse Arrays via Laser-Lift Off Process for Wearable Adaptive Neuromorphic Computing. SMALL METHODS 2023:e2201719. [PMID: 36960927 DOI: 10.1002/smtd.202201719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 02/06/2023] [Indexed: 06/18/2023]
Abstract
Wearable electronic devices with next-generation biocompatible, mechanical, ultraflexible, and portable sensors are a fast-growing technology. Hardware systems enabling artificial neural networks while consuming low power and processing massive in situ personal data are essential for adaptive wearable neuromorphic edging computing. Herein, the development of an ultraflexible artificial-synaptic array device with concrete-mechanical cyclic endurance consisting of a novel heterostructure with an all-solid-state 2D MoS2 channel and LiSiOx (lithium silicate) is demonstrated. Enabled by the sequential fabrication process of all layers, by excluding the transfer process, artificial van der Waals devices combined with the 2D-MoS2 channel and LiSiOx solid electrolyte exhibit excellent neuromorphic synaptic characteristics with a nonlinearity of 0.55 and asymmetry ratio of 0.22. Based on the excellent flexibility of colorless polyimide substrates and thin-layered structures, the fabricated flexible neuromorphic synaptic devices exhibit superior long-term potentiation and long-term depression cyclic endurance performance, even when bent over 700 times or on curved surfaces with a diameter of 10 mm. Thus, a high classification accuracy of 95% is achieved without any noticeable performance degradation in the Modified National Institute of Standards and Technology. These results are promising for the development of personalized wearable artificial neural systems in the future.
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Affiliation(s)
- Yunjeong Hwang
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
| | - Byeongjin Park
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan, 46241, Republic of Korea
| | - Seungkwon Hwang
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan, 46241, Republic of Korea
| | - Soo-Won Choi
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan, 46241, Republic of Korea
| | - Han Seul Kim
- Department of Advanced Materials Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, 28644, Republic of Korea
| | - Ah Ra Kim
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
| | - Jin Woo Choi
- Department of Data Information and Physics, Kongju National University, 56 Gongjudaehak-ro, Gongju, Chungcheongnam-do, 32588, Republic ofKorea
| | - Jongwon Yoon
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
| | - Jung-Dae Kwon
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
| | - Yonghun Kim
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
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16
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Zahoor F, Hussin FA, Isyaku UB, Gupta S, Khanday FA, Chattopadhyay A, Abbas H. Resistive random access memory: introduction to device mechanism, materials and application to neuromorphic computing. DISCOVER NANO 2023; 18:36. [PMID: 37382679 PMCID: PMC10409712 DOI: 10.1186/s11671-023-03775-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 01/17/2023] [Indexed: 06/30/2023]
Abstract
The modern-day computing technologies are continuously undergoing a rapid changing landscape; thus, the demands of new memory types are growing that will be fast, energy efficient and durable. The limited scaling capabilities of the conventional memory technologies are pushing the limits of data-intense applications beyond the scope of silicon-based complementary metal oxide semiconductors (CMOS). Resistive random access memory (RRAM) is one of the most suitable emerging memory technologies candidates that have demonstrated potential to replace state-of-the-art integrated electronic devices for advanced computing and digital and analog circuit applications including neuromorphic networks. RRAM has grown in prominence in the recent years due to its simple structure, long retention, high operating speed, ultra-low-power operation capabilities, ability to scale to lower dimensions without affecting the device performance and the possibility of three-dimensional integration for high-density applications. Over the past few years, research has shown RRAM as one of the most suitable candidates for designing efficient, intelligent and secure computing system in the post-CMOS era. In this manuscript, the journey and the device engineering of RRAM with a special focus on the resistive switching mechanism are detailed. This review also focuses on the RRAM based on two-dimensional (2D) materials, as 2D materials offer unique electrical, chemical, mechanical and physical properties owing to their ultrathin, flexible and multilayer structure. Finally, the applications of RRAM in the field of neuromorphic computing are presented.
