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Meng Y, Cheng G. Human somatosensory systems based on sensor-memory-integrated technology. NANOSCALE 2024; 16:11928-11958. [PMID: 38847091 DOI: 10.1039/d3nr06521a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
As a representative artificial neural network (ANN) for incorporating sensing functions and memory functions into one system to achieve highly miniaturized and highly integrated devices or systems, artificial sensory systems (ASSs) can have a far-reaching influence on precise instrumentation, sensing, and automation engineering. Artificial sensory systems have enjoyed considerable progress in recent years, from low degree integrations to highly advanced sophisticated integrations, from single-modal perceptions to multimode-fused perceptions. However, there are issues around the large hardware area, power consumption, and communication bandwidth needed during the processes where multimodal sensing signals are converted into a digital mode before they can be processed by a digital processor. Therefore, deepening the research into sensory integration is of great importance. In this review, we briefly introduce fundamental knowledge about the memristor mechanism, describe some representative human somatosensory systems, and elucidate the relationship between the properties of memristor devices and the structure. The electronic character of the sensors, future prospects, and key challenges surrounding sensor-memory integrated technologies are also discussed.
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Affiliation(s)
- Yanfang Meng
- Institute of Intelligent Flexible Mechatronics, School of Mechanical Engineering, Jiangsu University, Zhenjiang, No. 301 Xuefu Road, Zhenjiang, Jiangsu Province, 212013, China.
| | - Guanggui Cheng
- Institute of Intelligent Flexible Mechatronics, School of Mechanical Engineering, Jiangsu University, Zhenjiang, No. 301 Xuefu Road, Zhenjiang, Jiangsu Province, 212013, China.
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2
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Xu J, Luo Z, Chen L, Zhou X, Zhang H, Zheng Y, Wei L. Recent advances in flexible memristors for advanced computing and sensing. MATERIALS HORIZONS 2024. [PMID: 38919028 DOI: 10.1039/d4mh00291a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
Conventional computing systems based on von Neumann architecture face challenges such as high power consumption and limited data processing capability. Improving device performance via scaling guided by Moore's Law becomes increasingly difficult. Emerging memristors can provide a promising solution for achieving high-performance computing systems with low power consumption. In particular, the development of flexible memristors is an important topic for wearable electronics, which can lead to intelligent systems in daily life with high computing capacity and efficiency. Here, recent advances in flexible memristors are reviewed, from operating mechanisms and typical materials to representative applications. Potential directions and challenges for future study in this area are also discussed.
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Affiliation(s)
- Jiaming Xu
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore.
| | - Ziwang Luo
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore.
| | - Long Chen
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore.
| | - Xuhui Zhou
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore.
| | - Haozhe Zhang
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore.
| | - Yuanjin Zheng
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore.
| | - Lei Wei
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore, Singapore.
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3
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Rokade KA, Kumbhar DD, Patil SL, Sutar SS, More KV, Dandge PB, Kamat RK, Dongale TD. CogniFiber: Harnessing Biocompatible and Biodegradable 1D Collagen Nanofibers for Sustainable Nonvolatile Memory and Synaptic Learning Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2312484. [PMID: 38501916 DOI: 10.1002/adma.202312484] [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: 11/21/2023] [Revised: 03/11/2024] [Indexed: 03/20/2024]
Abstract
Here, resistive switching (RS) devices are fabricated using naturally abundant, nontoxic, biocompatible, and biodegradable biomaterials. For this purpose, 1D chitosan nanofibers (NFs), collagen NFs, and chitosan-collagen NFs are synthesized by using an electrospinning technique. Among different NFs, the collagen-NFs-based device shows promising RS characteristics. In particular, the optimized Ag/collagen NFs/fluorine-doped tin oxide RS device shows a voltage-tunable analog memory behavior and good nonvolatile memory properties. Moreover, it can also mimic various biological synaptic learning properties and can be used for pattern classification applications with the help of the spiking neural network. The time series analysis technique is employed to model and predict the switching variations of the RS device. Moreover, the collagen NFs have shown good cytotoxicity and anticancer properties, suggesting excellent biocompatibility as a switching layer. The biocompatibility of collagen NFs is explored with the help of NRK-52E (Normal Rat Kidney cell line) and MCF-7 (Michigan Cancer Foundation-7 cancer cell line). Additionally, the biodegradability of the device is evaluated through a physical transient test. This work provides a vital step toward developing a biocompatible and biodegradable switching material for sustainable nonvolatile memory and neuromorphic computing applications.
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Affiliation(s)
- Kasturi A Rokade
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur, 416004, India
| | - Dhananjay D Kumbhar
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur, 416004, India
| | - Snehal L Patil
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur, 416004, India
| | - Santosh S Sutar
- Yashwantrao Chavan School of Rural Development, Shivaji University, Kolhapur, 416004, India
| | - Krantiveer V More
- Department of Chemistry, Shivaji University, Kolhapur, 416004, India
| | - Padma B Dandge
- Department of Biochemistry, Shivaji University, Kolhapur, 416004, India
| | - Rajanish K Kamat
- Department of Electronics, Shivaji University, Kolhapur, 416004, India
- The Institute of Science, Dr. Homi Bhabha State University, 15, Madam Cama Road, Mumbai, 400032, India
| | - Tukaram D Dongale
- Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur, 416004, India
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Yang C, Wang H, Cao Z, Chen X, Zhou G, Zhao H, Wu Z, Zhao Y, Sun B. Memristor-Based Bionic Tactile Devices: Opening the Door for Next-Generation Artificial Intelligence. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2308918. [PMID: 38149504 DOI: 10.1002/smll.202308918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/13/2023] [Indexed: 12/28/2023]
Abstract
Bioinspired tactile devices can effectively mimic and reproduce the functions of the human tactile system, presenting significant potential in the field of next-generation wearable electronics. In particular, memristor-based bionic tactile devices have attracted considerable attention due to their exceptional characteristics of high flexibility, low power consumption, and adaptability. These devices provide advanced wearability and high-precision tactile sensing capabilities, thus emerging as an important research area within bioinspired electronics. This paper delves into the integration of memristors with other sensing and controlling systems and offers a comprehensive analysis of the recent research advancements in memristor-based bionic tactile devices. These advancements incorporate artificial nociceptors and flexible electronic skin (e-skin) into the category of bio-inspired sensors equipped with capabilities for sensing, processing, and responding to stimuli, which are expected to catalyze revolutionary changes in human-computer interaction. Finally, this review discusses the challenges faced by memristor-based bionic tactile devices in terms of material selection, structural design, and sensor signal processing for the development of artificial intelligence. Additionally, it also outlines future research directions and application prospects of these devices, while proposing feasible solutions to address the identified challenges.
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Affiliation(s)
- Chuan Yang
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
| | - Hongyan Wang
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
| | - Zelin Cao
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Xiaoliang Chen
- 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
| | - Hongbin Zhao
- State Key Laboratory of Advanced Materials for Smart Sensing, General Research Institute for Nonferrous Metals, Beijing, 100088, China
| | - Zhenhua Wu
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 DongChuan Rd, Shanghai, 200240, China
| | - Yong Zhao
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
- Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fujian Normal University, Fuzhou, Fujian, 350117, China
| | - Bai Sun
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
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Ni Y, Liu J, Han H, Yu Q, Yang L, Xu Z, Jiang C, Liu L, Xu W. Visualized in-sensor computing. Nat Commun 2024; 15:3454. [PMID: 38658551 PMCID: PMC11043433 DOI: 10.1038/s41467-024-47630-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 04/08/2024] [Indexed: 04/26/2024] Open
Abstract
In artificial nervous systems, conductivity changes indicate synaptic weight updates, but they provide limited information compared to living organisms. We present the pioneering design and production of an electrochromic neuromorphic transistor employing color updates to represent synaptic weight for in-sensor computing. Here, we engineer a specialized mechanism for adaptively regulating ion doping through an ion-exchange membrane, enabling precise control over color-coded synaptic weight, an unprecedented achievement. The electrochromic neuromorphic transistor not only enhances electrochromatic capabilities for hardware coding but also establishes a visualized pattern-recognition network. Integrating the electrochromic neuromorphic transistor with an artificial whisker, we simulate a bionic reflex system inspired by the longicorn beetle, achieving real-time visualization of signal flow within the reflex arc in response to environmental stimuli. This research holds promise in extending the biomimetic coding paradigm and advancing the development of bio-hybrid interfaces, particularly in incorporating color-based expressions.
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Affiliation(s)
- Yao Ni
- Institute of Optoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, National Institute for Advanced Materials, Nankai University, Tianjin, 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen, 518000, China
| | - Jiaqi Liu
- Institute of Optoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, National Institute for Advanced Materials, Nankai University, Tianjin, 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen, 518000, China
| | - Hong Han
- Institute of Optoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, National Institute for Advanced Materials, Nankai University, Tianjin, 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen, 518000, China
| | - Qianbo Yu
- Institute of Optoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, National Institute for Advanced Materials, Nankai University, Tianjin, 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen, 518000, China
| | - Lu Yang
- Institute of Optoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, National Institute for Advanced Materials, Nankai University, Tianjin, 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen, 518000, China
| | - Zhipeng Xu
- Institute of Optoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, National Institute for Advanced Materials, Nankai University, Tianjin, 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen, 518000, China
| | - Chengpeng Jiang
- Institute of Optoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, National Institute for Advanced Materials, Nankai University, Tianjin, 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen, 518000, China
| | - Lu Liu
- Institute of Optoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, National Institute for Advanced Materials, Nankai University, Tianjin, 300350, China
- Shenzhen Research Institute of Nankai University, Shenzhen, 518000, China
| | - Wentao Xu
- Institute of Optoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, National Institute for Advanced Materials, Nankai University, Tianjin, 300350, China.
- Shenzhen Research Institute of Nankai University, Shenzhen, 518000, China.
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6
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Nazari S, Jamshidi S. Efficient digital design of the nonlinear behavior of Hindmarsh-Rose neuron model in large-scale neural population. Sci Rep 2024; 14:3833. [PMID: 38360852 PMCID: PMC10869816 DOI: 10.1038/s41598-024-54525-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 02/13/2024] [Indexed: 02/17/2024] Open
Abstract
Spiking networks, as the third generation of neural networks, are of great interest today due to their low power consumption in cognitive processes. This important characteristic has caused the hardware implementation techniques of spiking networks in the form of neuromorphic systems attract a lot of attention. For the first time, the focus is on the digital implementation based on CORDIC approximation of the Hindmarsh-Rose (HR) neuron so that the hardware implementation cost is lower than previous studies. If the digital design of a neuron is done efficient, the possibility of implementing a population of neurons is provided for the feasibility of low-consumption implementation of high-level cognitive processes in hardware, which is considered in this paper through edge detector, noise removal and image magnification spiking networks based on the proposed CORDIC_HR model. While using less hardware resources, the proposed HR neuron model follows the behavior of the original neuron model in the time domain with much less error than previous study. Also, the complex nonlinear behavior of the original and the proposed model of HR neuron through the bifurcation diagram, phase space and nullcline space analysis under different system parameters was investigated and the good follow-up of the proposed model was confirmed from the original model. In addition to the fact that the individual behavior of the original and the proposed neurons is the same, the functional and behavioral performance of the randomly connected neuronal population of original and proposed neuron model is equal. In general, the main contribution of the paper is in presenting an efficient hardware model, which consumes less hardware resources, follows the behavior of the original model with high accuracy, and has an acceptable performance in image processing applications such as noise removal and edge detection.
