1
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Lee S, Huang Y, Chang YF, Baik S, Lee JC, Koo M. Enhancing simulation feasibility for multi-layer 2D MoS 2 RRAM devices: reliability performance learnings from a passive network model. Phys Chem Chem Phys 2024; 26:20962-20970. [PMID: 39046422 DOI: 10.1039/d4cp02669a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2024]
Abstract
While two-dimensional (2D) MoS2 has recently shown promise as a material for resistive random-access memory (RRAM) devices due to its demonstrated resistive switching (RS) characteristics, its practical application faces a significant challenge in industry regarding its limited yield and endurance. Our earlier work introduced an effective switching layer model to understand RS behavior in both mono- and multi-layered MoS2. However, functioning as a phenomenological percolation modeling tool, it lacks the capability to accurately simulate the intricate current-voltage (I-V) characteristics of the device, thereby hindering its practical applicability in 2D RRAM research. In contrast to the established conductive filament model for oxide-based RRAM, the RS mechanism in 2D RRAM remains elusive. This paper presents a novel simulator aimed at providing an intuitive, visual representation of the stochastic behaviors involved in the RS process of multi-layer 2D MoS2 RRAM devices. Building upon the previously proposed phenomenological simulator for 2D RRAM, users can now simulate both the I-V characteristics and the resistive switching behaviors of the RRAM devices. Through comparison with experimental data, it was observed that yield and endurance characteristics are linked to defect distributions in MoS2.
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Affiliation(s)
- Seonjeong Lee
- School of Electrical and Computer Engineering, University of Seoul, Seoul 02504, South Korea
| | - Yifu Huang
- Department of Electrical and Computer Engineering, University of Texas at Austin, 10100 Burnet Road, 78758 Austin, TX, USA
| | - Yao-Feng Chang
- Intel Corporation, 2501 NE Century Road, 97124 Hillsboro, OR, USA
| | - Seungjae Baik
- Semiconductor Research and Development Center, Samsung Electronics, Hwaseong-si 18448, South Korea
| | - Jack C Lee
- Department of Electrical and Computer Engineering, University of Texas at Austin, 10100 Burnet Road, 78758 Austin, TX, USA
| | - Minsuk Koo
- Department of Computer Science and Engineering, Incheon National University, Incheon 22012, South Korea.
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2
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Cao Z, Xiang L, Sun B, Gao K, Yu J, Zhou G, Duan X, Yan W, Lin F, Li Z, Wang R, Lv Y, Ren F, Yao Y, Lu Q. A reversible implantable memristor for health monitoring applications. Mater Today Bio 2024; 26:101096. [PMID: 38831909 PMCID: PMC11145331 DOI: 10.1016/j.mtbio.2024.101096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 05/08/2024] [Accepted: 05/19/2024] [Indexed: 06/05/2024] Open
Abstract
Conventional implantable electronics based on von Neumann architectures encounter significant limitations in computing and processing vast biological information due to computational bottlenecks. The memristor with integrated memory-computing and low power consumption offer a promising solution to overcome the computational bottleneck and Moore's law limitations of traditional silicon-based implantable devices, making them the most promising candidates for next-generation implantable devices. In this work, a highly stable memristor with an Ag/BaTiO3/MnO2/FTO structure was fabricated, demonstrating retention characteristics exceeding 1200 cycles and endurance above 1000 s. The device successfully exhibited three-stage responses to biological signals after implantation in SD (Sprague-Dawley) rats. Importantly, the memristor perform remarkable reversibility, maintaining over 100 cycles of stable repetition even after extraction from the rat. This study provides a new perspective on the biomedical application of memristors, expanding the potential of implantable memristive devices in intelligent medical fields such as health monitoring and auxiliary diagnostics.
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Affiliation(s)
- Zelin Cao
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Linbiao Xiang
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Bai Sun
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Kaikai Gao
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Jiawei Yu
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of 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
| | - Xuegang Duan
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Wentao Yan
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Fulai Lin
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Zhuoqun Li
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Ruixin Wang
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Yi Lv
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Fenggang Ren
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Yingmin Yao
- Department of Geriatric Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Qiang Lu
- Department of Geriatric Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
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3
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Wang S, Gao S, Tang C, Occhipinti E, Li C, Wang S, Wang J, Zhao H, Hu G, Nathan A, Dahiya R, Occhipinti LG. Memristor-based adaptive neuromorphic perception in unstructured environments. Nat Commun 2024; 15:4671. [PMID: 38821961 PMCID: PMC11143376 DOI: 10.1038/s41467-024-48908-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 05/16/2024] [Indexed: 06/02/2024] Open
Abstract
Efficient operation of control systems in robotics or autonomous driving targeting real-world navigation scenarios requires perception methods that allow them to understand and adapt to unstructured environments with good accuracy, adaptation, and generality, similar to humans. To address this need, we present a memristor-based differential neuromorphic computing, perceptual signal processing, and online adaptation method providing neuromorphic style adaptation to external sensory stimuli. The adaptation ability and generality of this method are confirmed in two application scenarios: object grasping and autonomous driving. In the former, a robot hand realizes safe and stable grasping through fast ( ~ 1 ms) adaptation based on the tactile object features with a single memristor. In the latter, decision-making information of 10 unstructured environments in autonomous driving is extracted with an accuracy of 94% with a 40×25 memristor array. By mimicking human low-level perception mechanisms, the electronic neuromorphic circuit-based method achieves real-time adaptation and high-level reactions to unstructured environments.
