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Sun M, Xu Z, Qu S, Liu L, Zhu Q, Xu W. Synaptic Transistors Using Scalable Graphene Nanoribbons. J Phys Chem Lett 2024; 15:8956-8963. [PMID: 39185714 DOI: 10.1021/acs.jpclett.4c02149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Graphene has demonstrated potential for use in neuromorphic electronics due to its superior electrical properties. However, these devices are all based on graphene sheets without patterning, restricting its applications. Here, we demonstrate a graphene nanoribbon synaptic transistor (GNST), with the graphene nanoribbon (GNR) channels fabricated using an electro-hydrodynamically printed nanowire array as lithographic masks for scalable fabrication. The GNST shows tunable synaptic plasticity by spike duration, frequency, and number. Moreover, the device is energy-efficient and ambipolar and shows a regulated response by nanoribbon width. The characteristics of GNSTs are applicable to pattern recognition, showing an accuracy of 84.5%. The device is applicable to Pavlov's classical conditioning. This study reports the first synaptic transistor based on GNRs, providing new insights into future neuromorphic electronics.
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Affiliation(s)
- Mingxin Sun
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen 518000, China
| | - Zhipeng Xu
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen 518000, China
| | - Shangda Qu
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen 518000, China
| | - Lu Liu
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen 518000, China
| | - Qingshan Zhu
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen 518000, China
| | - Wentao Xu
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen 518000, China
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Dong L, Xue B, Wei G, Yuan S, Chen M, Liu Y, Su Y, Niu Y, Xu B, Wang P. Highly Promising 2D/1D BP-C/CNT Bionic Opto-Olfactory Co-Sensory Artificial Synapses for Multisensory Integration. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2403665. [PMID: 38828870 PMCID: PMC11304314 DOI: 10.1002/advs.202403665] [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: 04/08/2024] [Revised: 05/08/2024] [Indexed: 06/05/2024]
Abstract
The development of high-performance artificial synaptic neuromorphic devices poses a significant challenge in the creation of biomimetic sensing neural systems that seamlessly integrate both sensory and computational functionalities. In pursuit of this objective, promising bionic opto-olfactory co-sensory artificial synapse devices are constructed utilizing the BP-C/CNT (2D/1D) hybrid filter membrane as the resistive layer. Experimental results demonstrated that the devices seamlessly integrated the light modulation, gas detection, and biological synaptic functions into a single device while addressing the challenge with separating artificial synaptic devices from sensors. These devices offered the following advantages: 1) Simulating visual synapses, they can effectively replicate fundamental synaptic functions under both electrical and optical stimulation. 2) By emulating olfactory synapse responses to specific gases, they can achieve ultra-low detection limits and rapid identification of ethanol and acetone gases. 3) They enable photo-olfactory co-sensing simulations that mimic synaptic function under light-modulated pulse conditions in distinct gas environments, facilitating the study of synaptic learning rules and Pavlovian responses. This work provides a pioneering approach for exploring highly stable 2D BP-based optoelectronics and advancing the development of biomimetic neural systems.