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Affiliation(s)
- Soheila Nazari
- Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran.
| | - Shabnam Jamshidi
- Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
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Lu C, Meng J, Yu J, Song J, Wang T, Zhu H, Sun QQ, Zhang DW, Chen L. Novel Three-Dimensional Artificial Neural Network Based on an Eight-Layer Vertical Memristor with an Ultrahigh Rectify Ratio (>10 7) and an Ultrahigh Nonlinearity (>10 5) for Neuromorphic Computing. NANO LETTERS 2024; 24:2018-2024. [PMID: 38315050 DOI: 10.1021/acs.nanolett.3c04577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
In recent years, memristors have successfully demonstrated their significant potential in artificial neural networks (ANNs) and neuromorphic computing. Nonetheless, ANNs constructed by crossbar arrays suffer from cross-talk issues and low integration densities. Here, we propose an eight-layer three-dimensional (3D) vertical crossbar memristor with an ultrahigh rectify ratio (RR > 107) and an ultrahigh nonlinearity (>105) to overcome these limitations, which enables it to reach a >1 Tb array size without reading failure. Furthermore, the proposed 3D RRAM shows advanced endurance (>1010 cycles), retention (>104 s), and uniformity. In addition, several synaptic functions observed in the human brain were mimicked. On the basis of the advanced performance, we constructed a novel 3D ANN, whose learning efficiency and recognition accuracy were enhanced significantly compared with those of conventional single-layer ANNs. These findings hold promise for the development of highly efficient, precise, integrated, and stable VLSI neuromorphic computing systems.
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Affiliation(s)
- Chen Lu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jialin Meng
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jiajie Yu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jieru Song
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Tianyu Wang
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Hao Zhu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Qing-Qing Sun
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - David Wei Zhang
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Lin Chen
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
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8
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Lu C, Meng J, Song J, Wang T, Zhu H, Sun QQ, Zhang DW, Chen L. Self-Rectifying All-Optical Modulated Optoelectronic Multistates Memristor Crossbar Array for Neuromorphic Computing. NANO LETTERS 2024; 24:1667-1672. [PMID: 38241735 DOI: 10.1021/acs.nanolett.3c04358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2024]
Abstract
Researching optoelectronic memristors capable of integrating sensory and processing functions is essential for advancing the development of efficient neuromorphic vision. Here, we experimentally demonstrated an all-optical controlled and self-rectifying optoelectronic memristor (OEM) crossbar array with the function of multilevel storage under light stimuli. The NiO/TiO2 device exhibits an ultrahigh (>104) rectifying ratio (RR) thus overcoming the presence of sneak current. The reversible conductance modulation without electric signal involvement provides a novel way to realize ultrafast information processing. The proposed OEM array realized synaptic functions observed in the human brain, including long-term potentiation (LTP), long-term depression (LTD), paired-pulse facilitation (PPF), the transition from short-term memory (STM) to long-term memory (LTM), and learning experience behaviors successfully. The authors present a novel OEM crossbar that possesses complete light-modulation capabilities, potentially advancing the future development of efficient neuromorphic vision.
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Affiliation(s)
- Chen Lu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jialin Meng
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jieru Song
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Tianyu Wang
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Hao Zhu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Qing-Qing Sun
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - David Wei Zhang
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Lin Chen
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
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9
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Wang S, Liu X, Yu H, Liu X, Zhao J, Hou L, Gao Y, Chen Z. Transfer-Free Analog and Digital Flexible Memristors Based on Boron Nitride Films. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:327. [PMID: 38392700 PMCID: PMC10893057 DOI: 10.3390/nano14040327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 02/03/2024] [Accepted: 02/05/2024] [Indexed: 02/24/2024]
Abstract
The traditional von Neumann architecture of computers, constrained by the inherent separation of processing and memory units, faces challenges, for instance, memory wall issue. Neuromorphic computing and in-memory computing offer promising paradigms to overcome the limitations of additional data movement and to enhance computational efficiency. In this work, transfer-free flexible memristors based on hexagonal boron nitride films were proposed for analog neuromorphic and digital memcomputing. Analog memristors were prepared; they exhibited synaptic behaviors, including paired-pulse facilitation and long-term potentiation/depression. The resistive switching mechanism of the analog memristors were investigated through transmission electron microscopy. Digital memristors were prepared by altering the electrode materials, and they exhibited reliable device performance, including a large on/off ratio (up to 106), reproducible switching endurance (>100 cycles), non-volatile characteristic (>60 min), and effective operating under bending conditions (>100 times).
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Affiliation(s)
- Sibo Wang
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, China
| | - Xiuhuan Liu
- College of Communication Engineering, Jilin University, Changchun 130012, China
| | - Han Yu
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, China
| | - Xiaohang Liu
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, China
| | - Jihong Zhao
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, China
| | - Lixin Hou
- College of Information Technology, Jilin Agricultural University, Changchun 130118, China
| | - Yanjun Gao
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, China
| | - Zhanguo Chen
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun 130012, China
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10
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Liu Y, Wang T, Xu K, Li Z, Yu J, Meng J, Zhu H, Sun Q, Zhang DW, Chen L. Low-power and high-speed HfLaO-based FE-TFTs for artificial synapse and reconfigurable logic applications. MATERIALS HORIZONS 2024; 11:490-498. [PMID: 37966103 DOI: 10.1039/d3mh01461d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
Emulating the human nervous system to build next-generation computing architectures is considered a promising way to solve the von Neumann bottleneck. Transistors based on ferroelectric layers are strong contenders for the basic unit of artificial neural systems due to their advantages of high speed and low power consumption. In this work, the potential of Fe-TFTs integrating the HfLaO ferroelectric film and ultra-thin ITO channel for artificial synaptic devices is demonstrated for the first time. The Fe-TFTs can respond significantly to pulses as low as 14 ns with an energy consumption of 93.1 aJ, which is at the leading level for similar devices. In addition, Fe-TFTs exhibit essential synaptic functions and achieve a recognition rate of 93.2% for handwritten digits. Notably, a novel reconfigurable approach involving the combination of two types of electrical pulses to realize Boolean logic operations ("AND", "OR") within a single Fe-TFT has been introduced for the first time. The simulations of array-level operations further demonstrated the potential for parallel computing. These multifunctional Fe-TFTs reveal new hardware options for neuromorphic computing chips.
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Affiliation(s)
- Yongkai Liu
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Tianyu Wang
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Kangli Xu
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Zhenhai Li
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jiajie Yu
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jialin Meng
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Hao Zhu
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Qingqing Sun
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - David Wei Zhang
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Lin Chen
- School of Microelectronics, Fudan University, State Key Laboratory of Integrated Chips and Systems, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
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11
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Guo Z, Zhang J, Yang B, Li L, Liu X, Xu Y, Wu Y, Guo P, Sun T, Dai S, Liang H, Wang J, Zou Y, Xiong L, Huang J. Organic High-Temperature Synaptic Phototransistors for Energy-Efficient Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023:e2310155. [PMID: 38100140 DOI: 10.1002/adma.202310155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 11/27/2023] [Indexed: 12/24/2023]
Abstract
Organic optoelectronic synaptic devices that can reliably operate in high-temperature environments (i.e., beyond 121°C) or remain stable after high-temperature treatments have significant potential in biomedical electronics and bionic robotic engineering. However, it is challenging to acquire this type of organic devices considering the thermal instability of conventional organic materials and the degradation of photoresponse mechanisms at high temperatures. Here, high-temperature synaptic phototransistors (HTSPs) based on thermally stable semiconductor polymer blends as the photosensitive layer are developed, successfully simulating fundamental optical-modulated synaptic characteristics at a wide operating temperature range from room temperature to 220°C. Robust optoelectronic performance can be observed in HTSPs even after experiencing 750 h of the double 85 testing due to the enhanced operational reliability. Using HTSPs, Morse-code optical decoding scheme and the visual object recognition capability are also verified at elevated temperatures. Furthermore, flexible HTSPs are fabricated, demonstrating an ultralow power consumption of 12.3 aJ per synaptic event at a low operating voltage of -0.05 mV. Overall, the conundrum of achieving reliable optical-modulated neuromorphic applications while balancing low power consumption can be effectively addressed. This research opens up a simple but effective avenue for the development of high-temperature and energy-efficient wearable optoelectronic devices in neuromorphic computing applications.
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Affiliation(s)
- Ziyi Guo
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Junyao Zhang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Ben Yang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Li Li
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Xu Liu
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yutong Xu
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yue Wu
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Pu Guo
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Tongrui Sun
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Shilei Dai
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Haixia Liang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Jun Wang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yidong Zou
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Lize Xiong
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai Fourth People's Hospital Affiliated to Tongji University, Tongji University, Shanghai, 200434, P. R. China
| | - Jia Huang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Shanghai Fourth People's Hospital Affiliated to Tongji University, Tongji University, Shanghai, 200434, P. R. China
- National Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai, 201804, P. R. China
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12
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Abstract
Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.
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Affiliation(s)
- Ke He
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Cong Wang
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Yongli He
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Jiangtao Su
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Xiaodong Chen
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore 636921, Singapore
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13
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Lee J, Yang K, Kwon JY, Kim JE, Han DI, Lee DH, Yoon JH, Park MH. Role of oxygen vacancies in ferroelectric or resistive switching hafnium oxide. NANO CONVERGENCE 2023; 10:55. [PMID: 38038784 PMCID: PMC10692067 DOI: 10.1186/s40580-023-00403-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 11/08/2023] [Indexed: 12/02/2023]
Abstract
HfO2 shows promise for emerging ferroelectric and resistive switching (RS) memory devices owing to its excellent electrical properties and compatibility with complementary metal oxide semiconductor technology based on mature fabrication processes such as atomic layer deposition. Oxygen vacancy (Vo), which is the most frequently observed intrinsic defect in HfO2-based films, determines the physical/electrical properties and device performance. Vo influences the polymorphism and the resulting ferroelectric properties of HfO2. Moreover, the switching speed and endurance of ferroelectric memories are strongly correlated to the Vo concentration and redistribution. They also strongly influence the device-to-device and cycle-to-cycle variability of integrated circuits based on ferroelectric memories. The concentration, migration, and agglomeration of Vo form the main mechanism behind the RS behavior observed in HfO2, suggesting that the device performance and reliability in terms of the operating voltage, switching speed, on/off ratio, analog conductance modulation, endurance, and retention are sensitive to Vo. Therefore, the mechanism of Vo formation and its effects on the chemical, physical, and electrical properties in ferroelectric and RS HfO2 should be understood. This study comprehensively reviews the literature on Vo in HfO2 from the formation and influencing mechanism to material properties and device performance. This review contributes to the synergetic advances of current knowledge and technology in emerging HfO2-based semiconductor devices.
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Affiliation(s)
- Jaewook Lee
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, College of Engineering, Seoul National University, Gwanak-Ro 1, Gwanak-Gu, Seoul, 08826, Republic of Korea
| | - Kun Yang
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, College of Engineering, Seoul National University, Gwanak-Ro 1, Gwanak-Gu, Seoul, 08826, Republic of Korea
| | - Ju Young Kwon
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
| | - Ji Eun Kim
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
| | - Dong In Han
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, College of Engineering, Seoul National University, Gwanak-Ro 1, Gwanak-Gu, Seoul, 08826, Republic of Korea
| | - Dong Hyun Lee
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, College of Engineering, Seoul National University, Gwanak-Ro 1, Gwanak-Gu, Seoul, 08826, Republic of Korea
| | - Jung Ho Yoon
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea.
| | - Min Hyuk Park
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, College of Engineering, Seoul National University, Gwanak-Ro 1, Gwanak-Gu, Seoul, 08826, Republic of Korea.
- Research Institute of Advanced Materials, Seoul National University, Gwanak-Ro 1, Gwanak-Gu, Seoul, 08826, Republic of Korea.