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Affiliation(s)
- Furqan Zahoor
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Fawnizu Azmadi Hussin
- Department of Electrical and Electronics Engineering, Universiti Teknologi Petronas, Seri Iskandar, Malaysia
| | - Usman Bature Isyaku
- Department of Electrical and Electronics Engineering, Universiti Teknologi Petronas, Seri Iskandar, Malaysia
| | - Shagun Gupta
- School of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, Katra, India
| | - Farooq Ahmad Khanday
- Department of Electronics & Instrumentation Technology, University of Kashmir, Srinagar, India
| | - Anupam Chattopadhyay
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Haider Abbas
- Division of Material Science and Engineering, Hanyang University, Seoul, South Korea
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
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Lu Q, Zhao Y, Huang L, An J, Zheng Y, Yap EH. Low-Dimensional-Materials-Based Flexible Artificial Synapse: Materials, Devices, and Systems. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:373. [PMID: 36770333 PMCID: PMC9921566 DOI: 10.3390/nano13030373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/10/2023] [Accepted: 01/15/2023] [Indexed: 06/18/2023]
Abstract
With the rapid development of artificial intelligence and the Internet of Things, there is an explosion of available data for processing and analysis in any domain. However, signal processing efficiency is limited by the Von Neumann structure for the conventional computing system. Therefore, the design and construction of artificial synapse, which is the basic unit for the hardware-based neural network, by mimicking the structure and working mechanisms of biological synapses, have attracted a great amount of attention to overcome this limitation. In addition, a revolution in healthcare monitoring, neuro-prosthetics, and human-machine interfaces can be further realized with a flexible device integrating sensing, memory, and processing functions by emulating the bionic sensory and perceptual functions of neural systems. Until now, flexible artificial synapses and related neuromorphic systems, which are capable of responding to external environmental stimuli and processing signals efficiently, have been extensively studied from material-selection, structure-design, and system-integration perspectives. Moreover, low-dimensional materials, which show distinct electrical properties and excellent mechanical properties, have been extensively employed in the fabrication of flexible electronics. In this review, recent progress in flexible artificial synapses and neuromorphic systems based on low-dimensional materials is discussed. The potential and the challenges of the devices and systems in the application of neuromorphic computing and sensory systems are also explored.
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Affiliation(s)
- Qifeng Lu
- School of CHIPS, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, 111 Taicang Avenue, Taicang, Suzhou 215488, China
| | - Yinchao Zhao
- School of CHIPS, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, 111 Taicang Avenue, Taicang, Suzhou 215488, China
| | - Long Huang
- School of Intelligent Manufacturing Ecosystem, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, 111 Taicang Avenue, Taicang, Suzhou 215488, China
| | - Jiabao An
- School of Intelligent Manufacturing Ecosystem, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, 111 Taicang Avenue, Taicang, Suzhou 215488, China
| | - Yufan Zheng
- School of Intelligent Manufacturing Ecosystem, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, 111 Taicang Avenue, Taicang, Suzhou 215488, China
| | - Eng Hwa Yap
- School of Robotics, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, 111 Taicang Avenue, Taicang, Suzhou 215488, China
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18
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Wang T, Meng J, Zhou X, Liu Y, He Z, Han Q, Li Q, Yu J, Li Z, Liu Y, Zhu H, Sun Q, Zhang DW, Chen P, Peng H, Chen L. Reconfigurable neuromorphic memristor network for ultralow-power smart textile electronics. Nat Commun 2022; 13:7432. [PMID: 36460675 PMCID: PMC9718838 DOI: 10.1038/s41467-022-35160-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 11/21/2022] [Indexed: 12/04/2022] Open
Abstract
Neuromorphic computing memristors are attractive to construct low-power- consumption electronic textiles due to the intrinsic interwoven architecture and promising applications in wearable electronics. Developing reconfigurable fiber-based memristors is an efficient method to realize electronic textiles that capable of neuromorphic computing function. However, the previously reported artificial synapse and neuron need different materials and configurations, making it difficult to realize multiple functions in a single device. Herein, a textile memristor network of Ag/MoS2/HfAlOx/carbon nanotube with reconfigurable characteristics was reported, which can achieve both nonvolatile synaptic plasticity and volatile neuron functions. In addition, a single reconfigurable memristor can realize integrate-and-fire function, exhibiting significant advantages in reducing the complexity of neuron circuits. The firing energy consumption of fiber-based memristive neuron is 1.9 fJ/spike (femtojoule-level), which is at least three orders of magnitude lower than that of the reported biological and artificial neuron (picojoule-level). The ultralow energy consumption makes it possible to create an electronic neural network that reduces the energy consumption compared to human brain. By integrating the reconfigurable synapse, neuron and heating resistor, a smart textile system is successfully constructed for warm fabric application, providing a unique functional reconfiguration pathway toward the next-generation in-memory computing textile system.