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Affiliation(s)
- Shengbo Wang
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China
| | - Shuo Gao
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China.
| | - Chenyu Tang
- Department of Engineering, University of Cambridge, Cambridge, UK
| | - Edoardo Occhipinti
- UKRI Centre for Doctoral Training in AI for Healthcare, Department of Computing, Imperial College London, London, UK
| | - Cong Li
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China
| | - Shurui Wang
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China
| | - Jiaqi Wang
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China
| | - Hubin Zhao
- HUB of Intelligent Neuro-engineering (HUBIN), CREATe, Division of Surgery and Interventional Science, UCL, HA7 4LP, Stanmore, UK
| | - Guohua Hu
- Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, N. T., Hong Kong S. A. R., China
| | - Arokia Nathan
- Darwin College, University of Cambridge, Cambridge, UK
- School of Information Science and Engineering, Shandong University, Qingdao, 266237, China
| | - Ravinder Dahiya
- Bendable Electronics and Sustainable Technologies (BEST) Group, Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115, USA
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4
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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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5
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Yadav R, Poudyal S, Rajarapu R, Biswal B, Barman PK, Kasiviswanathan S, Novoselov KS, Misra A. Low Power Volatile and Nonvolatile Memristive Devices from 1D MoO 2-MoS 2 Core-Shell Heterostructures for Future Bio-Inspired Computing. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2309163. [PMID: 38150637 DOI: 10.1002/smll.202309163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/05/2023] [Indexed: 12/29/2023]
Abstract
Memristors-based integrated circuits for emerging bio-inspired computing paradigms require an integrated approach utilizing both volatile and nonvolatile memristive devices. Here, an innovative architecture comprising of 1D CVD-grown core-shell heterostructures (CSHSs) of MoO2-MoS2 is employed as memristors manifesting both volatile switching (with high selectivity of 107 and steep slope of 0.6 mV decade-1) and nonvolatile switching phenomena (with Ion/Ioff ≈103 and switching speed of 60 ns). In these CSHSs, the metallic core MoO2 with high current carrying capacity provides a conformal and immaculate interface with semiconducting MoS2 shells and therefore it acts as a bottom electrode for the memristors. The power consumption in volatile devices is as low as 50 pW per set transition and 0.1 fW in standby mode. Voltage-driven current spikes are observed for volatile devices while with nonvolatile memristors, key features of a biological synapse such as short/long-term plasticity and paired pulse facilitation are emulated suggesting their potential for the development of neuromorphic circuits. These CSHSs offer an unprecedented solution for the interfacial issues between metallic electrodes and the layered materials-based switching element with the prospects of developing smaller footprint memristive devices for future integrated circuits.
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Affiliation(s)
- Renu Yadav
- Department of Physics, Indian Institute of Technology Madras, Chennai, 600036, India
- Centre for 2D Materials Research and Innovation, Indian Institute of Technology Madras, Chennai, 600036, India
| | - Saroj Poudyal
- Department of Physics, Indian Institute of Technology Madras, Chennai, 600036, India
- Centre for 2D Materials Research and Innovation, Indian Institute of Technology Madras, Chennai, 600036, India
| | - Ramesh Rajarapu
- Department of Physics, Indian Institute of Technology Madras, Chennai, 600036, India
- Centre for 2D Materials Research and Innovation, Indian Institute of Technology Madras, Chennai, 600036, India
| | - Bubunu Biswal
- Department of Physics, Indian Institute of Technology Madras, Chennai, 600036, India
- Centre for 2D Materials Research and Innovation, Indian Institute of Technology Madras, Chennai, 600036, India
| | - Prahalad Kanti Barman
- Department of Physics, Indian Institute of Technology Madras, Chennai, 600036, India
- Centre for 2D Materials Research and Innovation, Indian Institute of Technology Madras, Chennai, 600036, India
| | - S Kasiviswanathan
- Department of Physics, Indian Institute of Technology Madras, Chennai, 600036, India
| | - Kostya S Novoselov
- Institute for Functional Intelligent Materials, National University of Singapore, Singapore, 117544, Singapore
| | - Abhishek Misra
- Department of Physics, Indian Institute of Technology Madras, Chennai, 600036, India
- Centre for 2D Materials Research and Innovation, Indian Institute of Technology Madras, Chennai, 600036, India
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6
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He L, Lang S, Zhang W, Song S, Lyu J, Gong J. First-Principles Prediction of High and Low Resistance States in Ta/h-BN/Ta Atomristor. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:612. [PMID: 38607146 PMCID: PMC11013407 DOI: 10.3390/nano14070612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 03/13/2024] [Accepted: 03/20/2024] [Indexed: 04/13/2024]
Abstract
Two-dimensional (2D) materials have received significant attention for their potential use in next-generation electronics, particularly in nonvolatile memory and neuromorphic computing. This is due to their simple metal-insulator-metal (MIM) sandwiched structure, excellent switching performance, high-density capability, and low power consumption. In this work, using comprehensive material simulations and device modeling, the thinnest monolayer hexagonal boron nitride (h-BN) atomristor is studied by using a MIM configuration with Ta electrodes. Our first-principles calculations predicted both a high resistance state (HRS) and a low resistance state (LRS) in this device. We observed that the presence of van der Waals (vdW) gaps between the Ta electrodes and monolayer h-BN with a boron vacancy (VB) contributes to the HRS. The combination of metal electrode contact and the adsorption of Ta atoms onto a single VB defect (TaB) can alter the interface barrier between the electrode and dielectric layer, as well as create band gap states within the band gap of monolayer h-BN. These band gap states can shorten the effective tunneling path for electron transport from the left electrode to the right electrode, resulting in an increase in the current transmission coefficient of the LRS. This resistive switching mechanism in monolayer h-BN atomristors can serve as a theoretical reference for device design and optimization, making them promising for the development of atomristor technology with ultra-high integration density and ultra-low power consumption.