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14
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Li Z, Wang T, Meng J, Zhu H, Sun Q, Zhang DW, Chen L. Flexible aluminum-doped hafnium oxide ferroelectric synapse devices for neuromorphic computing. MATERIALS HORIZONS 2023; 10:3643-3650. [PMID: 37340846 DOI: 10.1039/d3mh00645j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
The HfO2-based ferroelectric tunnel junction has received outstanding attention owing to its high-speed and low-power characteristics. In this work, aluminum-doped HfO2 (HfAlO) ferroelectric thin films are deposited on a muscovite substrate (Mica). We investigate the bending effect on the ferroelectric characteristics of the Au/Ti/HfAlO/Pt/Ti/Mica device. After 1000 bending times, the ferroelectric properties and the fatigue characteristics are largely degraded. The finite element analysis indicates that crack formation is the main reason for the fatigue damage under threshold bending diameters. Moreover, the HfAlO-based ferroelectric synaptic device exhibits excellent performance of neuromorphic computing. The artificial synapse can mimic the paired-pulse facilitation and long-term potentiation/depression of biological synapses. Meanwhile, the accuracy of digit recognition is 88.8%. This research provides a new research idea for the further development of hafnium-based ferroelectric devices.
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Affiliation(s)
- Zhenhai Li
- School of Microelectronics, Fudan University, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Tianyu Wang
- School of Microelectronics, Fudan University, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jialin Meng
- School of Microelectronics, Fudan University, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Hao Zhu
- School of Microelectronics, Fudan University, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Qingqing Sun
- School of Microelectronics, Fudan University, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - David Wei Zhang
- School of Microelectronics, Fudan University, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Lin Chen
- School of Microelectronics, Fudan University, Shanghai 200433, P. R. China.
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
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15
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Li J, Gong M, Wang X, Fan F, Zhang B. Triphenylamine-Based Helical Polymer for Flexible Memristors. Biomimetics (Basel) 2023; 8:391. [PMID: 37754142 PMCID: PMC10526500 DOI: 10.3390/biomimetics8050391] [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: 07/26/2023] [Revised: 08/15/2023] [Accepted: 08/24/2023] [Indexed: 09/28/2023] Open
Abstract
Flexible nonvolatile memristors have potential applications in wearable devices. In this work, a helical polymer, poly (N, N-diphenylanline isocyanide) (PPIC), was synthesized as the active layer, and flexible electronic devices with an Al/PPIC/ITO architecture were prepared on a polyethylene terephthalate (PET) substrate. The device showed typical nonvolatile rewritable memristor characteristics. The high-molecular-weight helical structure stabilized the active layer under different bending degrees, bending times, and number of bending cycles. The memristor was further employed to simulate the information transmission capability of neural fibers, providing new perspectives for the development of flexible wearable memristors and biomimetic neural synapses. This demonstration highlights the promising possibilities for the advancement of artificial intelligence skin and intelligent flexible robots in the future.
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Affiliation(s)
- Jinyong Li
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Minglei Gong
- Shanghai i-Reader Biotech Co., Ltd., Shanghai 201100, China
| | - Xiaoyang Wang
- Guangxi Key Laboratory of Information Material, Engineering Research Center of Electronic Information Materials and Devices, School of Material Science and Engineering, Guilin University of Electronic Technology, Guilin 541200, China
| | - Fei Fan
- Shanghai i-Reader Biotech Co., Ltd., Shanghai 201100, China
| | - Bin Zhang
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
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16
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Huh W, Lee D, Jang S, Kang JH, Yoon TH, So JP, Kim YH, Kim JC, Park HG, Jeong HY, Wang G, Lee CH. Heterosynaptic MoS 2 Memtransistors Emulating Biological Neuromodulation for Energy-Efficient Neuromorphic Electronics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2211525. [PMID: 36930856 DOI: 10.1002/adma.202211525] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 03/04/2023] [Indexed: 06/16/2023]
Abstract
Heterosynaptic neuromodulation is a key enabler for energy-efficient and high-level biological neural processing. However, such manifold synaptic modulation cannot be emulated using conventional memristors and synaptic transistors. Thus, reported herein is a three-terminal heterosynaptic memtransistor using an intentional-defect-generated molybdenum disulfide channel. Particularly, the defect-mediated space-charge-limited conduction in the ultrathin channel results in memristive switching characteristics between the source and drain terminals, which are further modulated using a gate terminal according to the gate-tuned filling of trap states. The device acts as an artificial synapse controlled by sub-femtojoule impulses from both the source and gate terminals, consuming lower energy than its biological counterpart. In particular, electrostatic gate modulation, corresponding to biological neuromodulation, additionally regulates the dynamic range and tuning rate of the synaptic weight, independent of the programming (source) impulses. Notably, this heterosynaptic modulation not only improves the learning accuracy and efficiency but also reduces energy consumption in the pattern recognition. Thus, the study presents a new route leading toward the realization of highly networked and energy-efficient neuromorphic electronics.
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Affiliation(s)
- Woong Huh
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Donghun Lee
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Seonghoon Jang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jung Hoon Kang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Tae Hyun Yoon
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jae-Pil So
- Department of Physics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Yeon Ho Kim
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jong Chan Kim
- School of Materials Science and Engineering, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Ulsan, 44919, Republic of Korea
| | - Hong-Gyu Park
- Department of Physics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Hu Young Jeong
- UNIST Central Research Facilities (UCRF), Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Ulsan, 44919, Republic of Korea
| | - Gunuk Wang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Department of Integrative Energy Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology, 5 Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, Republic of Korea
| | - Chul-Ho Lee
- Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
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17
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Xiong C, Yang Z, Shen J, Tang F, He Q, Li Y, Xu M, Miao X. Nano t-Se Peninsulas Embedded in Natively Oxidized 2D TiSe 2 Enable Uniform and Fast Memristive Switching. ACS APPLIED MATERIALS & INTERFACES 2023; 15:23371-23379. [PMID: 37155833 DOI: 10.1021/acsami.3c00818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Memristive devices, regardless of their potential applications in memory and computing scenarios, still suffer from large cycle-to-cycle and device-to-device variations due to the stochastic growth of conductive filaments (CFs). In this work, we fabricated a crossbar memristor using the 2D TiSe2 material and then oxidized it into TiO2 in the atmosphere at a moderate temperature. Such a mild oxidation approach fails to evaporate all Se into the air, and after further annealing using thermal or electrical stimulations, the remnant Se atoms gather near the interfaces and grow into nanosized crystals with relatively high conductivity. The resulting peninsula-shaped nanocrystals distort the electric field, forcing CFs to grow on them, which could largely confine the location and length of CFs. As a result, this two-terminal TiSe2/TiO2/TiSe2 device exhibits excellent resistive switching performance with a fairly low threshold voltage (Vset < 0.8 V, Vreset > 0.55 V) and high cycle-to-cycle consistency, enabling resistive switching at narrow operating variations, e.g., 500 ± 48 and 845 ± 39 mV. Our work offers a new approach to minimize the cycle-to-cycle stochasticity of the memristive device, paving the way for its applications in data storage and brain-inspired computing.
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Affiliation(s)
- Changying Xiong
- Wuhan National Laboratory for Optoelectronics, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Zhe Yang
- Wuhan National Laboratory for Optoelectronics, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jiahao Shen
- Wuhan National Laboratory for Optoelectronics, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Feiyu Tang
- Wuhan National Laboratory for Optoelectronics, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Qiang He
- Wuhan National Laboratory for Optoelectronics, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan 430205, China
| | - Yi Li
- Wuhan National Laboratory for Optoelectronics, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan 430205, China
| | - Ming Xu
- Wuhan National Laboratory for Optoelectronics, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan 430205, China
| | - Xiangshui Miao
- Wuhan National Laboratory for Optoelectronics, School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan 430205, China
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18
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Zeng T, Wang Z, Lin Y, Cheng Y, Shan X, Tao Y, Zhao X, Xu H, Liu Y. Doppler Frequency-Shift Information Processing in WO x -Based Memristive Synapse for Auditory Motion Perception. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2300030. [PMID: 36862024 PMCID: PMC10161103 DOI: 10.1002/advs.202300030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/10/2023] [Indexed: 05/06/2023]
Abstract
Auditory motion perception is one crucial capability to decode and discriminate the spatiotemporal information for neuromorphic auditory systems. Doppler frequency-shift feature and interaural time difference (ITD) are two fundamental cues of auditory information processing. In this work, the functions of azimuth detection and velocity detection, as the typical auditory motion perception, are demonstrated in a WOx -based memristive synapse. The WOx memristor presents both the volatile mode (M1) and semi-nonvolatile mode (M2), which are capable of implementing the high-pass filtering and processing the spike trains with a relative timing and frequency shift. In particular, the Doppler frequency-shift information processing for velocity detection is emulated in the WOx memristor based auditory system for the first time, which relies on a scheme of triplet spike-timing-dependent-plasticity in the memristor. These results provide new opportunities for the mimicry of auditory motion perception and enable the auditory sensory system to be applied in future neuromorphic sensing.
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Affiliation(s)
- Tao Zeng
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Zhongqiang Wang
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Ya Lin
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - YanKun Cheng
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Xuanyu Shan
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Ye Tao
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Xiaoning Zhao
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Haiyang Xu
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
| | - Yichun Liu
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education, 5268 Renmin Street, Changchun, 130024, P. R. China
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19
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Wang K, Hou C, Cong L, Zhang W, Fan L, Wang X, Dong L. 3D Chiral Micro-Pinwheels Based on Rolling-Up Kirigami Technology. SMALL METHODS 2023:e2201627. [PMID: 37075739 DOI: 10.1002/smtd.202201627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/23/2023] [Indexed: 05/03/2023]
Abstract
Expanding micro-/nanostructures into 3D ones results not only in boosting structural integration level with compact geometry but also enhancing a device's complexity and functionality. Herein, a synergetic 3D micro-/nanoshape transformation is proposed by combining kirigami and rolling-up techniques, or rolling-up kirigami, for the first time. As an example, micro-pinwheels with multiple flabella are patterned on pre-stressed bilayer membranes and rolled up into 3D structures. The flabella are designed when they are patterned on a 2D thin film, facilitating the integration of micro-/nanoelement and other functionalization processes during 2D patterning, which is typically much easier than post-shaping an as-fabricated 3D structure by removing redundant materials or 3D printing. The dynamic rolling-up process is simulated using elastic mechanics with a movable releasing boundary. Mutual competition and cooperation among flabella are observed during the whole release process. More importantly, the mutual conversion between translation and rotation offers a reliable platform for developing parallel microrobots and adaptive 3D micro-antennas. Additionally, 3D chiral micro-pinwheel arrays integrated into a microfluidic chip are successfully applied to detect organic molecules in solution using a terahertz apparatus. With an extra actuation, active micro-pinwheels can potentially serve as a base to functionalize 3D kirigami as tunable devices.
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Affiliation(s)
- Kun Wang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, 999077, China
| | - Chaojian Hou
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, 999077, China
| | - Longqing Cong
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China
| | - Wenqi Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, 999077, China
| | - Lu Fan
- Shenzhen Key Laboratory of Marine Archaea Geo-Omics, Department of Ocean Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, Guangdong, 518055, China
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, Guangdong, 511458, China
| | - Xiaokai Wang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, 999077, China
| | - Lixin Dong
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, 999077, China
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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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Zhu S, Sun B, Zhou G, Guo T, Ke C, Chen Y, Yang F, Zhang Y, Shao J, Zhao Y. In-Depth Physical Mechanism Analysis and Wearable Applications of HfO x-Based Flexible Memristors. ACS APPLIED MATERIALS & INTERFACES 2023; 15:5420-5431. [PMID: 36688622 DOI: 10.1021/acsami.2c16569] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Since memristors as an emerging nonlinear electronic component have been considered the most promising candidate for integrating nonvolatile memory and advanced computing technology, the in-depth reveal of the memristive mechanism and the realization of hardware fabrication have facilitated their wide applications in next-generation artificial intelligence. Flexible memristors have shown great promising prospects in wearable electronics and artificial electronic skin (e-skin), but in-depth research on the physical mechanism is still lacking. Here, a flexible memristive device with a Ag/HfOx/Ti/PET crossbar structure was fabricated, and a remarkable analog switching characteristic similar to synaptic behavior was observed. Through detailed data fitting and in-depth physical mechanism analysis, it is confirmed that the analog switching characteristics of the device are mainly caused by carrier tunneling. Furthermore, the memristive properties of the Ag/HfOx/Ag/PET device can be attributed to the conductive filaments formed by the redox reaction of the active metal Ag. Finally, the interfacial barrier is extracted by the Arrhenius diagram and the energy band diagram, which is drawn to clearly demonstrate the conduction mechanism of charge trapping in the device. Therefore, the HfOx-based flexible memristor with analog switching behavior and stable memory performance lays the foundation for cutting-edge applications in wearable electronics and smart e-skin.