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Affiliation(s)
- Tianyu Wang
- School of Microelectronics, Fudan University, 200433, Shanghai, China
- Zhangjiang Fudan International Innovation Center, 201203, Shanghai, China
| | - Jialin Meng
- School of Microelectronics, Fudan University, 200433, Shanghai, China
- Zhangjiang Fudan International Innovation Center, 201203, Shanghai, China
| | - Xufeng Zhou
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, 200438, Shanghai, China
| | - Yue Liu
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, 200438, Shanghai, China
| | - Zhenyu He
- School of Microelectronics, Fudan University, 200433, Shanghai, China
- Zhangjiang Fudan International Innovation Center, 201203, Shanghai, China
| | - Qi Han
- School of Microelectronics, Fudan University, 200433, Shanghai, China
- Zhangjiang Fudan International Innovation Center, 201203, Shanghai, China
| | - Qingxuan Li
- School of Microelectronics, Fudan University, 200433, Shanghai, China
- Zhangjiang Fudan International Innovation Center, 201203, Shanghai, China
| | - Jiajie Yu
- School of Microelectronics, Fudan University, 200433, Shanghai, China
- Zhangjiang Fudan International Innovation Center, 201203, Shanghai, China
| | - Zhenhai Li
- School of Microelectronics, Fudan University, 200433, Shanghai, China
- Zhangjiang Fudan International Innovation Center, 201203, Shanghai, China
| | - Yongkai Liu
- School of Microelectronics, Fudan University, 200433, Shanghai, China
- Zhangjiang Fudan International Innovation Center, 201203, Shanghai, China
| | - Hao Zhu
- School of Microelectronics, Fudan University, 200433, Shanghai, China
- Zhangjiang Fudan International Innovation Center, 201203, Shanghai, China
| | - Qingqing Sun
- School of Microelectronics, Fudan University, 200433, Shanghai, China
- Zhangjiang Fudan International Innovation Center, 201203, Shanghai, China
| | - David Wei Zhang
- School of Microelectronics, Fudan University, 200433, Shanghai, China
- Zhangjiang Fudan International Innovation Center, 201203, Shanghai, China
| | - Peining Chen
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, 200438, Shanghai, China.
| | - Huisheng Peng
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, 200438, Shanghai, China
| | - Lin Chen
- School of Microelectronics, Fudan University, 200433, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, 201203, Shanghai, China.
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19
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Wu F, Chou CH, Tseng TY. CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing. NANOSCALE RESEARCH LETTERS 2022; 17:105. [PMID: 36342556 PMCID: PMC9640510 DOI: 10.1186/s11671-022-03744-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
Optoelectronic memristor is a promising candidate for future light-controllable high-density storage and neuromorphic computing. In this work, light-tunable resistive switching (RS) characteristics are demonstrated in the CMOS process-compatible ITO/HfO2/TiO2/ITO optoelectronic memristor. The device shows an average of 79.24% transmittance under visible light. After electroforming, stable bipolar analog switching, data retention beyond 104 s, and endurance of 106 cycles are realized. An obvious current increase is observed under 405 nm wavelength light irradiation both in high and in low resistance states. The long-term potentiation of synaptic property can be achieved by both electrical and optical stimulation. Moreover, based on the optical potentiation and electrical depression of conductances, the simulated Hopfield neural network (HNN) is trained for learning the 10 × 10 pixels size image. The HNN can be successfully trained to recognize the input image with a training accuracy of 100% in 13 iterations. These results suggest that this optoelectronic memristor has a high potential for neuromorphic application.