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Affiliation(s)
- Lan He
- School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Shuai Lang
- School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Wei Zhang
- School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Shun Song
- State Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
| | - Juan Lyu
- School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
| | - Jian Gong
- School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China
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7
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Li Y, Xiong Y, Zhai B, Yin L, Yu Y, Wang H, He J. Ag-doped non-imperfection-enabled uniform memristive neuromorphic device based on van der Waals indium phosphorus sulfide. SCIENCE ADVANCES 2024; 10:eadk9474. [PMID: 38478614 PMCID: PMC10936950 DOI: 10.1126/sciadv.adk9474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 02/06/2024] [Indexed: 03/17/2024]
Abstract
Memristors are considered promising energy-efficient artificial intelligence hardware, which can eliminate the von Neumann bottleneck by parallel in-memory computing. The common imperfection-enabled memristors are plagued with critical variability issues impeding their commercialization. Reported approaches to reduce the variability usually sacrifice other performances, e.g., small on/off ratios and high operation currents. Here, we demonstrate an unconventional Ag-doped nonimperfection diffusion channel-enabled memristor in van der Waals indium phosphorus sulfide, which can combine ultralow variabilities with desirable metrics. We achieve operation voltage, resistance, and on/off ratio variations down to 3.8, 2.3, and 6.9% at their extreme values of 0.2 V, 1011 ohms, and 108, respectively. Meanwhile, the operation current can be pushed from 1 nA to 1 pA at the scalability limit of 6 nm after Ag doping. Fourteen Boolean logic functions and convolutional image processing are successfully implemented by the memristors, manifesting the potential for logic-in-memory devices and efficient non-von Neumann accelerators.
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Affiliation(s)
- Yesheng Li
- Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, and School of Physical and Technology, Wuhan University, Wuhan 430072, China
- Suzhou Institute of Wuhan University, Suzhou 215123, China
| | - Yao Xiong
- School of Science, Wuhan University of Technology, Wuhan 430070, China
| | - Baoxing Zhai
- Institute of Semiconductors, Henan Academy of Sciences, Zhengzhou 450046, China
| | - Lei Yin
- Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, and School of Physical and Technology, Wuhan University, Wuhan 430072, China
| | - Yiling Yu
- Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, and School of Physical and Technology, Wuhan University, Wuhan 430072, China
| | - Hao Wang
- Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, and School of Physical and Technology, Wuhan University, Wuhan 430072, China
| | - Jun He
- Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, and School of Physical and Technology, Wuhan University, Wuhan 430072, China
- Institute of Semiconductors, Henan Academy of Sciences, Zhengzhou 450046, China
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8
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Wang G, Sun F, Zhou S, Zhang Y, Zhang F, Wang H, Huang J, Zheng Y. Enhanced Memristive Performance via a Vertically Heterointerface in Nanocomposite Thin Films for Artificial Synapses. ACS APPLIED MATERIALS & INTERFACES 2024; 16:12073-12084. [PMID: 38381527 DOI: 10.1021/acsami.3c18146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Memristors can be used to mimic synaptic behavior in artificial neural networks, which makes them a key component in neuromorphic computing and holds promise for advancing the field. In this study, a memory artificial synaptic device based on ZnO-BaTiO3 (ZnO-BTO) vertically aligned nanocomposite thin films was prepared. The vertical interface between the two phases can be used as a conduit for oxygen vacancy (OV) accumulation and a channel for OV movement, which greatly optimizes the resistive switching performance of the device and has the potential for multistage storage. By applying different pulse sequences to the device, the conductance of the device is adjusted from multiple angles, and a variety of synaptic functions are simulated, such as paired-pulse facilitation, spike-timing-dependent plasticity, short-term plasticity to long-term plasticity (STP-LTP), and long-term potentiation/depression (LTP/LTD). Finally, we construct a neural network for image recognition, and the recognition accuracy can reach 91%. Our study demonstrates the feasibility of using composite thin-film vertical interface to regulate the resistive performance of memristors and its great potential in artificial synaptic simulation and neuromorphic computing.
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Affiliation(s)
- Guoliang Wang
- School of Materials, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
- State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
- Centre for Physical Mechanics and Biophysics, School of Physics, Sun Yat-sen University, Guangzhou 510275, China
| | - Fei Sun
- School of Physics, Sun Yat-sen University, Guangzhou 510275, China
- State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
- Centre for Physical Mechanics and Biophysics, School of Physics, Sun Yat-sen University, Guangzhou 510275, China
| | - Shiyu Zhou
- School of Materials Engineering, Purdue University, West Lafayette, Indiana 47907, United States
| | - Yizhi Zhang
- School of Materials Engineering, Purdue University, West Lafayette, Indiana 47907, United States
| | - Fan Zhang
- School of Physics, Sun Yat-sen University, Guangzhou 510275, China
- State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
- Centre for Physical Mechanics and Biophysics, School of Physics, Sun Yat-sen University, Guangzhou 510275, China
| | - Haiyan Wang
- School of Materials Engineering, Purdue University, West Lafayette, Indiana 47907, United States
| | - Jijie Huang
- School of Materials, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
- State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
- Centre for Physical Mechanics and Biophysics, School of Physics, Sun Yat-sen University, Guangzhou 510275, China
| | - Yue Zheng
- School of Physics, Sun Yat-sen University, Guangzhou 510275, China
- State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510275, China
- Centre for Physical Mechanics and Biophysics, School of Physics, Sun Yat-sen University, Guangzhou 510275, China
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9
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Li Z, Wang J, Xu L, Wang L, Shang H, Ying H, Zhao Y, Wen L, Guo C, Zheng X. Achieving Reliable and Ultrafast Memristors via Artificial Filaments in Silk Fibroin. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2308843. [PMID: 37934889 DOI: 10.1002/adma.202308843] [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: 08/30/2023] [Revised: 10/28/2023] [Indexed: 11/09/2023]
Abstract
The practical implementation of memristors in neuromorphic computing and biomimetic sensing suffers from unexpected temporal and spatial variations due to the stochastic formation and rupture of conductive filaments (CFs). Here, the biocompatible silk fibroin (SF) is patterned with an on-demand nanocone array by using thermal scanning probe lithography (t-SPL) to guide and confine the growth of CFs in the silver/SF/gold (Ag/SF/Au) memristor. Benefiting from the high fabrication controllability, cycle-to-cycle (temporal) standard deviation of the set voltage for the structured memristor is significantly reduced by ≈95.5% (from 1.535 to 0.0686 V) and the device-to-device (spatial) standard deviation is also reduced to 0.0648 V. Besides, the statistical relationship between the structural nanocone design and the resultant performance is confirmed, optimizing at the small operation voltage (≈0.5 V) and current (100 nA), ultrafast switching speed (sub-100 ns), large on/off ratio (104 ), and the smallest switching slope (SS < 0.01 mV dec-1 ). Finally, the short-term plasticity and leaky integrated-and-fire behavior are emulated, and a reliable thermal nociceptor system is demonstrated for practical neuromorphic applications.
