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Wang S, Gao S, Tang C, Occhipinti E, Li C, Wang S, Wang J, Zhao H, Hu G, Nathan A, Dahiya R, Occhipinti LG. Memristor-based adaptive neuromorphic perception in unstructured environments. Nat Commun 2024; 15:4671. [PMID: 38821961 PMCID: PMC11143376 DOI: 10.1038/s41467-024-48908-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 05/16/2024] [Indexed: 06/02/2024] Open
Abstract
Efficient operation of control systems in robotics or autonomous driving targeting real-world navigation scenarios requires perception methods that allow them to understand and adapt to unstructured environments with good accuracy, adaptation, and generality, similar to humans. To address this need, we present a memristor-based differential neuromorphic computing, perceptual signal processing, and online adaptation method providing neuromorphic style adaptation to external sensory stimuli. The adaptation ability and generality of this method are confirmed in two application scenarios: object grasping and autonomous driving. In the former, a robot hand realizes safe and stable grasping through fast ( ~ 1 ms) adaptation based on the tactile object features with a single memristor. In the latter, decision-making information of 10 unstructured environments in autonomous driving is extracted with an accuracy of 94% with a 40×25 memristor array. By mimicking human low-level perception mechanisms, the electronic neuromorphic circuit-based method achieves real-time adaptation and high-level reactions to unstructured environments.
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Affiliation(s)
- Shengbo Wang
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China
| | - Shuo Gao
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China.
| | - Chenyu Tang
- Department of Engineering, University of Cambridge, Cambridge, UK
| | - Edoardo Occhipinti
- UKRI Centre for Doctoral Training in AI for Healthcare, Department of Computing, Imperial College London, London, UK
| | - Cong Li
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China
| | - Shurui Wang
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China
| | - Jiaqi Wang
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China
| | - Hubin Zhao
- HUB of Intelligent Neuro-engineering (HUBIN), CREATe, Division of Surgery and Interventional Science, UCL, HA7 4LP, Stanmore, UK
| | - Guohua Hu
- Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, N. T., Hong Kong S. A. R., China
| | - Arokia Nathan
- Darwin College, University of Cambridge, Cambridge, UK
- School of Information Science and Engineering, Shandong University, Qingdao, 266237, China
| | - Ravinder Dahiya
- Bendable Electronics and Sustainable Technologies (BEST) Group, Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115, USA
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2
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Bidoul N, Roisin N, Flandre D. Tuning the Intrinsic Stochasticity of Resistive Switching in VO 2 Microresistors. NANO LETTERS 2024; 24:6201-6209. [PMID: 38757925 DOI: 10.1021/acs.nanolett.4c00184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
Vanadium dioxide (VO2) microresistors exhibit resistive switching above a certain threshold voltage, allowing them to emulate neurons in neuromorphic systems. However, such devices present intrinsic cycle-to-cycle variations in their resistances and threshold voltages, which can be detrimental or beneficial, depending on their use. Here, we study this stochasticity in VO2 microresistors with various grain sizes and dimensions, through high-resolution electrical and optical measurements across numerous cycles. Our results highlight that the cycle-to-cycle variations in threshold voltage increase as the grain size becomes comparable to the device dimensions. We also present observations of multimodal threshold voltage distributions in the smaller-length resistors. To understand the underlying phenomenon, we investigate the relationship between the device insulating resistance and threshold voltage distributions, showing that these modes could correspond to distinct percolation paths and filaments. Our findings provide the first experimentally verified guidelines for designing VO2 devices with minimized/maximized stochasticity, depending on the targeted application.