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Affiliation(s)
- Facai Wu
- Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan
| | - Chien-Hung Chou
- Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan
| | - Tseung-Yuen Tseng
- Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan.
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20
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Zhang X, Chen H, Cheng S, Guo F, Jie W, Hao J. Tunable Resistive Switching in 2D MXene Ti 3C 2 Nanosheets for Non-Volatile Memory and Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2022; 14:44614-44621. [PMID: 36136123 DOI: 10.1021/acsami.2c14006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
An artificial synapse is essential for neuromorphic computing which has been expected to overcome the bottleneck of the traditional von-Neumann system. Memristors can work as an artificial synapse owing to their tunable non-volatile resistance states which offer the capabilities of information storage, processing, and computing. In this work, memristors based on two-dimensional (2D) MXene Ti3C2 nanosheets sandwiched by Pt electrodes are investigated in terms of resistive switching (RS) characteristics, synaptic functions, and neuromorphic computing. Digital and analog RS behaviors are found to coexist depending on the magnitude of operation voltage. Digital RS behaviors with two resistance states possessing a large switching ratio exceeding 103 can be achieved under a high operation voltage. Analog RS behaviors with a series of resistance states exhibiting a gradual change can be observed at a relatively low operation voltage. Furthermore, artificial synapses can be implemented based on the memristors with the basic synaptic functions, such as long-term plasticity of long-term potentiation and depression and short-term plasticity of the paired-pulse facilitation and depression. Moreover, the "learning-forgetting" experience is successfully emulated based on the artificial synapses. Also, more importantly, the artificial synapses can construct an artificial neural network to implement image recognition. The coexistence of digital and analog RS behaviors in the 2D Ti3C2 nanosheets suggests the potential applications in non-volatile memory and neuromorphic computing, which is expected to facilitate simplifying the manufacturing complexity for complex neutral systems where analog and digital switching is essential for information storage and processing.
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Affiliation(s)
- Xuelian Zhang
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu 610066, China
| | - Haohan Chen
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu 610066, China
| | - Siqi Cheng
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu 610066, China
| | - Feng Guo
- Department of Applied Physics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China
| | - Wenjing Jie
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu 610066, China
| | - Jianhua Hao
- Department of Applied Physics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China
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21
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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: 9] [Impact Index Per Article: 4.5] [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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22
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Zhang X, Wu C, Lv Y, Zhang Y, Liu W. High-Performance Flexible Polymer Memristor Based on Stable Filamentary Switching. NANO LETTERS 2022; 22:7246-7253. [PMID: 35984717 DOI: 10.1021/acs.nanolett.2c02765] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Polymer-based atomic switch memristors via the formation/dissolution of atomic-scale conductive filaments are considered as the leading candidate for next-generation nonvolatile memory. However, the instability of conductive filaments of incomplete bridge makes their switching performances unsatisfied. In this work, we report a flexible polymeric memristor using polyethylenimine incorporated with silver salt. The memristor device exhibited superior performances at room temperature with a favorable endurance, high ON/OFF ratio, good retention, and low operating voltage. These satisfactory performances are attributed to the pre-existing Ag ions in the polymer, guiding the formation of a robust Ag filament. In addition, the device shows stable bipolar switching behavior in bending conditions or after hundreds of bending cycles. In our work, we provide a simple and efficient method to construct robust filament-based memristors for flexible electronics.