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Affiliation(s)
- Zishun Li
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China
| | - Jiaqi Wang
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China
| | - Lanxin Xu
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China
| | - Li Wang
- Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Hongpeng Shang
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China
| | - Haoting Ying
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China
| | - Yingjie Zhao
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China
| | - Liaoyong Wen
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, 310030, China
| | - Chengchen Guo
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, 310030, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, 310024, China
| | - Xiaorui Zheng
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, 310030, China
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10
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Tong W, Wei W, Zhang X, Ding S, Lu Z, Liu L, Li W, Pan C, Kong L, Wang Y, Zhu M, Liang SJ, Miao F, Liu Y. Highly Stable HfO 2 Memristors through van der Waals Electrode Lamination and Delamination. NANO LETTERS 2023; 23:9928-9935. [PMID: 37862098 DOI: 10.1021/acs.nanolett.3c02888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Memristors have attracted considerable attention in the past decade, holding great promise for future neuromorphic computing. However, the intrinsic poor stability and large device variability remain key limitations for practical application. Here, we report a simple method to directly visualize the origin of poor stability. By mechanically removing the top electrodes of memristors operated at different states (such as SET or RESET), the memristive layer could be exposed and directly characterized through conductive atomic force microscopy, providing two-dimensional area information within memristors. Based on this technique, we observed the existence of multiple conducting filaments during the formation process and built up a physical model between filament numbers and the cycle-to-cycle variation. Furthermore, by improving the interface quality through the van der Waals top electrode, we could reduce the filament number down to a single filament during all switching cycles, leading to much controlled switching behavior and reliable device operation.
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Affiliation(s)
- Wei Tong
- Key Laboratory for Micro-Nano Optoelectronic Devices of Ministry of Education, School of Physics and Electronics, Hunan University, Changsha 410082, China
| | - Wei Wei
- National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Xiangzhe Zhang
- College of Advanced Interdisciplinary Studies & Hunan Provincial Key Laboratory of Novel Nano-Optoelectronic Information Materials and Devices, National University of Defense Technology, Changsha 410073, China
| | - Shuimei Ding
- Key Laboratory for Micro-Nano Optoelectronic Devices of Ministry of Education, School of Physics and Electronics, Hunan University, Changsha 410082, China
| | - Zheyi Lu
- Key Laboratory for Micro-Nano Optoelectronic Devices of Ministry of Education, School of Physics and Electronics, Hunan University, Changsha 410082, China
| | - Liting Liu
- Key Laboratory for Micro-Nano Optoelectronic Devices of Ministry of Education, School of Physics and Electronics, Hunan University, Changsha 410082, China
| | - Wanying Li
- Key Laboratory for Micro-Nano Optoelectronic Devices of Ministry of Education, School of Physics and Electronics, Hunan University, Changsha 410082, China
| | - Chen Pan
- Institute of Interdisciplinary of Physical Sciences, School of Science, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Lingan Kong
- Key Laboratory for Micro-Nano Optoelectronic Devices of Ministry of Education, School of Physics and Electronics, Hunan University, Changsha 410082, China
| | - Yiliu Wang
- Key Laboratory for Micro-Nano Optoelectronic Devices of Ministry of Education, School of Physics and Electronics, Hunan University, Changsha 410082, China
| | - Mengjian Zhu
- College of Advanced Interdisciplinary Studies & Hunan Provincial Key Laboratory of Novel Nano-Optoelectronic Information Materials and Devices, National University of Defense Technology, Changsha 410073, China
| | - Shi-Jun Liang
- National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Feng Miao
- National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Yuan Liu
- Key Laboratory for Micro-Nano Optoelectronic Devices of Ministry of Education, School of Physics and Electronics, Hunan University, Changsha 410082, China
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11
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Yoo C, Adepu V, Han SS, Kim JH, Shin JC, Cao J, Park J, Al Mahfuz MM, Tetard L, Lee GH, Ko DK, Sahatiya P, Jung Y. Low-Temperature Centimeter-Scale Growth of Layered 2D SnS for Piezoelectric Kirigami Devices. ACS NANO 2023; 17:20680-20688. [PMID: 37831937 DOI: 10.1021/acsnano.3c08826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/15/2023]
Abstract
Tin monosulfide (SnS) is a promising piezoelectric material with an intrinsically layered structure, making it attractive for self-powered wearable and stretchable devices. However, for practical application purposes, it is essential to improve the output and manufacturing compatibility of SnS-based piezoelectric devices by exploring their large-area synthesis principle. In this study, we report the chemical vapor deposition (CVD) growth of centimeter-scale two-dimensional (2D) SnS layers at temperatures as low as 200 °C, allowing compatibility with processing a range of polymeric substrates. The intrinsic piezoelectricity of 2D SnS layers directly grown on polyamides (PIs) was confirmed by piezoelectric force microscopy (PFM) phase maps and force-current corroborative measurements. Furthermore, the structural robustness of the centimeter-scale 2D SnS layers/PIs allowed for engraving complicated kirigami patterns on them. The kirigami-patterned 2D SnS layer devices exhibited intriguing strain-tolerant piezoelectricity, which was employed in detecting human body motions and generating photocurrents irrespective of strain rate variations. These results establish the great promise of 2D SnS layers for practically relevant large-scale device technologies with coupled electrical and mechanical properties.