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Affiliation(s)
- Noémie Bidoul
- Institute for Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve 1348, Belgium
| | - Nicolas Roisin
- Institute for Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve 1348, Belgium
| | - Denis Flandre
- Institute for Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve 1348, Belgium
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3
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Zhu T, Liu K, Zhang Y, Meng S, He M, Zhang Y, Yan M, Dong X, Li X, Jiang M, Xu H. Gate Voltage- and Bias Voltage-Tunable Staggered-Gap to Broken-Gap Transition Based on WSe 2/Ta 2NiSe 5 Heterostructure for Multimode Optoelectronic Logic Gate. ACS NANO 2024; 18:11462-11473. [PMID: 38632853 DOI: 10.1021/acsnano.4c02923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Two-dimensional (2D) materials with superior properties exhibit tremendous potential in developing next-generation electronic and optoelectronic devices. Integrating various functions into one device is highly expected as that endows 2D materials great promise for more Moore and more-than-Moore device applications. Here, we construct a WSe2/Ta2NiSe5 heterostructure by stacking the p-type WSe2 and the n-type narrow gap Ta2NiSe5 with the aim to achieve a multifunction optoelectronic device. Owing to the large interface potential barrier, the heterostructure device reveals a prominent diode feature with a large rectify ratio (7.6 × 104) and a low dark current (10-12 A). Especially, gate voltage- and bias voltage-tunable staggered-gap to broken-gap transition is achieved on the heterostructure device, which enables gate voltage-tunable forward and reverse rectifying features. As results, the heterostructure device exhibits superior self-powered photodetection properties, including a high detectivity of 1.08 × 1010 Jones and a fast response time of 91 μs. Additionally, the intrinsic structural anisotropy of Ta2NiSe5 endows the heterostructure device with strong polarization-sensitive photodetection and high-resolution polarization imaging. Based on these characteristics, a multimode optoelectronic logic gate is realized on the heterostructure via synergistically modulating the light on/off, polarization angle, gate voltage, and bias voltage. This work shed light on the future development of constructing high-performance multifunctional optoelectronic devices.
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Affiliation(s)
- Tao Zhu
- State Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, School of Physics, Northwest University, Xi'an 710069, P. R. China
- Key Laboratory of Applied Surface and Colloid Chemistry of Ministry of Education, Shaanxi Key Laboratory for Advanced Energy Devices, School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, P. R. China
| | - Kai Liu
- State Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, School of Physics, Northwest University, Xi'an 710069, P. R. China
- Key Laboratory of Applied Surface and Colloid Chemistry of Ministry of Education, Shaanxi Key Laboratory for Advanced Energy Devices, School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, P. R. China
| | - Yao Zhang
- State Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, School of Physics, Northwest University, Xi'an 710069, P. R. China
- Key Laboratory of Applied Surface and Colloid Chemistry of Ministry of Education, Shaanxi Key Laboratory for Advanced Energy Devices, School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, P. R. China
| | - Si Meng
- Key Laboratory of Applied Surface and Colloid Chemistry of Ministry of Education, Shaanxi Key Laboratory for Advanced Energy Devices, School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, P. R. China
| | - Mengfei He
- Key Laboratory of Applied Surface and Colloid Chemistry of Ministry of Education, Shaanxi Key Laboratory for Advanced Energy Devices, School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, P. R. China
| | - Yingli Zhang
- State Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, School of Physics, Northwest University, Xi'an 710069, P. R. China
| | - Minglu Yan
- State Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, School of Physics, Northwest University, Xi'an 710069, P. R. China
| | - Xiaoxiang Dong
- Department of Physics, Xiamen University, Xiamen 361005, P. R. China
| | - Xiaobo Li
- Shaanxi Joint Key Laboratory of Graphene, School of Advanced Materials and Nanotechnology, Xidian University, Xi'an 710126, P. R. China
| | - Man Jiang
- State Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, School of Physics, Northwest University, Xi'an 710069, P. R. China
| | - Hua Xu
- Key Laboratory of Applied Surface and Colloid Chemistry of Ministry of Education, Shaanxi Key Laboratory for Advanced Energy Devices, School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, P. R. China
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4
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Kunwar S, Cucciniello N, Mazza AR, Zhang D, Santillan L, Freiman B, Roy P, Jia Q, MacManus-Driscoll JL, Wang H, Nie W, Chen A. Reconfigurable Resistive Switching in VO 2/La 0.7Sr 0.3MnO 3/Al 2O 3 (0001) Memristive Devices for Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2024; 16:19103-19111. [PMID: 38578811 DOI: 10.1021/acsami.3c19032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/07/2024]
Abstract
The coexistence of nonvolatile and volatile switching modes in a single memristive device provides flexibility to emulate both neuronal and synaptic functions in the brain. Furthermore, such a device structure may eliminate the need for additional circuit elements such as transistor-based selectors, enabling low-power consumption and high-density device integration in fully memristive spiking neural networks. In this work, we report dual resistive switching (RS) modes in VO2/La0.7Sr0.3MnO3 (LSMO) bilayer memristive devices. Specifically, the nonvolatile RS is driven by the movement of oxygen vacancies (Vo) at the VO2/LSMO interface and requires a higher biasing voltage, whereas the volatile RS is controlled by the metal-insulator transition (MIT) of VO2 under a lower biasing voltage. The simple device structure is electrically driven between the two RS modes and thus can operate as a one selector-one resistor (1S1R) cell, which is a desirable feature in memristive crossbar arrays to avoid the sneak-path current issue. The RS modes are found to be stable and repeatable and can be reconfigured by exploiting the interfacial and phase transition properties, and thus, they hold great promise for applications in memristive neural networks and neuromorphic computing.