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Affiliation(s)
- Liyan Dong
- Xi 'an Key Laboratory of Compound Semiconductor Materials and DevicesSchool of Physics & Information ScienceShaanxi University of Science and TechnologyXi'an710021P. R. China
| | - Baojing Xue
- Xi 'an Key Laboratory of Compound Semiconductor Materials and DevicesSchool of Physics & Information ScienceShaanxi University of Science and TechnologyXi'an710021P. R. China
| | - Guodong Wei
- Xi 'an Key Laboratory of Compound Semiconductor Materials and DevicesSchool of Physics & Information ScienceShaanxi University of Science and TechnologyXi'an710021P. R. China
- Shanxi‐Zheda Institute of Advanced Materials and Chemical EngineeringTaiyuan030024P. R. China
| | - Shuai Yuan
- Xi 'an Key Laboratory of Compound Semiconductor Materials and DevicesSchool of Physics & Information ScienceShaanxi University of Science and TechnologyXi'an710021P. R. China
| | - Mi Chen
- Xi 'an Key Laboratory of Compound Semiconductor Materials and DevicesSchool of Physics & Information ScienceShaanxi University of Science and TechnologyXi'an710021P. R. China
| | - Yue Liu
- Xi 'an Key Laboratory of Compound Semiconductor Materials and DevicesSchool of Physics & Information ScienceShaanxi University of Science and TechnologyXi'an710021P. R. China
| | - Ying Su
- Xi 'an Key Laboratory of Compound Semiconductor Materials and DevicesSchool of Physics & Information ScienceShaanxi University of Science and TechnologyXi'an710021P. R. China
| | - Yong Niu
- Xi 'an Key Laboratory of Compound Semiconductor Materials and DevicesSchool of Physics & Information ScienceShaanxi University of Science and TechnologyXi'an710021P. R. China
| | - Bingshe Xu
- Xi 'an Key Laboratory of Compound Semiconductor Materials and DevicesSchool of Physics & Information ScienceShaanxi University of Science and TechnologyXi'an710021P. R. China
- Shanxi‐Zheda Institute of Advanced Materials and Chemical EngineeringTaiyuan030024P. R. China
| | - Pan Wang
- Xi 'an Key Laboratory of Compound Semiconductor Materials and DevicesSchool of Physics & Information ScienceShaanxi University of Science and TechnologyXi'an710021P. R. China
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Panisilvam J, Lee HY, Byun S, Fan D, Kim S. Two-dimensional material-based memristive devices for alternative computing. NANO CONVERGENCE 2024; 11:25. [PMID: 38937391 PMCID: PMC11211314 DOI: 10.1186/s40580-024-00432-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 06/14/2024] [Indexed: 06/29/2024]
Abstract
Two-dimensional (2D) materials have emerged as promising building blocks for next generation memristive devices, owing to their unique electronic, mechanical, and thermal properties, resulting in effective switching mechanisms for charge transport. Memristors are key components in a wide range of applications including neuromorphic computing, which is becoming increasingly important in artificial intelligence applications. Crossbar arrays are an important component in the development of hardware-based neural networks composed of 2D materials. In this paper, we summarize the current state of research on 2D material-based memristive devices utilizing different switching mechanisms, along with the application of these devices in neuromorphic crossbar arrays. Additionally, we discuss the challenges and future directions for the field.
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Affiliation(s)
- Jey Panisilvam
- Department of Electrical and Electronic Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, 3000, Australia
| | - Ha Young Lee
- Department of Electrical and Electronic Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, 3000, Australia
| | - Sujeong Byun
- Department of Electrical and Electronic Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, 3000, Australia
| | - Daniel Fan
- Department of Electrical and Electronic Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, 3000, Australia
| | - Sejeong Kim
- Department of Electrical and Electronic Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, 3000, Australia.
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Wu X, Chen S, Jiang L, Wang X, Qiu L, Zheng L. Highly Sensitive, Low-Energy-Consumption Biomimetic Olfactory Synaptic Transistors Based on the Aggregation of the Semiconductor Films. ACS Sens 2024; 9:2673-2683. [PMID: 38688032 DOI: 10.1021/acssensors.4c00616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Artificial olfactory synaptic devices with low energy consumption and low detection limits are important for the further development of neuromorphic computing and intelligent robotics. In this work, an ultralow energy consumption and low detection limit imitation olfactory synaptic device based on organic field-effect transistors (OFETs) was prepared. The aggregation state of poly(diketopyrrolopyrrole-selenophene) (PTDPP) semiconductor films is modulated by adding unfavorable solvents and annealing treatments to obtain excellent charge transfer and gas synaptic properties. The regulated OFET device can execute basic biological synaptic functions, including excitatory postsynaptic currents (EPSCs), paired-pulse facilitation (PPF), and the transition from short-term to long-term plasticity, at an ultralow operating voltage of -0.0005 V. The ultralow energy consumption during the biomimetic simulation is in the range of 8.94-88 fJ per spike. Noteworthily, the gas detection limit of the device is as low as 50 ppb, well below normal human NO2 gas perception limits (100-1000 ppb). Additionally, high-pass filtering, Pavlovian conditioned reflexes, and decoding of "Morse code" were simulated. Finally, a grid-free conformal device with outstanding flexibility and stability was fabricated. In conclusion, the control of semiconductor thin-film aggregation provides effective guidance for preparing low-energy-consumption, highly sensitive olfactory nerve-mimicking devices and promoting the development of wearable electronics.