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Affiliation(s)
- Changhyeon Yoo
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
| | - Vivek Adepu
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
- Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani Hyderabad Campus, Hyderabad, 500078, India
| | - Sang Sub Han
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
| | - Jung Han Kim
- Department of Materials Science and Engineering, Dong-A University, Busan 49315, Republic of Korea
| | - June-Chul Shin
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Justin Cao
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
- Department of Materials Science and Engineering, University of Central Florida, Orlando, Florida 32816, United States
| | - Junsung Park
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, New Jersey 07102, United States
| | - Mohammad M Al Mahfuz
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, New Jersey 07102, United States
| | - Laurene Tetard
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
- Physics Department, University of Central Florida, Orlando, Florida, 32816, United States
| | - Gwan-Hyoung Lee
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Dong-Kyun Ko
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, New Jersey 07102, United States
| | - Parikshit Sahatiya
- Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani Hyderabad Campus, Hyderabad, 500078, India
| | - Yeonwoong Jung
- NanoScience Technology Center, University of Central Florida, Orlando, Florida 32826, United States
- Department of Materials Science and Engineering, University of Central Florida, Orlando, Florida 32816, United States
- Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Florida 32816, United States
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12
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Liu G, Wang W, Guo Z, Jia X, Zhao Z, Zhou Z, Niu J, Duan G, Yan X. Silicon based Bi 0.9La 0.1FeO 3 ferroelectric tunnel junction memristor for convolutional neural network application. NANOSCALE 2023; 15:13009-13017. [PMID: 37485606 DOI: 10.1039/d3nr00510k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Computing in memory (CIM) based on memristors is expected to completely solve the dilemma caused by von Neumann architecture. However, the performance of memristors based on traditional conductive filament mechanism is unstable. In this study, we report a nonvolatile high-performance memristor based on ferroelectric tunnel junction (FTJ) Pd/Bi0.9La0.1FeO3 (6.9 nm) (BLFO)/La0.67Sr0.33MnO3 (LSMO) on a silicon substrate. The conductance of this device was adjusted by different pulse stimulation parameter to achieve various synaptic functions because of ferroelectric polarization reversal. Based on the multiple conductance characteristics of the devices and the high linearity and symmetry of weight updating, image processing and VGG8 convolutional neural network (CNN) simulation based on the devices were realized. Excellent results of the image processing are demonstrated. The recognition accuracy of CNN offline learning reached an astonishing 92.07% based on Cifar-10 dataset. This provides a more feasible solution to break through the bottleneck of von Neumann architecture.
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Affiliation(s)
- Gongjie Liu
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Wei Wang
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Zhenqiang Guo
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Xiaotong Jia
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Zhen Zhao
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Zhenyu Zhou
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Jiangzhen Niu
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Guojun Duan
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
| | - Xiaobing Yan
- Key Laboratory of brain-like neuromorphic devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China.
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13
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Yesilpinar D, Vondráček M, Čermák P, Mönig H, Kopeček J, Caha O, Carva K, Drašar Č, Honolka J. Defect pairing in Fe-doped SnS van der Waals crystals: a photoemission and scanning tunneling microscopy study. NANOSCALE 2023; 15:13110-13119. [PMID: 37503562 DOI: 10.1039/d3nr01905e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
We investigate the effect of low concentrations of iron on the physical properties of SnS van der Waals crystals grown from the melt. By means of scanning tunneling microscopy (STM) and photoemission spectroscopy we study Fe-induced defects and observe an electron doping effect in the band structure of the native p-type SnS semiconductor. Atomically resolved and bias dependent STM data of characteristic defects are compared to ab initio density functional theory simulations of vacancy (VS and VSn), Fe substitutional (FeSn), and Fe interstitial (Feint) defects. While native SnS is dominated by acceptor-like VSn vacancies, our results show that Fe preferentially occupies donor-like interstitial Feint sites in close proximity to VSn defects along the high-symmetry c-axis of SnS. The formation of such well-defined coupled (VSn, Feint) defect pairs leads to local compensation of the acceptor-like character of VSn, which is in line with a reduction of p-type carrier concentrations observed in our Hall transport measurements.
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Affiliation(s)
- Damla Yesilpinar
- Institute of Physics, AV ČR, Na Slovance 1999/2, 182 21 Praha 8, Czechia.
| | - Martin Vondráček
- Institute of Physics, AV ČR, Na Slovance 1999/2, 182 21 Praha 8, Czechia.
| | - Patrik Čermák
- Faculty of Chemical Technology, University of Pardubice, Studentská 573, 532 10 Pardubice, Czechia
| | - Harry Mönig
- Physikalisches Institut, Wilhelm-Klemm Str. 10, 48149 Münster, DE, Germany
| | - Jaromír Kopeček
- Institute of Physics, AV ČR, Na Slovance 1999/2, 182 21 Praha 8, Czechia.
| | - Ondřej Caha
- Department of Condensed Matter Physics, Masaryk University, Žerotínovo nám. 617/9, 601 77 Brno, Czechia
| | - Karel Carva
- Department of Condensed Matter Physics, Charles University, Ke Karlovu 5, 121 16 Prague, Czechia
| | - Čestmír Drašar
- Faculty of Chemical Technology, University of Pardubice, Studentská 573, 532 10 Pardubice, Czechia
| | - Jan Honolka
- Institute of Physics, AV ČR, Na Slovance 1999/2, 182 21 Praha 8, Czechia.
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14
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Ahn W, Jeong HB, Oh J, Hong W, Cha JH, Jeong HY, Choi SY. A Highly Reliable Molybdenum Disulfide-Based Synaptic Memristor Using a Copper Migration-Controlled Structure. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2300223. [PMID: 37093184 DOI: 10.1002/smll.202300223] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/13/2023] [Indexed: 05/03/2023]
Abstract
Memristors are drawing attention as neuromorphic hardware components because of their non-volatility and analog programmability. In particular, electrochemical metallization (ECM) memristors are extensively researched because of their linear conductance controllability. Two-dimensional materials as switching medium of ECM memristors give advantages of fast speed, low power consumption, and high switching uniformity. However, the multistate retention in the switching conductance range for the long-term reliable neuromorphic system has not been achieved using two-dimensional materials-based ECM memristors. In this study, the copper migration-controlled ECM memristor showing excellent multistate retention characteristics in the switching conductance range using molybdenum disulfide (MoS2 ) and aluminum oxide (Al2 O3 ) is proposed. The fabricated device exhibits gradual resistive switching with low switching voltage (<0.5 V), uniform switching (σ/µ ∼ 0.07), and a wide switching range (>12). Importantly, excellent reliabilities with robustness to cycling stress and retention over 104 s for more than 5-bit states in the switching conductance range are achieved. Moreover, the contribution of the Al2 O3 layer to the retention characteristic is investigated through filament morphology observation using transmission electron microscopy (TEM) and copper migration component analysis. This study provides a practical approach to developing highly reliable memristors with exceptional switching performance.