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Affiliation(s)
- Sundar Kunwar
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Nicholas Cucciniello
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Department of Materials Design and Innovation, University at Buffalo - The State University of New York, Buffalo, New York 14260, United States
| | - Alessandro R Mazza
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
- Materials Science and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Di Zhang
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Luis Santillan
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Ben Freiman
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Pinku Roy
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Quanxi Jia
- Department of Materials Design and Innovation, University at Buffalo - The State University of New York, Buffalo, New York 14260, United States
| | - Judith L MacManus-Driscoll
- Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, U.K
| | - Haiyan Wang
- School of Materials Engineering, Purdue University, West Lafayette, Indiana 47907, United States
| | - Wanyi Nie
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Aiping Chen
- Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
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5
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Jiang M, Zeng Z. Memristive Bionic Memory Circuit Implementation and Its Application in Multisensory Mutual Associative Learning Networks. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2024; 18:308-321. [PMID: 37831580 DOI: 10.1109/tbcas.2023.3324574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/15/2023]
Abstract
Memory is vital and indispensable for organisms and brain-inspired intelligence to gain complete sensation and cognition of the environment. In this work, a memristive bionic memory circuit inspired by human memory model is proposed, which includes 1) receptor and sensory neuron (SN), 2) short-term memory (STM) module, and 3) long-term memory (LTM) module. By leveraging the in-memory computing characteristic of memristors, various functions such as sensation, learning, forgetting, recall, consolidation, reconsolidation, retrieval, and reset are realized. Besides, a multisensory mutual associative learning network is constructed with several bionic memory units to memorize and associate sensory information of different modalities bidirectionally. Except for association establishment, enhancement, and extinction, we also mimicked multisensory integration to manifest the synthetic process of information from different sensory channels. According to the simulation results in PSPICE, the proposed circuit performs high robustness, low area overhead, and low power consumption. Combining associative memory with human memory model, this work provides a possible idea for further research in associative learning networks.
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Liu C, Tiw PJ, Zhang T, Wang Y, Cai L, Yuan R, Pan Z, Yue W, Tao Y, Yang Y. VO 2 memristor-based frequency converter with in-situ synthesize and mix for wireless internet-of-things. Nat Commun 2024; 15:1523. [PMID: 38374302 PMCID: PMC10876666 DOI: 10.1038/s41467-024-45923-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 02/07/2024] [Indexed: 02/21/2024] Open
Abstract
Wireless internet-of-things (WIoT) with data acquisition sensors are evolving rapidly and the demand for transmission efficiency is growing rapidly. Frequency converter that synthesizes signals at different frequencies and mixes them with sensor datastreams is a key component for efficient wireless transmission. However, existing frequency converters employ separate synthesize and mix circuits with complex digital and analog circuits using complementary metal-oxide semiconductor (CMOS) devices, naturally incurring excessive latency and energy consumption. Here we report a highly uniform and calibratable VO2 memristor oscillator, based on which we build memristor-based frequency converter using 8[Formula: see text]8 VO2 array that can realize in-situ frequency synthesize and mix with help of compact periphery circuits. We investigate the self-oscillation based on negative differential resistance of VO2 memristors and the programmability with different driving currents and calibration resistances, demonstrating capabilities of such frequency converter for in-situ frequency synthesize and mix for 2 ~ 8 channels with frequencies up to 48 kHz for low frequency transmission link. When transmitting classical sensor data (acoustic, vision and spatial) in an end-to-end WIoT experimental setup, our VO2-based memristive frequency converter presents up to 1.45× ~ 1.94× power enhancement with only 0.02 ~ 0.21 dB performance degradations compared with conventional CMOS-based frequency converter. This work highlights the potential in solving frequency converter's speed and energy efficiency problems in WIoT using high crystalline quality epitaxially grown VO2 and calibratable VO2-based oscillator array, revealing a promising direction for next-generation WIoT system design.
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Affiliation(s)
- Chang Liu
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Pek Jun Tiw
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Teng Zhang
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Yanghao Wang
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Lei Cai
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Rui Yuan
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Zelun Pan
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Wenshuo Yue
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China
| | - Yaoyu Tao
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.
- Center for Brain Inspired Chips, Institute for Artificial Intelligence, Frontiers Science Center for Nano-optoelectronics, Peking University, Beijing, 100871, China.
| | - Yuchao Yang
- Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.
- Center for Brain Inspired Chips, Institute for Artificial Intelligence, Frontiers Science Center for Nano-optoelectronics, Peking University, Beijing, 100871, China.