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Affiliation(s)
- Shouhui Zhu
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan610031, China
- Superconductivity and New Energy R&D Center, Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, Southwest Jiaotong University, Chengdu610031, China
| | - Bai Sun
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi710049, China
| | - Guangdong Zhou
- College of Artificial Intelligence, Brain-inspired Computing & Intelligent Control of Chongqing Key Lab, Southwest University, Chongqing400715, China
| | - Tao Guo
- Department of Mechanical and Mechatronics Engineering, Waterloo Institute for Nanotechnology, University of Waterloo, Waterloo, OntarioN2L 3G1, Canada
| | - Chuan Ke
- Superconductivity and New Energy R&D Center, Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, Southwest Jiaotong University, Chengdu610031, China
| | - Yuanzheng Chen
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan610031, China
| | - Feng Yang
- Superconductivity and New Energy R&D Center, Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, Southwest Jiaotong University, Chengdu610031, China
| | - Yong Zhang
- Superconductivity and New Energy R&D Center, Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, Southwest Jiaotong University, Chengdu610031, China
| | - Jinyou Shao
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi710049, China
| | - Yong Zhao
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan610031, China
- Superconductivity and New Energy R&D Center, Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, Southwest Jiaotong University, Chengdu610031, China
- Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering, Fujian Normal University, Fuzhou, Fujian350117, China
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22
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High-speed metamagnetic switching of FeRh through Joule heating. Sci Rep 2022; 12:22061. [PMID: 36543817 PMCID: PMC9772412 DOI: 10.1038/s41598-022-26587-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
Due to its proximity to room temperature and demonstrated high degree of temperature tunability, FeRh's metamagnetic ordering transition is attractive for novel high-performance computing devices seeking to use magnetism as the state variable. We demonstrate electrical control of the antiferromagnetic-to-ferromagnetic transition via Joule heating in FeRh wires. The magnetic transition of FeRh is accompanied by a change in resistivity, which can be probed electrically and allows for integration into switching devices. Finite element simulations based on abrupt state transition within each domain result in a globally smooth transition that agrees with the experimental findings and provides insight into the thermodynamics involved. We measure a 150 K decrease in transition temperature with currents up to 60 mA, limited only by the dimensions of the device. The sizeable shift in transition temperature scales with current density and wire length, suggesting the absolute resistance and heat dissipation of the substrate are also important. The FeRh phase change is evaluated by pulsed I-V using a variety of bias conditions. We demonstrate high speed (~ ns) memristor-like behavior and report device performance parameters such as switching speed and power consumption that compare favorably with state-of-the-art phase change memristive technologies.
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23
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Wang T, Meng J, Zhou X, Liu Y, He Z, Han Q, Li Q, Yu J, Li Z, Liu Y, Zhu H, Sun Q, Zhang DW, Chen P, Peng H, Chen L. Reconfigurable neuromorphic memristor network for ultralow-power smart textile electronics. Nat Commun 2022; 13:7432. [PMID: 36460675 PMCID: PMC9718838 DOI: 10.1038/s41467-022-35160-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 11/21/2022] [Indexed: 12/04/2022] Open
Abstract
Neuromorphic computing memristors are attractive to construct low-power- consumption electronic textiles due to the intrinsic interwoven architecture and promising applications in wearable electronics. Developing reconfigurable fiber-based memristors is an efficient method to realize electronic textiles that capable of neuromorphic computing function. However, the previously reported artificial synapse and neuron need different materials and configurations, making it difficult to realize multiple functions in a single device. Herein, a textile memristor network of Ag/MoS2/HfAlOx/carbon nanotube with reconfigurable characteristics was reported, which can achieve both nonvolatile synaptic plasticity and volatile neuron functions. In addition, a single reconfigurable memristor can realize integrate-and-fire function, exhibiting significant advantages in reducing the complexity of neuron circuits. The firing energy consumption of fiber-based memristive neuron is 1.9 fJ/spike (femtojoule-level), which is at least three orders of magnitude lower than that of the reported biological and artificial neuron (picojoule-level). The ultralow energy consumption makes it possible to create an electronic neural network that reduces the energy consumption compared to human brain. By integrating the reconfigurable synapse, neuron and heating resistor, a smart textile system is successfully constructed for warm fabric application, providing a unique functional reconfiguration pathway toward the next-generation in-memory computing textile system.
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Affiliation(s)
- Tianyu Wang
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Jialin Meng
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Xufeng Zhou
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, 200438 Shanghai, China
| | - Yue Liu
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, 200438 Shanghai, China
| | - Zhenyu He
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Qi Han
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Qingxuan Li
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Jiajie Yu
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Zhenhai Li
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Yongkai Liu
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Hao Zhu
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Qingqing Sun
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - David Wei Zhang
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Peining Chen
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, 200438 Shanghai, China
| | - Huisheng Peng
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, 200438 Shanghai, China
| | - Lin Chen
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
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24
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Wu F, Chou CH, Tseng TY. CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing. NANOSCALE RESEARCH LETTERS 2022; 17:105. [PMID: 36342556 PMCID: PMC9640510 DOI: 10.1186/s11671-022-03744-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
Optoelectronic memristor is a promising candidate for future light-controllable high-density storage and neuromorphic computing. In this work, light-tunable resistive switching (RS) characteristics are demonstrated in the CMOS process-compatible ITO/HfO2/TiO2/ITO optoelectronic memristor. The device shows an average of 79.24% transmittance under visible light. After electroforming, stable bipolar analog switching, data retention beyond 104 s, and endurance of 106 cycles are realized. An obvious current increase is observed under 405 nm wavelength light irradiation both in high and in low resistance states. The long-term potentiation of synaptic property can be achieved by both electrical and optical stimulation. Moreover, based on the optical potentiation and electrical depression of conductances, the simulated Hopfield neural network (HNN) is trained for learning the 10 × 10 pixels size image. The HNN can be successfully trained to recognize the input image with a training accuracy of 100% in 13 iterations. These results suggest that this optoelectronic memristor has a high potential for neuromorphic application.
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Affiliation(s)
- Facai Wu
- Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan
| | - Chien-Hung Chou
- Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan
| | - Tseung-Yuen Tseng
- Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan.
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25
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Cho SW, Jo C, Kim YH, Park SK. Progress of Materials and Devices for Neuromorphic Vision Sensors. NANO-MICRO LETTERS 2022; 14:203. [PMID: 36242681 PMCID: PMC9569410 DOI: 10.1007/s40820-022-00945-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/08/2022] [Indexed: 05/31/2023]
Abstract
The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords: smaller, faster, and smarter. (1) Smaller: Devices are becoming more compact by integrating previously separated components such as sensors, memory, and processing units. As a prime example, the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits, such as simpler circuitry, lower power consumption, and less data redundancy. (2) Swifter: Owing to the nature of physics, smaller and more integrated devices can detect, process, and react to input more quickly. In addition, the methods for sensing and processing optical information using various materials (such as oxide semiconductors) are evolving. (3) Smarter: Owing to these two main research directions, we can expect advanced applications such as adaptive vision sensors, collision sensors, and nociceptive sensors. This review mainly focuses on the recent progress, working mechanisms, image pre-processing techniques, and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.
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Affiliation(s)
- Sung Woon Cho
- Department of Advanced Components and Materials Engineering, Sunchon National University, Sunchŏn, Jeonnam, 57922, Republic of Korea
| | - Chanho Jo
- Department of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea
| | - Yong-Hoon Kim
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, Republic of Korea.
| | - Sung Kyu Park
- Department of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea.
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26
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Kim M, Rehman MA, Lee D, Wang Y, Lim DH, Khan MF, Choi H, Shao QY, Suh J, Lee HS, Park HH. Filamentary and Interface-Type Memristors Based on Tantalum Oxide for Energy-Efficient Neuromorphic Hardware. ACS APPLIED MATERIALS & INTERFACES 2022; 14:44561-44571. [PMID: 36164762 DOI: 10.1021/acsami.2c12296] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
To implement artificial neural networks (ANNs) based on memristor devices, it is essential to secure the linearity and symmetry in weight update characteristics of the memristor, and reliability in the cycle-to-cycle and device-to-device variations. This study experimentally demonstrated and compared the filamentary and interface-type resistive switching (RS) behaviors of tantalum oxide (Ta2O5 and TaO2)-based devices grown by atomic layer deposition (ALD) to propose a suitable RS type in terms of reliability and weight update characteristics. Although Ta2O5 is a strong candidate for memristor, the filament-type RS behavior of Ta2O5 does not fit well with ANNs demanding analog memory characteristics. Therefore, this study newly designed an interface-type TaO2 memristor and compared it to a filament type of Ta2O5 memristor to secure the weight update characteristics and reliability. The TaO2-based interface-type memristor exhibited gradual RS characteristics and area dependency in both high- and low-resistance states. In addition, compared to the filamentary memristor, the RS behaviors of the TaO2-based interface-type device exhibited higher suitability for the neuromorphic, symmetric, and linear long-term potentiation (LTP) and long-term depression (LTD). These findings suggest better types of memristors for implementing ionic memristor-based ANNs among the two types of RS mechanisms.
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Affiliation(s)
- Minjae Kim
- Department of Materials Science and Engineering, Yonsei University, Seoul 03722, South Korea
| | - Malik Abdul Rehman
- Department of Materials Science and Engineering, Yonsei University, Seoul 03722, South Korea
| | - Donghyun Lee
- Department of Materials Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
| | - Yue Wang
- Department of Materials Science and Engineering, Yonsei University, Seoul 03722, South Korea
| | - Dong-Hyeok Lim
- Department of Materials Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
| | - Muhammad Farooq Khan
- Department of Electrical Engineering, Sejong University, Seoul 05006, South Korea
| | - Haryeong Choi
- Department of Materials Science and Engineering, Yonsei University, Seoul 03722, South Korea
| | - Qing Yi Shao
- Provincial Key Laboratory of Nuclear Science, Institute of Quantum Matter, South China Normal University, Guangzhou 510006, China
| | - Joonki Suh
- Department of Materials Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
| | - Hong-Sub Lee
- Department of Advanced Materials Engineering for Information and Electronics, Kyung Hee University, Yongin, Gyeonggi-do 17104, Korea
| | - Hyung-Ho Park
- Department of Materials Science and Engineering, Yonsei University, Seoul 03722, South Korea
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27
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Zhu Y, Liang JS, Shi X, Zhang Z. Full-Inorganic Flexible Ag 2S Memristor with Interface Resistance-Switching for Energy-Efficient Computing. ACS APPLIED MATERIALS & INTERFACES 2022; 14:43482-43489. [PMID: 36102604 PMCID: PMC9523614 DOI: 10.1021/acsami.2c11183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 09/03/2022] [Indexed: 06/01/2023]
Abstract
Flexible memristor-based neural network hardware is capable of implementing parallel computation within the memory units, thus holding great promise for fast and energy-efficient neuromorphic computing in flexible electronics. However, the current flexible memristor (FM) is mostly operated with a filamentary mechanism, which demands large energy consumption in both setting and computing. Herein, we report an Ag2S-based FM working with distinct interface resistance-switching (RS) mechanism. In direct contrast to conventional filamentary memristors, RS in this Ag2S device is facilitated by the space charge-induced Schottky barrier modification at the Ag/Ag2S interface, which can be achieved with the setting voltage below the threshold voltage required for filament formation. The memristor based on interface RS exhibits 105 endurance cycles and 104 s retention under bending condition, and multiple level conductive states with exceptional tunability and stability. Since interface RS does not require the formation of a continuous Ag filament via Ag+ ion reduction, it can achieve an ultralow switching energy of ∼0.2 fJ. Furthermore, a hardware-based image processing with a software-comparable computing accuracy is demonstrated using the flexible Ag2S memristor array. And the image processing with interface RS indeed consumes 2 orders of magnitude lower power than that with filamentary RS on the same hardware. This study demonstrates a new resistance-switching mechanism for energy-efficient flexible neural network hardware.