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Affiliation(s)
- Xinshui Zhang
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Cong Wu
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Yinjie Lv
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Yue Zhang
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Wei Liu
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
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23
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Shtansky DV, Matveev AT, Permyakova ES, Leybo DV, Konopatsky AS, Sorokin PB. Recent Progress in Fabrication and Application of BN Nanostructures and BN-Based Nanohybrids. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:2810. [PMID: 36014675 PMCID: PMC9416166 DOI: 10.3390/nano12162810] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/11/2022] [Accepted: 08/12/2022] [Indexed: 05/27/2023]
Abstract
Due to its unique physical, chemical, and mechanical properties, such as a low specific density, large specific surface area, excellent thermal stability, oxidation resistance, low friction, good dispersion stability, enhanced adsorbing capacity, large interlayer shear force, and wide bandgap, hexagonal boron nitride (h-BN) nanostructures are of great interest in many fields. These include, but are not limited to, (i) heterogeneous catalysts, (ii) promising nanocarriers for targeted drug delivery to tumor cells and nanoparticles containing therapeutic agents to fight bacterial and fungal infections, (iii) reinforcing phases in metal, ceramics, and polymer matrix composites, (iv) additives to liquid lubricants, (v) substrates for surface enhanced Raman spectroscopy, (vi) agents for boron neutron capture therapy, (vii) water purifiers, (viii) gas and biological sensors, and (ix) quantum dots, single photon emitters, and heterostructures for electronic, plasmonic, optical, optoelectronic, semiconductor, and magnetic devices. All of these areas are developing rapidly. Thus, the goal of this review is to analyze the critical mass of knowledge and the current state-of-the-art in the field of BN-based nanomaterial fabrication and application based on their amazing properties.
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Affiliation(s)
- Dmitry V. Shtansky
- Labotoary of Inorganic Nanomaterials, National University of Science and Technology “MISiS”, Leninsky Prospect 4, 119049 Moscow, Russia
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24
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Mao S, Sun B, Zhou G, Guo T, Wang J, Zhao Y. Applications of biomemristors in next generation wearable electronics. NANOSCALE HORIZONS 2022; 7:822-848. [PMID: 35697026 DOI: 10.1039/d2nh00163b] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
With the rapid development of mobile internet and artificial intelligence, wearable electronic devices have a great market prospect. In particular, information storage and processing of real-time collected data are an indispensable part of wearable electronic devices. Biomaterial-based memristive systems are suitable for storage and processing of the obtained information in wearable electronics due to the accompanying merits, i.e. sustainability, lightweight, degradability, low power consumption, flexibility and biocompatibility. So far, many biomaterial-based flexible and wearable memristive devices were prepared by spin coating or other technologies on a flexible substrate at room temperature. However, mechanical deformation caused by mechanical mismatch between devices and soft tissues leads to the instability of device performance. From the current research and practical application, the device will face great challenges when adapting to different working environments. In fact, some interesting studies have been performed to address the above issues while they were not intensively highlighted and overviewed. Herein, the progress in wearable biomemristive devices is reviewed, and the outlook and perspectives are provided in consideration of the existing challenges during the development of wearable biomemristive systems.
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Affiliation(s)
- Shuangsuo Mao
- Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fujian Normal University, Fuzhou, Fujian 350117, China.
- College of Physics and Energy, Fujian Normal University, Fuzhou, Fujian 351007, China
| | - Bai Sun
- Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fujian Normal University, Fuzhou, Fujian 350117, China.
- College of Physics and Energy, Fujian Normal University, Fuzhou, Fujian 351007, China
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
- Superconductivity and New Energy R&D Center, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Guangdong Zhou
- Scholl of Artificial Intelligence, Southwest University, Chongqing, 400715, China
| | - Tao Guo
- Department of Mechanical and Mechatronics Engineering, Waterloo Institute for Nanotechnology, Centre for Advanced Materials Joining, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
| | - Jiangqiu Wang
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
- Superconductivity and New Energy R&D Center, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Yong Zhao
- Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fujian Normal University, Fuzhou, Fujian 350117, China.