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Affiliation(s)
- Xiaocheng Wu
- National Engineering Lab of Special Display Technology, State Key Lab of Advanced Display Technology, Academy of Optoelectronic Technology, Hefei University of Technology, Hefei 230009, P. R. China
- Intelligent Interconnected Systems Laboratory of Anhui, Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronic Engineering, Hefei University of Technology, Hefei 230009, P. R. China
| | - Siyu Chen
- National Engineering Lab of Special Display Technology, State Key Lab of Advanced Display Technology, Academy of Optoelectronic Technology, Hefei University of Technology, Hefei 230009, P. R. China
- Intelligent Interconnected Systems Laboratory of Anhui, Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronic Engineering, Hefei University of Technology, Hefei 230009, P. R. China
| | - Longlong Jiang
- National Engineering Lab of Special Display Technology, State Key Lab of Advanced Display Technology, Academy of Optoelectronic Technology, Hefei University of Technology, Hefei 230009, P. R. China
- Intelligent Interconnected Systems Laboratory of Anhui, Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronic Engineering, Hefei University of Technology, Hefei 230009, P. R. China
| | - Xiaohong Wang
- National Engineering Lab of Special Display Technology, State Key Lab of Advanced Display Technology, Academy of Optoelectronic Technology, Hefei University of Technology, Hefei 230009, P. R. China
- Intelligent Interconnected Systems Laboratory of Anhui, Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronic Engineering, Hefei University of Technology, Hefei 230009, P. R. China
| | - Longzhen Qiu
- National Engineering Lab of Special Display Technology, State Key Lab of Advanced Display Technology, Academy of Optoelectronic Technology, Hefei University of Technology, Hefei 230009, P. R. China
- Intelligent Interconnected Systems Laboratory of Anhui, Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, School of Instrument Science and Optoelectronic Engineering, Hefei University of Technology, Hefei 230009, P. R. China
| | - Lei Zheng
- School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, China
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Dong L, Yuan S, Wei G, Zhu P, Ma S, Xu B, Yang Y. Artificial Optoelectronic Synapse Based on Violet Phosphorus Microfiber Arrays. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023:e2306998. [PMID: 37963849 DOI: 10.1002/smll.202306998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/31/2023] [Indexed: 11/16/2023]
Abstract
Memristor-based artificial synapses are regarded as the most promising candidate to develop brain-like neuromorphic network computers and overcome the bottleneck of Von-Neumann architecture. Violet phosphorus (VP) as a new allotrope of available phosphorus with outstanding electro-optical properties and stability has attracted more and more attention in the past several years. In this study, large-scale, high-yield VP microfiber vertical arrays have been successfully developed on a Sn-coated graphite paper and are used as the memristor functional layers to build reliable, low-power artificial synaptic devices. The VP devices can well mimic the major synaptic functions such as short-term memory (STM), long-term memory (LTM), paired-pulse facilitation (PPF), spike timing-dependent plasticity (STDP), and spike rate-dependent plasticity (SRDP) under both electrical and light stimulation conditions, even the dendritic synapse functions and simple logical operations. By virtue of the excellent performance, the VP artificial synapse devices can be conductive to building high-performance optic-neural synaptic devices simulating the human-like optic nerve system. On this basis, Pavlov's associative memory can be successfully implemented optically. This study provides a promising approach for the design and manufacture of VP-based artificial synaptic devices and outlines a direction with multifunctional neural devices.