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Affiliation(s)
- Yuan Zhu
- Division
of Solid-State Electronics, Department of Electrical Engineering, Uppsala University, Uppsala 75121, Sweden
| | - Jia-sheng Liang
- State
Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of
Sciences, Shanghai 200050, China
| | - Xun Shi
- State
Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of
Sciences, Shanghai 200050, China
| | - Zhen Zhang
- Division
of Solid-State Electronics, Department of Electrical Engineering, Uppsala University, Uppsala 75121, Sweden
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28
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Wu HY, Shi X, Zhou ZH, Liu Y, Cheng XR, Yang YB, Kang XY, Guo Y, Zeng KW, Wang BJ, Sun XM, Chen PN, Peng HS. Seamlessly-integrated Textile Electric Circuit Enabled by Self-connecting Interwoven Points. CHINESE JOURNAL OF POLYMER SCIENCE 2022. [DOI: 10.1007/s10118-022-2829-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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29
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Barman A, Das D, Deshmukh S, Sarkar PK, Banerjee D, Hübner R, Gupta M, Saini CP, Kumar S, Johari P, Dhar S, Kanjilal A. Aliovalent Ta-Doping-Engineered Oxygen Vacancy Configurations for Ultralow-Voltage Resistive Memory Devices: A DFT-Supported Experimental Study. ACS APPLIED MATERIALS & INTERFACES 2022; 14:34822-34834. [PMID: 35866235 DOI: 10.1021/acsami.2c05089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Alteration of transport properties of any material, especially metal oxides, by doping suitable impurities is not straightforward as it may introduce multiple defects like oxygen vacancies (Vo) in the system. It plays a decisive role in controlling the resistive switching (RS) performance of metal oxide-based memory devices. Therefore, a judicious choice of dopants and their atomic concentrations is crucial for achieving an optimum Vo configuration. Here, we show that the rational designing of RS memory devices with cationic dopants (Ta), in particular, Au/Ti1-xTaxO2-δ/Pt devices, is promising for the upcoming non-volatile memory technology. Indeed, a current window of ∼104 is realized at an ultralow voltage as low as 0.25 V with significant retention (∼104 s) and endurance (∼105 cycles) of the device by considering 1.11 at % Ta doping. The obtained device parameters are compared with those in the available literature to establish its excellent performance. Furthermore, using detailed experimental analyses and density functional theory (DFT)-based first-principles calculations, we comprehend that the meticulous presence of Vo configurations and the columnar-like dendritic structures is crucial for achieving ultralow-voltage bipolar RS characteristics. In fact, the dopant-mediated Vo interactions are found to be responsible for the enhancement in local current conduction, as evidenced from the DFT-simulated electron localization function plots, and these, in turn, augment the device performance. Overall, the present study on cationic-dopant-controlled defect engineering could pave a neoteric direction for future energy-efficient oxide memristors.
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Affiliation(s)
- Arabinda Barman
- Department of Physics, School of Natural Sciences, Shiv Nadar University, NH-91, Dadri, Gautam Buddha Nagar, Greater Noida, Uttar Pradesh 201 314, India
- Department of Physics, Dinhata College, Dinhata, West Bengal 736 135, India
| | - Dip Das
- Department of Physics, School of Natural Sciences, Shiv Nadar University, NH-91, Dadri, Gautam Buddha Nagar, Greater Noida, Uttar Pradesh 201 314, India
| | - Sujit Deshmukh
- Department of Physics, School of Natural Sciences, Shiv Nadar University, NH-91, Dadri, Gautam Buddha Nagar, Greater Noida, Uttar Pradesh 201 314, India
| | - Pranab Kumar Sarkar
- Department of Applied Sciences and Humanities, Assam University, Silchar, Assam 788 011, India
| | - Debosmita Banerjee
- Department of Physics, School of Natural Sciences, Shiv Nadar University, NH-91, Dadri, Gautam Buddha Nagar, Greater Noida, Uttar Pradesh 201 314, India
| | - René Hübner
- Institute of Ion Beam Physics and Materials Research, Helmholtz-Zentrum Dresden-Rossendorf, Dersden 01328, Germany
| | - Mukul Gupta
- UGC-DAE Consortium for Scientific Research, Khandwa Road, Indore, Madhya Pradesh 452 001, India
| | | | - Shammi Kumar
- Department of Physics, School of Natural Sciences, Shiv Nadar University, NH-91, Dadri, Gautam Buddha Nagar, Greater Noida, Uttar Pradesh 201 314, India
| | - Priya Johari
- Department of Physics, School of Natural Sciences, Shiv Nadar University, NH-91, Dadri, Gautam Buddha Nagar, Greater Noida, Uttar Pradesh 201 314, India
| | - Sankar Dhar
- Department of Physics, School of Natural Sciences, Shiv Nadar University, NH-91, Dadri, Gautam Buddha Nagar, Greater Noida, Uttar Pradesh 201 314, India
| | - Aloke Kanjilal
- Department of Physics, School of Natural Sciences, Shiv Nadar University, NH-91, Dadri, Gautam Buddha Nagar, Greater Noida, Uttar Pradesh 201 314, India
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30
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Shu H, Long H, Sun H, Li B, Zhang H, Wang X. Dynamic Model of the Short-Term Synaptic Behaviors of PEDOT-based Organic Electrochemical Transistors with Modified Shockley Equations. ACS OMEGA 2022; 7:14622-14629. [PMID: 35557652 PMCID: PMC9088794 DOI: 10.1021/acsomega.1c06864] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 04/07/2022] [Indexed: 06/15/2023]
Abstract
Neuromorphic computing is an emerging area with prospects to break the energy efficiency bottleneck of artificial intelligence (AI). A crucial challenge for neuromorphic computing is understanding the working principles of artificial synaptic devices. As an emerging class of synaptic devices, organic electrochemical transistors (OECTs) have attracted significant interest due to ultralow voltage operation, analog conductance tuning, mechanical flexibility, and biocompatibility. However, little work has been focused on the first-principal modeling of the synaptic behaviors of OECTs. The simulation of OECT synaptic behaviors is of great importance to understanding the OECT working principles as neuromorphic devices and optimizing ultralow power consumption neuromorphic computing devices. Here, we develop a two-dimensional transient drift-diffusion model based on modified Shockley equations for poly(3,4-ethylenedioxythiophene) (PEDOT)-based OECTs. We reproduced the typical transistor characteristics of these OECTs including the unique non-monotonic transconductance-gate bias curve and frequency dependency of transconductance. Furthermore, typical synaptic phenomena, such as excitatory/inhibitory postsynaptic current (EPSC/IPSC), paired-pulse facilitation/depression (PPF/PPD), and short-term plasticity (STP), are also demonstrated. This work is crucial in guiding the experimental exploration of neuromorphic computing devices and has the potential to serve as a platform for future OECT device simulation based on a wide range of semiconducting materials.
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Affiliation(s)
- Haonian Shu
- Department
of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruff Ave, Columbus, Ohio 43210, United States
| | - Haowei Long
- School
of Materials Science and Engineering, Zhejiang
University, Hangzhou, Zhejiang 310027, P. R. China
| | - Haibin Sun
- Department
of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruff Ave, Columbus, Ohio 43210, United States
| | - Baochen Li
- Department
of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruff Ave, Columbus, Ohio 43210, United States
| | - Haomiao Zhang
- State
Key Laboratory of Chemical Engineering, College of Chemical and Biological
Engineering, Zhejiang University, Hangzhou 310027, P. R. China
| | - Xiaoxue Wang
- Department
of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruff Ave, Columbus, Ohio 43210, United States
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31
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Wang H, Jiang S, Hao Z, Xu X, Pei M, Guo J, Wang Q, Li Y, Chen J, Xu J, Wang X, Wang J, Shi Y, Li Y. Molecular-Layer-Defined Asymmetric Schottky Contacts in Organic Planar Diodes for Self-Powered Optoelectronic Synapses. J Phys Chem Lett 2022; 13:2338-2347. [PMID: 35254069 DOI: 10.1021/acs.jpclett.2c00176] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Optoelectronic synapses have been utilized as neuromorphic vision sensors for image preprocessing in artificial visual systems. Self-powered optoelectronic synapses, which can directly convert optical power into electrical power, are promising for practical applications. The Schottky junction tends to be a promising candidate as the energy source for electrical operations. However, fully utilizing the potential of Schottky barriers is still challenging. Herein, organic self-powered optoelectronic synapses with planar diode architecture are fabricated, which can simultaneously sense and process ultraviolet (UV) signals. The photovoltaic operations are facilitated by the built-in potential originating from the molecular-layer-defined asymmetric Schottky contacts. Diverse synaptic behaviors under UV light stimulation without external power supplies are facilitated by the interfacial carrier-capturing layer, which emulates the membranes of synapses. Furthermore, retina-inspired image preprocessing functions are demonstrated on the basis of synaptic plasticity. Therefore, our devices provide the potential for the development of power-efficient and advanced artificial visual systems.
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Affiliation(s)
- Hengyuan Wang
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Sai Jiang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, P.R. China
| | - Ziqian Hao
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Xin Xu
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Mengjiao Pei
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Jianhang Guo
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Qijing Wang
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Yating Li
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Jiaming Chen
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Jun Xu
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Xinran Wang
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Junzhuan Wang
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Yi Shi
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
| | - Yun Li
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, P. R. China
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Kingra SK, Parmar V, Suri M. In-Memory Computation Based Mapping of Keccak-f Hash Function. FRONTIERS IN NANOTECHNOLOGY 2022. [DOI: 10.3389/fnano.2022.841756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Cryptographic hash functions play a central role in data security for applications such as message authentication, data verification, and detecting malicious or illegal modification of data. However, such functions typically require intensive computations with high volume of memory accesses. Novel computing architectures such as logic-in-memory (LIM)/in-memory computing (IMC) have been investigated in the literature to address the limitations of intense compute and memory bottleneck. In this work, we present an implementation of Keccak-f (a state-of-the-art secure hash algorithm) using a variant of simultaneous logic-in-memory (SLIM) that utilizes emerging non-volatile memory (NVM) devices. Detailed operation and instruction mapping on SLIM-based digital gates is presented. Through simulations, we benchmark the proposed approach using LIM cells based on four different emerging NVM devices (OxRAM, CBRAM, PCM, and FeRAM). The proposed mapping strategy when used with state-of-the-art emerging NVM devices offers EDP savings of up to 300× compared to conventional methods.