- College of Physics and Energy, Fujian Normal University, Fuzhou, Fujian 351007, China
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
- Superconductivity and New Energy R&D Center, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
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25
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Yan Q, Fan F, Zhang B, Liu G, Chen Y. MoS2 nanosheets functionalized with ferrocene-containing polymer via SI-ATRP for memristive devices with multilevel resistive switching. Eur Polym J 2022. [DOI: 10.1016/j.eurpolymj.2022.111316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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26
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Perovskite-Based Memristor with 50-Fold Switchable Photosensitivity for In-Sensor Computing Neural Network. NANOMATERIALS 2022; 12:nano12132217. [PMID: 35808058 PMCID: PMC9268359 DOI: 10.3390/nano12132217] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 06/23/2022] [Accepted: 06/27/2022] [Indexed: 02/06/2023]
Abstract
In-sensor computing can simultaneously output image information and recognition results through in-situ visual signal processing, which can greatly improve the efficiency of machine vision. However, in-sensor computing is challenging due to the requirement to controllably adjust the sensor’s photosensitivity. Herein, it is demonstrated a ternary cationic halide Cs0.05FA0.81MA0.14 Pb(I0.85Br0.15)3 (CsFAMA) perovskite, whose External quantum efficiency (EQE) value is above 80% in the entire visible region (400–750 nm), and peak responsibility value at 750 nm reaches 0.45 A/W. In addition, the device can achieve a 50-fold enhancement of the photoresponsibility under the same illumination by adjusting the internal ion migration and readout voltage. A proof-of-concept visually enhanced neural network system is demonstrated through the switchable photosensitivity of the perovskite sensor array, which can simultaneously optimize imaging and recognition results and improve object recognition accuracy by 17% in low-light environments.
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27
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Liu X, Cao J, Qiu J, Zhang X, Wang M, Liu Q. Flexible and Stretchable Memristive Arrays for in-Memory Computing. FRONTIERS IN NANOTECHNOLOGY 2022. [DOI: 10.3389/fnano.2021.821687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
With the tremendous progress of Internet of Things (IoT) and artificial intelligence (AI) technologies, the demand for flexible and stretchable electronic systems is rapidly increasing. As the vital component of a system, existing computing units are usually rigid and brittle, which are incompatible with flexible and stretchable electronics. Emerging memristive devices with flexibility and stretchability as well as direct processing-in-memory ability are promising candidates to perform data computing in flexible and stretchable electronics. To execute the in-memory computing paradigm including digital and analogue computing, the array configuration of memristive devices is usually required. Herein, the recent progress on flexible and stretchable memristive arrays for in-memory computing is reviewed. The common materials used for flexible memristive arrays, including inorganic, organic and two-dimensional (2D) materials, will be highlighted, and effective strategies used for stretchable memristive arrays, including material innovation and structural design, will be discussed in detail. The current challenges and future perspectives of the in-memory computing utilizing flexible and stretchable memristive arrays are presented. These efforts aim to accelerate the development of flexible and stretchable memristive arrays for data computing in advanced intelligent systems, such as electronic skin, soft robotics, and wearable devices.
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28
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Wang Y, Huang Z, Chen X, Lu M. Building resistive switching memory having super-steep switching slope with in-plane boron nitride. NANOTECHNOLOGY 2021; 33:125202. [PMID: 34902845 DOI: 10.1088/1361-6528/ac4289] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 12/13/2021] [Indexed: 06/14/2023]
Abstract
The two-dimensional hexagonal boron nitride (h-BN) has been used as resistive switching (RS) material for memory due to its insulation, good thermal conductivity and excellent thermal/chemical stability. A typical h-BN based RS memory employs a metal-insulator-metal vertical structure, in which metal ions pass through the h-BN layers to realize the transition from high resistance state to low resistance state. Alternatively, just like the horizontal structure widely used in the traditional MOS capacitor based memory, the performance of in-plane h-BN memory should also be evaluated to determine its potential applications. As consequence, a horizontal structured resistive memory has been designed in this work by forming freestanding h-BN across Ag nanogap, where the two-dimensional h-BN favored in-plane transport of metal ions to emphasize the RS behavior. As a result, the memory devices showed switching slope down to 0.25 mV dec-1, ON/OFF ratio up to 108, SET current down to pA and SET voltage down to 180 mV.