- School of Electronic and Computer Engineering, Peking University, Shenzhen, 518055, China.
- Center for Brain Inspired Intelligence, Chinese Institute for Brain Research (CIBR), Beijing, Beijing, 102206, China.
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7
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Wan C, Pei M, Shi K, Cui H, Long H, Qiao L, Xing Q, Wan Q. Toward a Brain-Neuromorphics Interface. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2311288. [PMID: 38339866 DOI: 10.1002/adma.202311288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/17/2024] [Indexed: 02/12/2024]
Abstract
Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.
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Affiliation(s)
- Changjin Wan
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, China
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Mengjiao Pei
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Kailu Shi
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Hangyuan Cui
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Haotian Long
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Lesheng Qiao
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qianye Xing
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qing Wan
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, China
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
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Wu T, Gao S, Li Y. IGZO/WO 3-x -Heterostructured Artificial Optoelectronic Synaptic Devices Mimicking Image Segmentation and Motion Capture. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2309857. [PMID: 38258604 DOI: 10.1002/smll.202309857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/11/2024] [Indexed: 01/24/2024]
Abstract
Currently, artificial neural networks (ANNs) based on memristors are limited to recognizing static images of objects when simulating human visual system, preventing them from performing high-dimensional information perception, and achieving more complex biomimetic functions is subject to certain limitations. In this work, indium gallium zinc oxide (IGZO)/tungsten oxide (WO3-x )-heterostructured artificial optoelectronic synaptic devices mimicking image segmentation and motion capture exhibiting high-performance optoelectronic synaptic responses are proposed and demonstrated. Upon electrical and optical stimulations, the device shows a variety of fundamental and advanced electrical and optical synaptic plasticity. Most importantly, outstanding and repeatable linear synaptic weight changes are attained by the developed memristor. By taking advantage of the notable linear synaptic weight changes, ANNs have been constructed and successfully utilized to demonstrate two applications in the field of computer vision, including image segmentation and object tracking. The accuracy attained by the memristor-based ANNs is similar to that of the computer algorithms, while its power has been significantly reduced by 105 orders of magnitude. With successful emulations of the human brain reactions when observing objects, the demonstrated memristor and related ANNs can be effectively utilized in constructing artificial optoelectronic synaptic devices and show promising potential in emulating human visual perception.
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Affiliation(s)
- Tong Wu
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan, 250022, China
| | - Song Gao
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan, 250022, China
| | - Yang Li
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan, 250022, China
- School of Microelectronics, Shandong University, Jinan, 250101, China
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9
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Tseng LM, Chen PF, Wen CY. Design of Edge-IoMT Network Architecture with Weight-Based Scheduling. SENSORS (BASEL, SWITZERLAND) 2023; 23:8553. [PMID: 37896645 PMCID: PMC10611017 DOI: 10.3390/s23208553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/15/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023]
Abstract
Population health monitoring based on the Internet of Medical Things (IoMT) is becoming an important application trend healthcare improvement. This work aims to develop an autonomous network architecture, collecting sensor data with a cluster topology, forwarding information through relay nodes, and applying edge computing and transmission scheduling for network scalability and operational efficiency. The proposed distributed network architecture incorporates data compression technologies and effective scheduling algorithms for handling the transmission scheduling of various physiological signals. Compared to existing scheduling mechanisms, the experimental results depict the network performance and show that in analyzing the delay and jitter, the proposed WFQ-based algorithms have reduced the delay and jitter ratio by about 40% and 19.47% compared to LLQ with priority queueing scheme, respectively. The experimental results also demonstrate that the proposed network topology is more effective than the direct path transmission approach in terms of energy consumption, which suggests that the proposed network architecture may improve the development of medical applications with body area networks such that the goal of self-organizing population health monitoring can be achieved.
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Affiliation(s)
- Li-Min Tseng
- Department of Electrical Engineering, National Chung Hsing University, Taichung 402, Taiwan; (L.-M.T.); (P.-F.C.)
| | - Ping-Feng Chen
- Department of Electrical Engineering, National Chung Hsing University, Taichung 402, Taiwan; (L.-M.T.); (P.-F.C.)
| | - Chih-Yu Wen
- Department of Electrical Engineering, National Chung Hsing University, Taichung 402, Taiwan; (L.-M.T.); (P.-F.C.)
- Smart Sustainable New Agriculture Research Center (SMARTer), National Chung Hsing University, Taichung 402, Taiwan
- Innovation and Development Center of Sustainable Agriculture (IDCSA), National Chung Hsing University, Taichung 402, Taiwan
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