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Artificial Neurons and Synapses Based on Al/a-SiNxOy:H/P+-Si Device with Tunable Resistive Switching from Threshold to Memory. NANOMATERIALS 2022; 12:nano12030311. [PMID: 35159656 PMCID: PMC8839940 DOI: 10.3390/nano12030311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/12/2022] [Accepted: 01/14/2022] [Indexed: 01/09/2023]
Abstract
As the building block of brain-inspired computing, resistive switching memory devices have recently attracted great interest due to their biological function to mimic synapses and neurons, which displays the memory switching or threshold switching characteristic. To make it possible for the Si-based artificial neurons and synapse to be integrated with the neuromorphic chip, the tunable threshold and memory switching characteristic is highly in demand for their perfect compatibility with the mature CMOS technology. We first report artificial neurons and synapses based on the Al/a-SiNxOy:H/P+-Si device with the tunable switching from threshold to memory can be realized by controlling the compliance current. It is found that volatile TS from Al/a-SiNxOy:H/P+-Si device under the lower compliance current is induced by the weak Si dangling bond conductive pathway, which originates from the broken Si-H bonds. While stable nonvolatile MS under the higher compliance current is attributed to the strong Si dangling bond conductive pathway, which is formed by the broken Si-H and Si-O bonds. Theoretical calculation reveals that the conduction mechanism of TS and MS agree with P-F model, space charge limited current model and Ohm’s law, respectively. The tunable TS and MS characteristic of Al/a-SiNxOy:H/P+-Si device can be successfully employed to mimic the biological behavior of neurons and synapse including the integrate-and-fire function, paired-pulse facilitation, long-term potentiation and long-term depression as well as spike-timing-dependent plasticity. Our discovery supplies an effective way to construct the neuromorphic devices for brain-inspired computing in the AI period.
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Liu X, Cao J, Qiu J, Zhang X, Wang M, Liu Q. Flexible and Stretchable Memristive Arrays for in-Memory Computing. FRONTIERS IN NANOTECHNOLOGY 2022. [DOI: 10.3389/fnano.2021.821687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
With the tremendous progress of Internet of Things (IoT) and artificial intelligence (AI) technologies, the demand for flexible and stretchable electronic systems is rapidly increasing. As the vital component of a system, existing computing units are usually rigid and brittle, which are incompatible with flexible and stretchable electronics. Emerging memristive devices with flexibility and stretchability as well as direct processing-in-memory ability are promising candidates to perform data computing in flexible and stretchable electronics. To execute the in-memory computing paradigm including digital and analogue computing, the array configuration of memristive devices is usually required. Herein, the recent progress on flexible and stretchable memristive arrays for in-memory computing is reviewed. The common materials used for flexible memristive arrays, including inorganic, organic and two-dimensional (2D) materials, will be highlighted, and effective strategies used for stretchable memristive arrays, including material innovation and structural design, will be discussed in detail. The current challenges and future perspectives of the in-memory computing utilizing flexible and stretchable memristive arrays are presented. These efforts aim to accelerate the development of flexible and stretchable memristive arrays for data computing in advanced intelligent systems, such as electronic skin, soft robotics, and wearable devices.
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Abstract
With the development of the Internet of things, artificial intelligence, and wearable devices, massive amounts of data are generated and need to be processed. High standards are required to store and analyze this information. In the face of the explosive growth of information, the memory used in data storage and processing faces great challenges. Among many types of memories, memristors have received extensive attentions due to their low energy consumption, strong tolerance, simple structure, and strong miniaturization. However, they still face many problems, especially in the application of artificial bionic synapses, which call for higher requirements in the mechanical properties of the device. The progress of integrated circuit and micro-processing manufacturing technology has greatly promoted development of the flexible memristor. The use of a flexible memristor to simulate nerve synapses will provide new methods for neural network computing and bionic sensing systems. In this paper, the materials and structure of the flexible memristor are summarized and discussed, and the latest configuration and new materials are described. In addition, this paper will focus on its application in artificial bionic synapses and discuss the challenges and development direction of flexible memristors from this perspective.
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He ZY, Wang TY, Meng JL, Zhu H, Ji L, Sun QQ, Chen L, Zhang DW. CMOS back-end compatible memristors for in situ digital and neuromorphic computing applications. MATERIALS HORIZONS 2021; 8:3345-3355. [PMID: 34635907 DOI: 10.1039/d1mh01257f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In-memory logic calculations and brain-inspired artificial synaptic neuromorphic computing are expected to solve the limitations of the traditional von Neumann computing architecture. The data processing efficiency of the traditional von Neumann architecture is inherently limited by its physically separated processing and storage units, and thus data transmission besides calculation leads to a limited calculation speed and additional high-power consumption. In addition, traditional digital logic calculations and analog calculations have greater limitations in conversion. Herein, we report a flexible two-terminal memristor based on SiCO:H, which is a porous low-k back-end complementary metal-oxide-semiconductor (CMOS)-compatible material. Due to its low operating voltage (200 mV) and fast response speed (100 ns), it could perform digital memory calculation and neuromorphic calculation simultaneously. The memristor could realize a transition from short-term to long-term plasticity in the process of enhancement and inhibition during neuromorphic calculation, with high biological reality. In digital logic calculations, IMP-based and MAGIC-based logic calculations were verified. In neuromorphic computing, an Ag ion-based conductive filament was introduced. The relationship between the temporal dynamics of the conductance evolution and the diffusive dynamics of the Ag active metal could be modulated by the external programming electric field strength. The synapses and neuron dynamics in biology were faithfully simulated, realizing a transition from short-term to long-term plasticity in the process of enhancement and inhibition, which has high compatibility and scalability, proposing a novel solution for the next generation of computer architectures.
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Affiliation(s)
- Zhen-Yu He
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
| | - Tian-Yu Wang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
| | - Jia-Lin Meng
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
| | - Hao Zhu
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
- National Integrated Circuit Innovation Center, No. 825 Zhangheng Road, Shanghai 201203, China
| | - Li Ji
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
- National Integrated Circuit Innovation Center, No. 825 Zhangheng Road, Shanghai 201203, China
| | - Qing-Qing Sun
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
- National Integrated Circuit Innovation Center, No. 825 Zhangheng Road, Shanghai 201203, China
| | - Lin Chen
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
- National Integrated Circuit Innovation Center, No. 825 Zhangheng Road, Shanghai 201203, China
| | - David Wei Zhang
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
- National Integrated Circuit Innovation Center, No. 825 Zhangheng Road, Shanghai 201203, China
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Abnavi A, Ahmadi R, Hasani A, Fawzy M, Mohammadzadeh MR, De Silva T, Yu N, Adachi MM. Free-Standing Multilayer Molybdenum Disulfide Memristor for Brain-Inspired Neuromorphic Applications. ACS APPLIED MATERIALS & INTERFACES 2021; 13:45843-45853. [PMID: 34542262 DOI: 10.1021/acsami.1c11359] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Recently, atomically thin two-dimensional (2D) transition-metal dichalcogenides (TMDs) have attracted great interest in electronic and opto-electronic devices for high-integration-density applications such as data storage due to their small vertical dimension and high data storage capability. Here, we report a memristor based on free-standing multilayer molybdenum disulfide (MoS2) with a high current on/off ratio of ∼103 and a stable retention for at least 3000 s. Through light modulation of the carrier density in the suspended MoS2 channel, the on/off ratio can be further increased to ∼105. Moreover, the essential photosynaptic functions with short- and long-term memory (STM and LTM) behaviors are successfully mimicked by such devices. These results also indicate that STM can be transferred to LTM by increasing the light stimuli power, pulse duration, and number of pulses. The electrical measurements performed under vacuum and ambient air conditions propose that the observed resistive switching is due to adsorbed oxygen and water molecules on both sides of the MoS2 channel. Thus, our free-standing 2D multilayer MoS2-based memristors propose a simple approach for fabrication of a low-power-consumption and reliable resistive switching device for neuromorphic applications.
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Affiliation(s)
- Amin Abnavi
- School of Engineering Science, Simon Fraser University, Burnaby V5A 1S6, British Columbia, Canada
| | - Ribwar Ahmadi
- School of Engineering Science, Simon Fraser University, Burnaby V5A 1S6, British Columbia, Canada
| | - Amirhossein Hasani
- School of Engineering Science, Simon Fraser University, Burnaby V5A 1S6, British Columbia, Canada
| | - Mirette Fawzy
- Department of Physics, Simon Fraser University, Burnaby V5A 1S6, British Columbia, Canada
| | | | - Thushani De Silva
- School of Engineering Science, Simon Fraser University, Burnaby V5A 1S6, British Columbia, Canada
| | - Niannian Yu
- School of Science, Wuhan University of Technology, Wuhan 430070, China
| | - Michael M Adachi
- School of Engineering Science, Simon Fraser University, Burnaby V5A 1S6, British Columbia, Canada
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Zhou Q, Ji B, Hu F, Luo J, Zhou B. Magnetized Micropillar-Enabled Wearable Sensors for Touchless and Intelligent Information Communication. NANO-MICRO LETTERS 2021; 13:197. [PMID: 34523060 PMCID: PMC8440750 DOI: 10.1007/s40820-021-00720-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 08/16/2021] [Indexed: 05/10/2023]
Abstract
The wearable sensors have recently attracted considerable attentions as communication interfaces through the information perception, decoding, and conveying process. However, it is still challenging to obtain a sensor that can convert detectable signals into multiple outputs for convenient, efficient, cryptic, and high-capacity information transmission. Herein, we present a capacitive sensor of magnetic field based on a tilted flexible micromagnet array (t-FMA) as the proposed interaction interface. With the bidirectional bending capability of t-FMA actuated by magnetic torque, the sensor can recognize both the magnitude and orientation of magnetic field in real time with non-overlapping capacitance signals. The optimized sensor exhibits the high sensitivity of over 1.3 T-1 and detection limit down to 1 mT with excellent durability. As a proof of concept, the sensor has been successfully demonstrated for convenient, efficient, and programmable interaction systems, e.g., touchless Morse code and Braille communication. The distinguishable recognition of the magnetic field orientation and magnitude further enables the sensor unit as a high-capacity transmitter for cryptic information interaction (e.g., encoded ID recognition) and multi-control instruction outputting. We believe that the proposed magnetic field sensor can open up a potential avenue for future applications including information communication, virtual reality device, and interactive robotics.
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Affiliation(s)
- Qian Zhou
- Joint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, 999078, P. R. China
| | - Bing Ji
- Joint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, 999078, P. R. China
| | - Fengming Hu
- School of Applied Physics and Materials, Research Center of Flexible Sensing Materials and Devices, Wuyi University, Jiangmen, 529020, P. R. China
| | - Jianyi Luo
- School of Applied Physics and Materials, Research Center of Flexible Sensing Materials and Devices, Wuyi University, Jiangmen, 529020, P. R. China
| | - Bingpu Zhou
- Joint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, 999078, P. R. China.
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Zhang W, Gao H, Deng C, Lv T, Hu S, Wu H, Xue S, Tao Y, Deng L, Xiong W. An ultrathin memristor based on a two-dimensional WS 2/MoS 2 heterojunction. NANOSCALE 2021; 13:11497-11504. [PMID: 34165120 DOI: 10.1039/d1nr01683k] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Memristors are regarded as one of the key devices to break through the traditional Von Neumann computer architecture due to their capability of simulating the function of neural synapses. Among various memristive materials, two-dimensional (2D) materials are promising candidates to build advanced memristors with extremely high integration density and low power consumption. However, memristors based on 2D materials usually suffer from poor endurance and retention due to their vulnerability to material degradation during the formation/fusing processes of conductive filament channels within the switching media of 2D materials. Here, a new memristor architecture based on a WS2/MoS2 2D semiconducting heterojunction (metal/heterojunction/metal, MHM) is proposed, which is completely different from the conventional metal/insulator/metal (MIM) sandwich structure. Through the introduction of a type-II 2D heterojunction, a resistance switching mechanism based on band modulation rather than the conductive filaments can be realized to eliminate the material degradation during the set/reset processes. A prototype MHM memristor based on the WS2/MoS2 heterojunction is successfully developed with a large switching on/off ratio up to 104 and a clearly extended endurance over 120 switching cycles, showing the advantage of the 2D WS2/MoS2 heterojunction over the individual MoS2 or WS2 layers in memristive performance. The proposed method for the MHM-type 2D memristor has the potential to achieve a large-scale integrated memristor matrix with low power consumption and high integration density, which is promising for future artificial intelligence and brain-like computing systems.