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Affiliation(s)
- Wonbae Ahn
- Graphene/2D Materials Research Center, School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Han Beom Jeong
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Jungyeop Oh
- Graphene/2D Materials Research Center, School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Woonggi Hong
- Convergence Semiconductor Research Center, School of Electronics and Electrical Engineering, Dankook University, 152 Jukjeon-ro, Suji-gu, Yongin-si, Gyeonggi-do 16890, Republic of Korea
| | - Jun-Hwe Cha
- Graphene/2D Materials Research Center, School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Hu Young Jeong
- Graduate School of Semiconductor Materials and Devices Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 44919, Republic of Korea
| | - Sung-Yool Choi
- Graphene/2D Materials Research Center, School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
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15
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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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16
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Wang L, Zuo Z, Wen D. Realization of Artificial Nerve Synapses Based on Biological Threshold Resistive Random Access Memory. Adv Biol (Weinh) 2023; 7:e2200298. [PMID: 36650948 DOI: 10.1002/adbi.202200298] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/09/2022] [Indexed: 01/19/2023]
Abstract
A one-selector one resistor (1S1R) array formed of a selector and resistive random access memory (RRAM) is an important way to achieve high-density storage and neuromorphic computing. However, the low durability and poor consistency of the selector limit its practical application. The fabrication of a selector based on egg albumen (EA) is reported in this paper. The device exhibits excellent bidirectional threshold switching characteristics, including a low leakage current (10-7 A), a high ON/OFF current ratio (106 ), and good endurance (>700 days). It is used as a selector to form a 1S1R unit in combination with an EA-based RRAM to effectively solve the leakage current in a crossbar array. A feasible solution is provided for the realization of a protein-based 1S1R array to achieve high-density storage. The 1S1R unit shows characteristics similar to those of synapses in the human brain under impulse excitation and has great potential in simulating the human brain for neuromorphic calculations.).
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Affiliation(s)
- Lu Wang
- School of Electronic Engineering, Heilongjiang University, Harbin, 150080, P. R. China
| | - Ze Zuo
- School of Electronic Engineering, Heilongjiang University, Harbin, 150080, P. R. China
| | - Dianzhong Wen
- School of Electronic Engineering, Heilongjiang University, Harbin, 150080, P. R. China
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17
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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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18
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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: 11] [Impact Index Per Article: 11.0] [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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19
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Seok H, Son S, Jathar SB, Lee J, Kim T. Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:3118. [PMID: 36991829 PMCID: PMC10058286 DOI: 10.3390/s23063118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/11/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.
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Affiliation(s)
- Hyunho Seok
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Shihoon Son
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Sagar Bhaurao Jathar
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Jaewon Lee
- School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Taesung Kim
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
- School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
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20
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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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21
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Kim D, Lee HJ, Yang TJ, Choi WS, Kim C, Choi SJ, Bae JH, Kim DM, Kim S, Kim DH. Effect of Post-Annealing on Barrier Modulations in Pd/IGZO/SiO 2/p +-Si Memristors. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:3582. [PMID: 36296772 PMCID: PMC9610976 DOI: 10.3390/nano12203582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 10/07/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
In this article, we study the post-annealing effect on the synaptic characteristics in Pd/IGZO/SiO2/p+-Si memristor devices. The O-H bond in IGZO films affects the switching characteristics that can be controlled by the annealing process. We propose a switching model based on using a native oxide as the Schottky barrier. The barrier height is extracted by the conduction mechanism of thermionic emission in samples with different annealing temperatures. Additionally, the change in conductance is explained by an energy band diagram including trap models. The activation energy is obtained by the depression curve of the samples with different annealing temperatures to better understand the switching mechanism. Moreover, our results reveal that the annealing temperature and retention can affect the linearity of potentiation and depression. Finally, we investigate the effect of the annealing temperature on the recognition rate of MNIST in the proposed neural network.
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Affiliation(s)
- Donguk Kim
- School of Electrical Engineering, Kookmin University, Seoul 02707, Korea
| | - Hee Jun Lee
- School of Electrical Engineering, Kookmin University, Seoul 02707, Korea
| | - Tae Jun Yang
- School of Electrical Engineering, Kookmin University, Seoul 02707, Korea
| | - Woo Sik Choi
- School of Electrical Engineering, Kookmin University, Seoul 02707, Korea
| | - Changwook Kim
- School of Electrical Engineering, Kookmin University, Seoul 02707, Korea
| | - Sung-Jin Choi
- School of Electrical Engineering, Kookmin University, Seoul 02707, Korea
| | - Jong-Ho Bae
- School of Electrical Engineering, Kookmin University, Seoul 02707, Korea
| | - Dong Myong Kim
- School of Electrical Engineering, Kookmin University, Seoul 02707, Korea
| | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Korea
| | - Dae Hwan Kim
- School of Electrical Engineering, Kookmin University, Seoul 02707, Korea
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22
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Wang Y, Zhou G, Sun B, Wang W, Li J, Duan S, Song Q. Ag/HfO x/Pt Unipolar Memristor for High-Efficiency Logic Operation. J Phys Chem Lett 2022; 13:8019-8025. [PMID: 35993690 DOI: 10.1021/acs.jpclett.2c01906] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Unipolar resistive switching (URS) behavior, known as the SET and RESET operating in a single voltage sweep direction, has shown great potential in the simplification of the peripheral circuit. The URS memristor always involves complicated interfacial engineering and structural design. In this work, a reliable URS behavior is realized using a simple Ag/HfOx/Pt memristor structure. The memristor displays a retention time of >104 s, an ON/OFF ratio of >103, and a good operation voltage. Synergy and competition between the Ag conductive filament formed by redox reaction and the migration of an oxygen vacancy are responsible for the observed URS. By comparison, a 35% power consumption is reduced during the logical operation from 0 to 1 to 0. The operation strategy is demonstrated by exhibiting the ACSII code of the capital letter denoted by eight logic states. This work provides a low-power concept for ultrahigh data storage using the URS memristor.