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Affiliation(s)
- Liyan Dong
- Materials Institute of Atomic and Molecular Science, Shaanxi University of Science and Technology, Xi'an, 710021, P. R. China
| | - Shuai Yuan
- Materials Institute of Atomic and Molecular Science, Shaanxi University of Science and Technology, Xi'an, 710021, P. R. China
| | - Guodong Wei
- Materials Institute of Atomic and Molecular Science, Shaanxi University of Science and Technology, Xi'an, 710021, P. R. China
| | - Peifen Zhu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA
| | - Shufang Ma
- Materials Institute of Atomic and Molecular Science, Shaanxi University of Science and Technology, Xi'an, 710021, P. R. China
| | - Bingshe Xu
- Materials Institute of Atomic and Molecular Science, Shaanxi University of Science and Technology, Xi'an, 710021, P. R. China
- Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering, Taiyuan, 030024, P. R. China
| | - Ya Yang
- CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, P. R. China
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Li K, Ji Q, Liang H, Hua Z, Hang X, Zeng L, Han H. Biomedical application of 2D nanomaterials in neuroscience. J Nanobiotechnology 2023; 21:181. [PMID: 37280681 DOI: 10.1186/s12951-023-01920-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/05/2023] [Indexed: 06/08/2023] Open
Abstract
Two-dimensional (2D) nanomaterials, such as graphene, black phosphorus and transition metal dichalcogenides, have attracted increasing attention in biology and biomedicine. Their high mechanical stiffness, excellent electrical conductivity, optical transparency, and biocompatibility have led to rapid advances. Neuroscience is a complex field with many challenges, such as nervous system is difficult to repair and regenerate, as well as the early diagnosis and treatment of neurological diseases are also challenged. This review mainly focuses on the application of 2D nanomaterials in neuroscience. Firstly, we introduced various types of 2D nanomaterials. Secondly, due to the repairment and regeneration of nerve is an important problem in the field of neuroscience, we summarized the studies of 2D nanomaterials applied in neural repairment and regeneration based on their unique physicochemical properties and excellent biocompatibility. We also discussed the potential of 2D nanomaterial-based synaptic devices to mimic connections among neurons in the human brain due to their low-power switching capabilities and high mobility of charge carriers. In addition, we also reviewed the potential clinical application of various 2D nanomaterials in diagnosing and treating neurodegenerative diseases, neurological system disorders, as well as glioma. Finally, we discussed the challenge and future directions of 2D nanomaterials in neuroscience.
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Affiliation(s)
- Kangchen Li
- School of Medicine, Institute of Brain and Cognitive Science, Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, 310015, Zhejiang, China
| | - Qianting Ji
- School of Medicine, Institute of Brain and Cognitive Science, Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, 310015, Zhejiang, China
| | - Huanwei Liang
- School of Medicine, Institute of Brain and Cognitive Science, Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, 310015, Zhejiang, China
| | - Zixuan Hua
- School of Medicine, Institute of Brain and Cognitive Science, Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, 310015, Zhejiang, China
| | - Xinyi Hang
- School of Medicine, Institute of Brain and Cognitive Science, Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, 310015, Zhejiang, China
| | - Linghui Zeng
- School of Medicine, Institute of Brain and Cognitive Science, Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, 310015, Zhejiang, China.
| | - Haijun Han
- School of Medicine, Institute of Brain and Cognitive Science, Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, Hangzhou City University, Hangzhou, 310015, Zhejiang, China.