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Affiliation(s)
- Wenguang Zhang
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Hui Gao
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Chunsan Deng
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Ting Lv
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Sanlue Hu
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Hao Wu
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Songyan Xue
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Yufeng Tao
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China. and Institute of Micro-nano Optoelectronics and Terahertz Technology, Jiangsu University, Zhenjiang, 212013, China
| | - Leimin Deng
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Wei Xiong
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
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Nguyen TV, An J, Min KS. Memristor-CMOS Hybrid Neuron Circuit with Nonideal-Effect Correction Related to Parasitic Resistance for Binary-Memristor-Crossbar Neural Networks. MICROMACHINES 2021; 12:mi12070791. [PMID: 34357201 PMCID: PMC8304214 DOI: 10.3390/mi12070791] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/27/2021] [Accepted: 06/28/2021] [Indexed: 11/23/2022]
Abstract
Voltages and currents in a memristor crossbar can be significantly affected due to nonideal effects such as parasitic source, line, and neuron resistance. These nonideal effects related to the parasitic resistance can cause the degradation of the neural network’s performance realized with the nonideal memristor crossbar. To avoid performance degradation due to the parasitic-resistance-related nonideal effects, adaptive training methods were proposed previously. However, the complicated training algorithm could add a heavy computational burden to the neural network hardware. Especially, the hardware and algorithmic burden can be more serious for edge intelligence applications such as Internet of Things (IoT) sensors. In this paper, a memristor-CMOS hybrid neuron circuit is proposed for compensating the parasitic-resistance-related nonideal effects during not the training phase but the inference one, where the complicated adaptive training is not needed. Moreover, unlike the previous linear correction method performed by the external hardware, the proposed correction circuit can be included in the memristor crossbar to minimize the power and hardware overheads for compensating the nonideal effects. The proposed correction circuit has been verified to be able to restore the degradation of source and output voltages in the nonideal crossbar. For the source voltage, the average percentage error of the uncompensated crossbar is as large as 36.7%. If the correction circuit is used, the percentage error in the source voltage can be reduced from 36.7% to 7.5%. For the output voltage, the average percentage error of the uncompensated crossbar is as large as 65.2%. The correction circuit can improve the percentage error in the output voltage from 65.2% to 8.6%. Almost the percentage error can be reduced to ~1/7 if the correction circuit is used. The nonideal memristor crossbar with the correction circuit has been tested for MNIST and CIFAR-10 datasets in this paper. For MNIST, the uncompensated and compensated crossbars indicate the recognition rate of 90.4% and 95.1%, respectively, compared to 95.5% of the ideal crossbar. For CIFAR-10, the nonideal crossbars without and with the nonideal-effect correction show the rate of 85.3% and 88.1%, respectively, compared to the ideal crossbar achieving the rate as large as 88.9%.
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Affiliation(s)
- Tien Van Nguyen
- School of Electrical Engineering, Kookmin University, Seoul 02707, Korea
| | - Jiyong An
- School of Electrical Engineering, Kookmin University, Seoul 02707, Korea
| | - Kyeong-Sik Min
- School of Electrical Engineering, Kookmin University, Seoul 02707, Korea
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Wang J, Zhuge X, Zhuge F. Hybrid oxide brain-inspired neuromorphic devices for hardware implementation of artificial intelligence. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2021; 22:326-344. [PMID: 34025215 PMCID: PMC8128179 DOI: 10.1080/14686996.2021.1911277] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
The state-of-the-art artificial intelligence technologies mainly rely on deep learning algorithms based on conventional computers with classical von Neumann computing architectures, where the memory and processing units are separated resulting in an enormous amount of energy and time consumed in the data transfer process. Inspired by the human brain acting like an ultra-highly efficient biological computer, neuromorphic computing is proposed as a technology for hardware implementation of artificial intelligence. Artificial synapses are the main component of a neuromorphic computing architecture. Memristors are considered to be a relatively ideal candidate for artificial synapse applications due to their high scalability and low power consumption. Oxides are most widely used in memristors due to the ease of fabrication and high compatibility with complementary metal-oxide-semiconductor processes. However, oxide memristors suffer from unsatisfactory stability and reliability. Oxide-based hybrid structures can effectively improve the device stability and reliability, therefore providing a promising prospect for the application of oxide memristors to neuromorphic computing. This work reviews the recent advances in the development of hybrid oxide memristive synapses. The discussion is organized according to the blending schemes as well as the working mechanisms of hybrid oxide memristors.
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Affiliation(s)
- Jingrui Wang
- School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Xia Zhuge
- School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo, China
| | - Fei Zhuge
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- CONTACT Fei Zhuge Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo315201, China
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Zeng T, Yang Z, Liang J, Lin Y, Cheng Y, Hu X, Zhao X, Wang Z, Xu H, Liu Y. Flexible and transparent memristive synapse based on polyvinylpyrrolidone/N-doped carbon quantum dot nanocomposites for neuromorphic computing. NANOSCALE ADVANCES 2021; 3:2623-2631. [PMID: 36134157 PMCID: PMC9419774 DOI: 10.1039/d1na00152c] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 03/28/2021] [Indexed: 05/19/2023]
Abstract
Memristive devices are widely recognized as promising hardware implementations of neuromorphic computing. Herein, a flexible and transparent memristive synapse based on polyvinylpyrrolidone (PVP)/N-doped carbon quantum dot (NCQD) nanocomposites through regulating the NCQD doping concentration is reported. In situ Kelvin probe force microscopy showed that the trapping/detrapping of space charge can account for the memristive mechanism of the device. Diverse synaptic functions, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), spike-timing-dependent plasticity (STDP), and the transition from short-term plasticity (STP) to long-term plasticity (LTP), are emulated, enabling the PVP-NCQD hybrid system to be a valuable candidate for the design of novel artificial neural architectures. In addition, the synaptic device showed excellent flexibility against mechanical strain after repeated bending tests. This work provides a new approach to develop flexible and transparent organic artificial synapses for future wearable neuromorphic computing systems.
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Affiliation(s)
- Tao Zeng
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education 5268 Renmin Street Changchun P. R. China
| | - Zhi Yang
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education 5268 Renmin Street Changchun P. R. China
| | - Jiabing Liang
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education 5268 Renmin Street Changchun P. R. China
| | - Ya Lin
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education 5268 Renmin Street Changchun P. R. China
| | - Yankun Cheng
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education 5268 Renmin Street Changchun P. R. China
| | - Xiaochi Hu
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education 5268 Renmin Street Changchun P. R. China
| | - Xiaoning Zhao
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education 5268 Renmin Street Changchun P. R. China
| | - Zhongqiang Wang
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education 5268 Renmin Street Changchun P. R. China
| | - Haiyang Xu
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education 5268 Renmin Street Changchun P. R. China
| | - Yichun Liu
- Key Laboratory for UV Light-Emitting Materials and Technology (Northeast Normal University), Ministry of Education 5268 Renmin Street Changchun P. R. China
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Sun F, Lu Q, Feng S, Zhang T. Flexible Artificial Sensory Systems Based on Neuromorphic Devices. ACS NANO 2021; 15:3875-3899. [PMID: 33507725 DOI: 10.1021/acsnano.0c10049] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Emerging flexible artificial sensory systems using neuromorphic electronics have been considered as a promising solution for processing massive data with low power consumption. The construction of artificial sensory systems with synaptic devices and sensing elements to mimic complicated sensing and processing in biological systems is a prerequisite for the realization. To realize high-efficiency neuromorphic sensory systems, the development of artificial flexible synapses with low power consumption and high-density integration is essential. Furthermore, the realization of efficient coupling between the sensing element and the synaptic device is crucial. This Review presents recent progress in the area of neuromorphic electronics for flexible artificial sensory systems. We focus on both the recent advances of artificial synapses, including device structures, mechanisms, and functions, and the design of intelligent, flexible perception systems based on synaptic devices. Additionally, key challenges and opportunities related to flexible artificial perception systems are examined, and potential solutions and suggestions are provided.
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Affiliation(s)
- Fuqin Sun
- i -Lab, Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences (CAS), 398 Ruoshui Road, Suzhou 215123, P. R. China
- School of Nano Technology and Nano Bionics, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui 230026, P. R. China
| | - Qifeng Lu
- i -Lab, Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences (CAS), 398 Ruoshui Road, Suzhou 215123, P. R. China
| | - Simin Feng
- i -Lab, Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences (CAS), 398 Ruoshui Road, Suzhou 215123, P. R. China
| | - Ting Zhang
- i -Lab, Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences (CAS), 398 Ruoshui Road, Suzhou 215123, P. R. China
- School of Nano Technology and Nano Bionics, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui 230026, P. R. China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
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Jeon YR, Choi J, Kwon JD, Park MH, Kim Y, Choi C. Suppressed Stochastic Switching Behavior and Improved Synaptic Functions in an Atomic Switch Embedded with a 2D NbSe 2 Material. ACS APPLIED MATERIALS & INTERFACES 2021; 13:10161-10170. [PMID: 33591167 DOI: 10.1021/acsami.0c18784] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
We investigated chemical vapor-deposited (CVD) two-dimensional (2D) niobium diselenide (NbSe2) material for the resistive switching and synaptic characteristics. Three different atomic switch devices with Ag/HfO2/Pt, Ag/Ti/HfO2/Pt, and Ag/NbSe2/HfO2/Pt were studied as both memory and neuromorphic devices. Both the inserted Ti and NbSe2 buffer layers effectively control the stochastic Ag-ion diffusion, leading to suppressed variation of switching characteristics, which is a critical issue in an atomic switch device. Especially, the device with the 2D NbSe2 buffer layer strikingly enhanced the device reliability in both endurance and retention. In conjunction with scanning transmission electron microscopy (STEM) and energy-dispersive spectrometry (EDS) analysis of the control of the Ag-ion migration, it was understood that filament connection is interrelated with the SET and RESET processes. Besides resistive behaviors in the memory device, various synapse functions such as spike-rate-dependent plasticity (SRDP), forgetting curve, potentiation, and depression were demonstrated with an atomic switch with the 2D NbSe2 buffer layer. Furthermore, the emulated long-term synaptic property was simulated using the MNIST 28 × 28 pixel database. Using adopting a CVD 2D NbSe2 blocking layer, the stochastic Ag-ion diffusion behavior is well-controlled and therefore stable switching and synapse functions are attained.