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Affiliation(s)
- Yuchen Wang
- School of Materials and Energy, Southwest University, Chongqing 400715, China
| | - Guangdong Zhou
- School of Materials and Energy, Southwest University, Chongqing 400715, China
- College of Artificial Intelligence, Southwest University, Chongqing 400715, China
| | - Bai Sun
- Department of Mechanics and Mechatronics Engineering, Centre for Advanced Materials Joining, Waterloo Institute for Nanotechnology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
| | - Wenhua Wang
- School of Materials and Energy, Southwest University, Chongqing 400715, China
| | - Jie Li
- College of Artificial Intelligence, Southwest University, Chongqing 400715, China
| | - Shukai Duan
- College of Artificial Intelligence, Southwest University, Chongqing 400715, China
| | - Qunliang Song
- School of Materials and Energy, Southwest University, Chongqing 400715, China
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23
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Duan H, Cheng S, Qin L, Zhang X, Xie B, Zhang Y, Jie W. Low-Power Memristor Based on Two-Dimensional Materials. J Phys Chem Lett 2022; 13:7130-7138. [PMID: 35900941 DOI: 10.1021/acs.jpclett.2c01962] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The memristor is an excellent candidate for nonvolatile memory and neuromorphic computing. Recently, two-dimensional (2D) materials have been developed for use in memristors with high-performance resistive switching characteristics, such as high on/off ratios, low SET/RESET voltages, good retention and endurance, fast switching speed, and low power and energy consumption. Low-power memristors are highly desired for recent fast-speed and energy-efficient artificial neuromorphic networks. This Perspective focuses on the recent progress of low-power memristors based on 2D materials, providing a condensed overview of relevant developments in memristive performance, physical mechanism, material modification, and device assembly as well as potential applications. The detailed research status of memristors has been reviewed based on different 2D materials from insulating hexagonal boron nitride, semiconducting transition metal dichalcogenides, to some newly developed 2D materials. Furthermore, a brief summary introducing the perspectives and challenges is included, with the aim of providing an insightful guide for this research field.
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Affiliation(s)
- Huan Duan
- 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
| | - Ling Qin
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu 610066, China
| | - Xuelian Zhang
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu 610066, China
| | - Bingyang Xie
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu 610066, China
| | - Yang Zhang
- Institute of Modern Optics & Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology, Nankai University, Tianjin 300071, China
| | - Wenjing Jie
- College of Chemistry and Materials Science, Sichuan Normal University, Chengdu 610066, China
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24
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Li Y, Wang J, Yang Q, Shen G. Flexible Artificial Optoelectronic Synapse based on Lead-Free Metal Halide Nanocrystals for Neuromorphic Computing and Color Recognition. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2202123. [PMID: 35661449 PMCID: PMC9353487 DOI: 10.1002/advs.202202123] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/14/2022] [Indexed: 05/04/2023]
Abstract
Optoelectronic synapses combining optical-sensing and synaptic functions are playing an increasingly vital role in the neuromorphic computing systems development, which can efficiently process visual information and complex recognition, memory, and learning. Metal halides are considered promising candidates for synaptic devices due to their excellent optoelectronic properties. However, the toxicity of lead and the further development of device functions are the recognized problems at present. Herein, a flexible optoelectronic synapses system based on high-quality lead-free Cs3 Bi2 I9 nanocrystals is demonstrated, in which the carrier confinement caused by the band mismatching between the Cs3 Bi2 I9 and the organic semiconductor layer provides the possibility to simulate synaptic behaviors. The synaptic functions including long/short-term memory and learning-forgetting-relearning are demonstrated in this device and visual perception, visual memory, and color recognition functions are successfully implemented. Additionally, the flexible device exhibits excellent robustness and can realize imaging of light distribution under curved hemispheres similar to the human eye. Finally, through the simulation based on an artificial neural network algorithm, the device successfully realizes the high-precision recognition of handwritten digital images and possesses a strong fault tolerant capability even in bending states. These results are expected to drive the practical progress of metal halide for neuromorphic computing.
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Affiliation(s)
- Ying Li
- State Key Laboratory for Superlattices and MicrostructuresInstitute of Semiconductors, Chinese Academy of SciencesBeijing100083China
| | - Jiahui Wang
- Department of Chemistryand Laboratory of Nanomaterials for Energy ConversionUniversity of Science and Technology of ChinaHefei230026P. R. China
| | - Qing Yang
- Department of Chemistryand Laboratory of Nanomaterials for Energy ConversionUniversity of Science and Technology of ChinaHefei230026P. R. China
| | - Guozhen Shen
- State Key Laboratory for Superlattices and MicrostructuresInstitute of Semiconductors, Chinese Academy of SciencesBeijing100083China
- School of Integrated Circuits and ElectronicsBeijing Institute of TechnologyBeijing100081China
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25
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Yan X, He H, Liu G, Zhao Z, Pei Y, Liu P, Zhao J, Zhou Z, Wang K, Yan H. A Robust Memristor Based on Epitaxial Vertically Aligned Nanostructured BaTiO 3 -CeO 2 Films on Silicon. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2110343. [PMID: 35289446 DOI: 10.1002/adma.202110343] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 02/14/2022] [Indexed: 06/14/2023]
Abstract
With the exploration of ferroelectric materials, researchers have a strong desire to explore the next generation of non-volatile ferroelectric memory with silicon-based epitaxy, high-density storage, and algebraic operations. Herein, a silicon-based memristor with an epitaxial vertically aligned nanostructures BaTiO3 -CeO2 film based on La0.67 Sr0.33 MnO3 /SrTiO3 /Si substrate is reported. The ferroelectric polarization reversal is optimized through the continuous exploring of growth temperature, and the epitaxial structure is obtained, thus it improves the resistance characteristic, the multi-value storage function of five states is achieved, and the robust endurance characteristic can reach 109 cycles. In the synapse plasticity modulated by pulse voltage process, the function of the spiking-time-dependent plasticity and paired-pulse facilitation is simulated successfully. More importantly, the algebraic operations of addition, subtraction, multiplication, and division are realized by using fast speed pulse of the width ≈50 ns. Subsequently, a convolutional neural network is constructed for identifying the CIFAR-10 dataset, to simulate the performance of the device; the online and offline learning recognition rate reach 90.03% and 92.55%, respectively. Overall, this study paves the way for memristors with silicon-based epitaxial ferroelectric films to realize multi-value storage, algebraic operations, and neural computing chip applications.