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Affiliation(s)
- Yisen Wang
- Pen-Tung Sah Research Institute of Micro-Nano Science & Technology, Xiamen University, Xiamen 361005, People's Republic of China
| | - Zhifang Huang
- Pen-Tung Sah Research Institute of Micro-Nano Science & Technology, Xiamen University, Xiamen 361005, People's Republic of China
| | - Xinyi Chen
- Pen-Tung Sah Research Institute of Micro-Nano Science & Technology, Xiamen University, Xiamen 361005, People's Republic of China
| | - Miao Lu
- Pen-Tung Sah Research Institute of Micro-Nano Science & Technology, Xiamen University, Xiamen 361005, People's Republic of China
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29
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He ZY, Wang TY, Meng JL, Zhu H, Ji L, Sun QQ, Chen L, Zhang DW. CMOS back-end compatible memristors for in situ digital and neuromorphic computing applications. MATERIALS HORIZONS 2021; 8:3345-3355. [PMID: 34635907 DOI: 10.1039/d1mh01257f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In-memory logic calculations and brain-inspired artificial synaptic neuromorphic computing are expected to solve the limitations of the traditional von Neumann computing architecture. The data processing efficiency of the traditional von Neumann architecture is inherently limited by its physically separated processing and storage units, and thus data transmission besides calculation leads to a limited calculation speed and additional high-power consumption. In addition, traditional digital logic calculations and analog calculations have greater limitations in conversion. Herein, we report a flexible two-terminal memristor based on SiCO:H, which is a porous low-k back-end complementary metal-oxide-semiconductor (CMOS)-compatible material. Due to its low operating voltage (200 mV) and fast response speed (100 ns), it could perform digital memory calculation and neuromorphic calculation simultaneously. The memristor could realize a transition from short-term to long-term plasticity in the process of enhancement and inhibition during neuromorphic calculation, with high biological reality. In digital logic calculations, IMP-based and MAGIC-based logic calculations were verified. In neuromorphic computing, an Ag ion-based conductive filament was introduced. The relationship between the temporal dynamics of the conductance evolution and the diffusive dynamics of the Ag active metal could be modulated by the external programming electric field strength. The synapses and neuron dynamics in biology were faithfully simulated, realizing a transition from short-term to long-term plasticity in the process of enhancement and inhibition, which has high compatibility and scalability, proposing a novel solution for the next generation of computer architectures.
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Affiliation(s)
- Zhen-Yu He
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
| | - Tian-Yu Wang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
| | - Jia-Lin Meng
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
| | - Hao Zhu
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
- National Integrated Circuit Innovation Center, No. 825 Zhangheng Road, Shanghai 201203, China
| | - Li Ji
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
- National Integrated Circuit Innovation Center, No. 825 Zhangheng Road, Shanghai 201203, China
| | - Qing-Qing Sun
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
- National Integrated Circuit Innovation Center, No. 825 Zhangheng Road, Shanghai 201203, China
| | - Lin Chen
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
- National Integrated Circuit Innovation Center, No. 825 Zhangheng Road, Shanghai 201203, China
| | - David Wei Zhang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
- National Integrated Circuit Innovation Center, No. 825 Zhangheng Road, Shanghai 201203, China
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30
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Xiong H, Ling S, Li Y, Duan F, Zhu H, Lu S, Du M. Flexible and recyclable bio-based transient resistive memory enabled by self-healing polyimine membrane. J Colloid Interface Sci 2021; 608:1126-1134. [PMID: 34735849 DOI: 10.1016/j.jcis.2021.10.126] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/12/2021] [Accepted: 10/21/2021] [Indexed: 01/12/2023]
Abstract
The recyclable, self-healing and easily-degradable transient electronic technology has aroused tremendous attention in flexible electronic products. However, integrating the above advantages into one single flexible electronic device is still a huge challenge. Herein, we demonstrate a flexible and recyclable bio-based memory device using fish colloid as the resistive switching layer on a polyimine substrate, which affords reliable mechanical and electrical properties under repetitive conformal deformation operation. This flexible bio-based memory device presents potential analog behaviors including memory characteristics and excitatory current response, which undergoes incremental potentiation in conductance under successive electrical pulses. Moreover, this device is expected to greatly alleviate the environmental problems caused by electronic waste. It can be decomposed rapidly in water and well recycled, which is a promising candidate for transient memories and information security. We believe that this study can provide new possibilities to the field of high-performance transient electronics and flexible resistive memory devices.