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Yu T, Fang Y, Chen X, Liu M, Wang D, Liu S, Lei W, Jiang H, Shafie S, Mohtar MN, Pan L, Zhao Z. Hybridization state transition-driven carbon quantum dot (CQD)-based resistive switches for bionic synapses. MATERIALS HORIZONS 2023; 10:2181-2190. [PMID: 36994553 DOI: 10.1039/d3mh00117b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
As an emerging carbon-based material, carbon quantum dots (CQDs) have shown unstoppable prospects in the field of bionic electronics with their outstanding optoelectronic properties and unique biocompatible characteristics. In this study, a novel CQD-based memristor is proposed for neuromorphic computing. Unlike the models that rely on the formation and rupturing of conductive filaments, it is speculated that the resistance switching mechanism of CQD-based memristors is due to the conductive path caused by the hybridization state transition of the sp2 carbon domain and sp3 carbon domain induced by the reversible electric field. This avoids the drawback of uncontrollable nucleation sites leading to the random formation of conductive filaments in resistive switching. Importantly, it illustrates that the coefficient of variation (CV) of the threshold voltage can be as low as -1.551% and 0.083%, which confirms the remarkable uniform switching characteristics. Interestingly, the Pavlov's dog reflection as an important biological behavior can be demonstrated by the samples. Finally, the accuracy recognition rate of MNIST handwriting can reach up to 96.7%, which is very close to the ideal number (97.8%). A carbon-based memristor based on a new mechanism presented provides new possibilities for the improvement of brain-like computing.
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Affiliation(s)
- Tianqi Yu
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China.
| | - Yong Fang
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China.
| | - Xinyue Chen
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China.
| | - Min Liu
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China.
| | - Dong Wang
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China.
| | - Shilin Liu
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China.
| | - Wei Lei
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China.
| | - Helong Jiang
- State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology Chinese Academy of Sciences, Nanjing 210008, People's Republic of China
| | - Suhaidi Shafie
- Institute of Nanoscience and Nanotechnology, University Putra Malaysia, Serdang, Selangor, Malaysia
| | - Mohd Nazim Mohtar
- Institute of Nanoscience and Nanotechnology, University Putra Malaysia, Serdang, Selangor, Malaysia
| | - Likun Pan
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200241, People's Republic of China
| | - Zhiwei Zhao
- Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China.
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Dong Z, Hua Q, Xi J, Shi Y, Huang T, Dai X, Niu J, Wang B, Wang ZL, Hu W. Ultrafast and Low-Power 2D Bi 2O 2Se Memristors for Neuromorphic Computing Applications. NANO LETTERS 2023; 23:3842-3850. [PMID: 37093653 DOI: 10.1021/acs.nanolett.3c00322] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Memristors that emulate synaptic plasticity are building blocks for opening a new era of energy-efficient neuromorphic computing architecture, which will overcome the limitation of the von Neumann bottleneck. Layered two-dimensional (2D) Bi2O2Se, as an emerging material for next-generation electronics, is of great significance in improving the efficiency and performance of memristive devices. Herein, high-quality Bi2O2Se nanosheets are grown by configuring mica substrates face-down on the Bi2O2Se powder. Then, bipolar Bi2O2Se memristors are fabricated with excellent performance including ultrafast switching speed (<5 ns) and low-power consumption (<3.02 pJ). Moreover, synaptic plasticity, such as long-term potentiation/depression (LTP/LTD), paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP), are demonstrated in the Bi2O2Se memristor. Furthermore, MNIST recognition with simulated artificial neural networks (ANN) based on conductance modification could reach a high accuracy of 91%. Notably, the 2D Bi2O2Se enables the memristor to possess ultrafast and low-power attributes, showing great potential in neuromorphic computing applications.
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Affiliation(s)
- Zilong Dong
- Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qilin Hua
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Jianguo Xi
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
| | - Yuanhong Shi
- Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tianci Huang
- Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xinhuan Dai
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jianan Niu
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bingjun Wang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhong Lin Wang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiguo Hu
- Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning 530004, China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
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