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Affiliation(s)
- Xiaobing Yan
- Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Haidong He
- Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Gongjie Liu
- Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Zhen Zhao
- Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Yifei Pei
- Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Pan Liu
- Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Jianhui Zhao
- Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Zhenyu Zhou
- Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Kaiyang Wang
- Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
| | - Hongwei Yan
- Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding, 071002, P. R. China
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26
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Ma Z, Ge J, Chen W, Cao X, Diao S, Liu Z, Pan S. Reliable Memristor Based on Ultrathin Native Silicon Oxide. ACS APPLIED MATERIALS & INTERFACES 2022; 14:21207-21216. [PMID: 35476399 DOI: 10.1021/acsami.2c03266] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Memristors based on two-dimensional (2D) materials can exhibit great scalability and ultralow power consumption, yet the structural and thickness inhomogeneity of ultrathin electrolytes lowers the production yield and reliability of devices. Here, we report that the self-limiting amorphous SiOx (∼2.7 nm) provides a perfect atomically thin electrolyte with high uniformity, featuring a record high production yield. With the guidance of physical modeling, we reveal that the atomic thickness of SiOx enables anomalous resistive switching with a transition to an analog quasi-reset mode, where the filament stability can be further enhanced using Ag-Au nanocomposite electrodes. Such a picojoule memristor shows record low switching variabilities (C2C and D2D variation down to 1.1 and 2.6%, respectively), good retention at a few microsiemens, and high conductance-updating linearity, constituting key metrics for analog neural networks. In addition, the stable high-resistance state is found to be an excellent source for true random numbers of Gaussian distribution. This work opens up opportunities in mass production of Si-compatible memristors for ultradense neuromorphic and security hardware.
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Affiliation(s)
- Zelin Ma
- Research Center for Advanced Information Materials (CAIM), Huangpu Research & Graduate School of Guangzhou University, Guangzhou 510555, China
| | - Jun Ge
- Research Center for Advanced Information Materials (CAIM), Huangpu Research & Graduate School of Guangzhou University, Guangzhou 510555, China
- Solid State Physics & Material Research Laboratory, School of Physics and Material Science, Guangzhou University, Guangzhou 510006, China
| | - Wanjun Chen
- Solid State Physics & Material Research Laboratory, School of Physics and Material Science, Guangzhou University, Guangzhou 510006, China
| | - Xucheng Cao
- Research Center for Advanced Information Materials (CAIM), Huangpu Research & Graduate School of Guangzhou University, Guangzhou 510555, China
| | - Shanqing Diao
- Research Center for Advanced Information Materials (CAIM), Huangpu Research & Graduate School of Guangzhou University, Guangzhou 510555, China
| | - Zhiyu Liu
- Research Center for Advanced Information Materials (CAIM), Huangpu Research & Graduate School of Guangzhou University, Guangzhou 510555, China
- Solid State Physics & Material Research Laboratory, School of Physics and Material Science, Guangzhou University, Guangzhou 510006, China
| | - Shusheng Pan
- Research Center for Advanced Information Materials (CAIM), Huangpu Research & Graduate School of Guangzhou University, Guangzhou 510555, China
- Solid State Physics & Material Research Laboratory, School of Physics and Material Science, Guangzhou University, Guangzhou 510006, China
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27
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Kwon KC, Baek JH, Hong K, Kim SY, Jang HW. Memristive Devices Based on Two-Dimensional Transition Metal Chalcogenides for Neuromorphic Computing. NANO-MICRO LETTERS 2022; 14:58. [PMID: 35122527 PMCID: PMC8818077 DOI: 10.1007/s40820-021-00784-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 12/03/2021] [Indexed: 05/21/2023]
Abstract
Two-dimensional (2D) transition metal chalcogenides (TMC) and their heterostructures are appealing as building blocks in a wide range of electronic and optoelectronic devices, particularly futuristic memristive and synaptic devices for brain-inspired neuromorphic computing systems. The distinct properties such as high durability, electrical and optical tunability, clean surface, flexibility, and LEGO-staking capability enable simple fabrication with high integration density, energy-efficient operation, and high scalability. This review provides a thorough examination of high-performance memristors based on 2D TMCs for neuromorphic computing applications, including the promise of 2D TMC materials and heterostructures, as well as the state-of-the-art demonstration of memristive devices. The challenges and future prospects for the development of these emerging materials and devices are also discussed. The purpose of this review is to provide an outlook on the fabrication and characterization of neuromorphic memristors based on 2D TMCs.
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Affiliation(s)
- Ki Chang Kwon
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826 Republic of Korea
- Interdisciplinary Materials Measurement Institute, Korea Research Institute of Standards and Science (KRISS), Daejeon, 34133 Republic of Korea
| | - Ji Hyun Baek
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826 Republic of Korea
| | - Kootak Hong
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826 Republic of Korea
| | - Soo Young Kim
- Department of Materials Science and Engineering, Institute of Green Manufacturing Technology, Korea University, Seoul, 02841 Republic of Korea
| | - Ho Won Jang
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826 Republic of Korea
- Advanced Institute of Convergence Technology, Seoul National University, Suwon, 16229 Korea
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