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Affiliation(s)
- Hanli Xiong
- Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, School of Chemical and Material Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Songtao Ling
- Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application, School of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou, Jiangsu 215009, China
| | - Yang Li
- Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application, School of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou, Jiangsu 215009, China.
| | - Fang Duan
- Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, School of Chemical and Material Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Han Zhu
- Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, School of Chemical and Material Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Shuanglong Lu
- Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, School of Chemical and Material Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China.
| | - Mingliang Du
- Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, School of Chemical and Material Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China.
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31
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Abnavi A, Ahmadi R, Hasani A, Fawzy M, Mohammadzadeh MR, De Silva T, Yu N, Adachi MM. Free-Standing Multilayer Molybdenum Disulfide Memristor for Brain-Inspired Neuromorphic Applications. ACS APPLIED MATERIALS & INTERFACES 2021; 13:45843-45853. [PMID: 34542262 DOI: 10.1021/acsami.1c11359] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Recently, atomically thin two-dimensional (2D) transition-metal dichalcogenides (TMDs) have attracted great interest in electronic and opto-electronic devices for high-integration-density applications such as data storage due to their small vertical dimension and high data storage capability. Here, we report a memristor based on free-standing multilayer molybdenum disulfide (MoS2) with a high current on/off ratio of ∼103 and a stable retention for at least 3000 s. Through light modulation of the carrier density in the suspended MoS2 channel, the on/off ratio can be further increased to ∼105. Moreover, the essential photosynaptic functions with short- and long-term memory (STM and LTM) behaviors are successfully mimicked by such devices. These results also indicate that STM can be transferred to LTM by increasing the light stimuli power, pulse duration, and number of pulses. The electrical measurements performed under vacuum and ambient air conditions propose that the observed resistive switching is due to adsorbed oxygen and water molecules on both sides of the MoS2 channel. Thus, our free-standing 2D multilayer MoS2-based memristors propose a simple approach for fabrication of a low-power-consumption and reliable resistive switching device for neuromorphic applications.
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Affiliation(s)
- Amin Abnavi
- School of Engineering Science, Simon Fraser University, Burnaby V5A 1S6, British Columbia, Canada
| | - Ribwar Ahmadi
- School of Engineering Science, Simon Fraser University, Burnaby V5A 1S6, British Columbia, Canada
| | - Amirhossein Hasani
- School of Engineering Science, Simon Fraser University, Burnaby V5A 1S6, British Columbia, Canada
| | - Mirette Fawzy
- Department of Physics, Simon Fraser University, Burnaby V5A 1S6, British Columbia, Canada
| | | | - Thushani De Silva
- School of Engineering Science, Simon Fraser University, Burnaby V5A 1S6, British Columbia, Canada
| | - Niannian Yu
- School of Science, Wuhan University of Technology, Wuhan 430070, China
| | - Michael M Adachi
- School of Engineering Science, Simon Fraser University, Burnaby V5A 1S6, British Columbia, Canada
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