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Affiliation(s)
- Yu-Rim Jeon
- Division of Materials Science and Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Jungmin Choi
- Materials Center for Energy Convergence, Korea Institute of Materials Science, Changwon 51508, Republic of Korea
| | - Jung-Dae Kwon
- Materials Center for Energy Convergence, Korea Institute of Materials Science, Changwon 51508, Republic of Korea
| | - Min Hyuk Park
- School of Materials Science and Engineering, Pusan National University, Pusan 46241, Republic of Korea
| | - Yonghun Kim
- Materials Center for Energy Convergence, Korea Institute of Materials Science, Changwon 51508, Republic of Korea
| | - Changhwan Choi
- Division of Materials Science and Engineering, Hanyang University, Seoul 04763, Republic of Korea
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Li ZY, Zhu LQ, Guo LQ, Ren ZY, Xiao H, Cai JC. Mimicking Neurotransmitter Activity and Realizing Algebraic Arithmetic on Flexible Protein-Gated Oxide Neuromorphic Transistors. ACS APPLIED MATERIALS & INTERFACES 2021; 13:7784-7791. [PMID: 33533611 DOI: 10.1021/acsami.0c22047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Recently, flexible neuromorphic devices have attracted extensive attention for the construction of perception cognitive systems with the ultimate objective to achieve robust computation, efficient learning, and adaptability to evolutionary changes. In particular, the design of flexible neuromorphic devices with data processing and arithmetic capabilities is highly desirable for wearable cognitive platforms. Here, an albumen-based protein-gated flexible indium tin oxide (ITO) ionotronic neuromorphic transistor was proposed. First, the transistor demonstrates excellent mechanical robustness against bending stress. Moreover, spike-duration-dependent synaptic plasticity and spike-amplitude-dependent synaptic plasticity behaviors are not affected by bending stress. With the unique protonic gating behaviors, neurotransmission processes in biological synapses are emulated, exhibiting three characteristics in neurotransmitter release, including quantal release, stochastic release, and excitatory or inhibitory release. In addition, three types of spike-timing-dependent plasticity learning rules are mimicked on the ITO ionotronic neuromorphic transistor. Most interestingly, algebraic arithmetic operations, including addition, subtraction, multiplication, and division, are implemented on the protein gated neuromorphic transistor for the first time. The present work would open a promising biorealistic avenue to the scientific community to control and design wearable "green" cognitive platforms, with potential applications including but not limited to intelligent humanoid robots and replacement neuroprosthetics.
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Affiliation(s)
- Zhi Yuan Li
- School of Physical Science and Technology, Ningbo University, Ningbo, Zhejiang 315211, People's Republic of China
- Institute of Intelligent Flexible Mechatronics, Jiangsu University, Zhenjiang 212013, People's Republic of China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang 315201, People's Republic of China
| | - Li Qiang Zhu
- School of Physical Science and Technology, Ningbo University, Ningbo, Zhejiang 315211, People's Republic of China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang 315201, People's Republic of China
| | - Li Qiang Guo
- Institute of Intelligent Flexible Mechatronics, Jiangsu University, Zhenjiang 212013, People's Republic of China
| | - Zheng Yu Ren
- School of Physical Science and Technology, Ningbo University, Ningbo, Zhejiang 315211, People's Republic of China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang 315201, People's Republic of China
| | - Hui Xiao
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang 315201, People's Republic of China
| | - Jia Cheng Cai
- School of Physical Science and Technology, Ningbo University, Ningbo, Zhejiang 315211, People's Republic of China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang 315201, People's Republic of China
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Khot AC, Dongale TD, Park JH, Kesavan AV, Kim TG. Ti 3C 2-Based MXene Oxide Nanosheets for Resistive Memory and Synaptic Learning Applications. ACS APPLIED MATERIALS & INTERFACES 2021; 13:5216-5227. [PMID: 33397081 DOI: 10.1021/acsami.0c19028] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
MXene, a new state-of-the-art two-dimensional (2D) nanomaterial, has attracted considerable interest from both industry and academia because of its excellent electrical, mechanical, and chemical properties. However, MXene-based device engineering has rarely been reported. In this study, we explored Ti3C2 MXene for digital and analog computing applications by engineering the top electrode. For this purpose, Ti3C2 MXene was synthesized by a simple chemical process, and its structural, compositional, and morphological properties were studied using various analytical tools. Finally, we explored its potential application in bipolar resistive switching (RS) and synaptic learning devices. In particular, the effect of the top electrode (Ag, Pt, and Al) on the RS properties of the Ti3C2 MXene-based memory devices was thoroughly investigated. Compared with the Ag and Pt top electrode-based devices, the Al/Ti3C2/Pt device exhibited better RS and operated more reliably, as determined by the evaluation of the charge-magnetic property and memory endurance and retention. Thus, we selected the Al/Ti3C2/Pt memristive device to mimic the potentiation and depression synaptic properties and spike-timing-dependent plasticity-based Hebbian learning rules. Furthermore, the electron transport in this device was found to occur by a filamentary RS mechanism (based on oxidized Ti3C2 MXene), as determined by analyzing the electrical fitting curves. The results suggest that the 2D Ti3C2 MXene is an excellent nanomaterial for non-volatile memory and synaptic learning applications.
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Affiliation(s)
- Atul C Khot
- School of Electrical Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Tukaram D Dongale
- School of Electrical Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea
- School of Nanoscience and Biotechnology, Shivaji University, Kolhapur 416 004, India
| | - Ju Hyun Park
- School of Electrical Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Arul Varman Kesavan
- School of Electrical Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Tae Geun Kim
- School of Electrical Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea
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Meng JL, Wang TY, He ZY, Chen L, Zhu H, Ji L, Sun QQ, Ding SJ, Bao WZ, Zhou P, Zhang DW. Flexible boron nitride-based memristor for in situ digital and analogue neuromorphic computing applications. MATERIALS HORIZONS 2021; 8:538-546. [PMID: 34821269 DOI: 10.1039/d0mh01730b] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The data processing efficiency of traditional computers is suffering from the intrinsic limitation of physically separated processing and memory units. Logic-in-memory and brain-inspired neuromorphic computing are promising in-memory computing paradigms for improving the computing efficiency and avoiding high power consumption caused by extra data movement. However, memristors that can conduct digital memcomputing and neuromorphic computing simultaneously are limited by the difference in the information form between digital data and analogue data. In order to solve this problem, this paper proposes a flexible low-dimensional memristor based on boron nitride (BN), which has ultralow-power non-volatile memory characteristic, reliable digital memcomputing capabilities, and integrated ultrafast neuromorphic computing capabilities in a single in situ computing system. The logic-in-memory basis, including FALSE, material implication (IMP), and NAND, are implemented successfully. The power consumption of the proposed memristor per synaptic event (198 fJ) can be as low as biology (fJ level) and the response time (1 μs) of the neuromorphic computing is four orders of magnitude shorter than that of the human brain (10 ms), paving the way for wearable ultrahigh efficient next-generation in-memory computing architectures.
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Affiliation(s)
- Jia-Lin Meng
- State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
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Hou YX, Li Y, Zhang ZC, Li JQ, Qi DH, Chen XD, Wang JJ, Yao BW, Yu MX, Lu TB, Zhang J. Large-Scale and Flexible Optical Synapses for Neuromorphic Computing and Integrated Visible Information Sensing Memory Processing. ACS NANO 2021; 15:1497-1508. [PMID: 33372769 DOI: 10.1021/acsnano.0c08921] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Optoelectronic synapses integrating synaptic and optical-sensing functions exhibit large advantages in neuromorphic computing for visual information processing and complex learning, recognition, and memory in an energy-efficient way. However, electric stimulation is still essential for existing optoelectronic synapses to realize bidirectional weight-updating, restricting the processing speed, bandwidth, and integration density of the devices. Herein, a two-terminal optical synapse based on a wafer-scale pyrenyl graphdiyne/graphene/PbS quantum dot heterostructure is proposed that can emulate both the excitatory and inhibitory synaptic behaviors in an optical pathway. The simple device architecture and low-dimensional features of the heterostructure endow the optical synapse with robust flexibility for wearable electronics. This optical synapse features a linear and symmetric conductance-update trajectory with numerous conductance states and low noise, which facilitates the demonstration of accurate and effective pattern recognition with a strong fault-tolerant capability even at bending states. A series of logic functions and associative learning capabilities have been demonstrated by the optical synapses in optical pathways, significantly enhancing the information processing capability for neuromorphic computing. Moreover, an integrated visible information sensing memory processing system based on the optical synapse array is constructed to perform real-time detection, in situ image memorization, and distinction tasks. This work is an important step toward the development of optogenetics-inspired neuromorphic computing and adaptive parallel processing networks for wearable electronics.
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Affiliation(s)
| | | | | | - Jia-Qiang Li
- Center for Nanochemistry, Beijing Science and Engineering Center for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | | | | | | | | | | | | | - Jin Zhang
- Center for Nanochemistry, Beijing Science and Engineering Center for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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Choi S, Yang J, Wang G. Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e2004659. [PMID: 33006204 DOI: 10.1002/adma.202004659] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 08/12/2020] [Indexed: 06/11/2023]
Abstract
Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the memristor is necessary for achieving memristor-based neuromorphic hardware systems. Herein, a wide range of memristors and memristive-related devices for artificial synapses and neurons is highlighted. The device structures, switching principles, and the applications of essential synaptic and neuronal functionalities are sequentially presented. Moreover, recent advances in memristive artificial neural networks and their hardware implementations are introduced along with an overview of the various learning algorithms. Finally, the main challenges of the memristive synapses and neurons toward high-performance and energy-efficient neuromorphic computing are briefly discussed. This progress report aims to be an insightful guide for the research on memristors and neuromorphic-based computing.
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Affiliation(s)
- Sanghyeon Choi
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Jehyeon Yang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Gunuk Wang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
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50
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Wang L, Wen J, Jiang Y, Ou Q, Yu L, Xiong BS, Yang B, Zhang C, Tong Y. Electrical Conduction Characteristic of a 2D MXene Device with Cu/Cr 2C/TiN Structure Based on Density Functional Theory. MATERIALS 2020; 13:ma13173671. [PMID: 32825231 PMCID: PMC7503317 DOI: 10.3390/ma13173671] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 08/10/2020] [Accepted: 08/17/2020] [Indexed: 11/16/2022]
Abstract
The electronic structure and the corresponding electrical conductive behavior of the Cu/Cr2C/TiN stack were assessed according to a newly developed first-principle model based on density functional theory. Using an additional Cr2C layer provides the metal-like characteristic of the Cu/Cr2C/TiN stack with much larger electrical conduction coefficients (i.e., mobility, diffusivity, and electrical conductivity) than the conventional Ag/Ti3C2/Pt stack due to the lower activation energy. This device is therefore capable of offering faster switching speeds, lower programming voltage, and better stability and durability than the memristor device with conventional Ti3C2 MXene.
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Affiliation(s)
- Lei Wang
- School of Information Engineering, Nanchang Hangkong University, Nanchang 330000, China; (J.W.); (Y.J.); (Q.O.); (L.Y.); (B.-S.X.)
- Correspondence: (L.W.); (Y.T.)
| | - Jing Wen
- School of Information Engineering, Nanchang Hangkong University, Nanchang 330000, China; (J.W.); (Y.J.); (Q.O.); (L.Y.); (B.-S.X.)
| | - Yuan Jiang
- School of Information Engineering, Nanchang Hangkong University, Nanchang 330000, China; (J.W.); (Y.J.); (Q.O.); (L.Y.); (B.-S.X.)
| | - Qiaofeng Ou
- School of Information Engineering, Nanchang Hangkong University, Nanchang 330000, China; (J.W.); (Y.J.); (Q.O.); (L.Y.); (B.-S.X.)
| | - Lei Yu
- School of Information Engineering, Nanchang Hangkong University, Nanchang 330000, China; (J.W.); (Y.J.); (Q.O.); (L.Y.); (B.-S.X.)
| | - Bang-Shu Xiong
- School of Information Engineering, Nanchang Hangkong University, Nanchang 330000, China; (J.W.); (Y.J.); (Q.O.); (L.Y.); (B.-S.X.)
| | - Bingxing Yang
- State of Key Laboratory of Polyolefins and Catalysis, Shanghai 200062, China;
- Shanghai Institute of Technology, Shanghai 201418, China
| | - Chao Zhang
- Shanghai Research Institute of Chemical Industry Co., Ltd., Shanghai 200062, China;
| | - Yi Tong
- College of Electronic and Optical Engineering & College of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
- Correspondence: (L.W.); (Y.T.)
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