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Kim HS, Park H, Cho WJ. Light-Stimulated IGZO Transistors with Tunable Synaptic Plasticity Based on Casein Electrolyte Electric Double Layer for Neuromorphic Systems. Biomimetics (Basel) 2023; 8:532. [PMID: 37999173 PMCID: PMC10669183 DOI: 10.3390/biomimetics8070532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/05/2023] [Accepted: 11/07/2023] [Indexed: 11/25/2023] Open
Abstract
In this study, optoelectronic synaptic transistors based on indium-gallium-zinc oxide (IGZO) with a casein electrolyte-based electric double layer (EDL) were examined. The casein electrolyte played a crucial role in modulating synaptic plasticity through an internal proton-induced EDL effect. Thus, important synaptic behaviors, such as excitatory post-synaptic current, paired-pulse facilitation, and spike rate-dependent and spike number-dependent plasticity, were successfully implemented by utilizing the persistent photoconductivity effect of the IGZO channel stimulated by light. The synergy between the light stimulation and the EDL effect allowed the effective modulation of synaptic plasticity, enabling the control of memory levels, including the conversion of short-term memory to long-term memory. Furthermore, a Modified National Institute of Standards and Technology digit recognition simulation was performed using a three-layer artificial neural network model, achieving a high recognition rate of 90.5%. These results demonstrated a high application potential of the proposed optoelectronic synaptic transistors in neuromorphic visual systems.
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Affiliation(s)
- Hwi-Su Kim
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea;
| | - Hamin Park
- Department of Electronic Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea;
| | - Won-Ju Cho
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea;
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Pecunia V, Occhipinti LG, Cloutier SG, Sun S, Grace AN, Leong WL. Editorial: Focus on green nanomaterials for a sustainable internet of things. NANOTECHNOLOGY 2023; 35:040201. [PMID: 37848022 DOI: 10.1088/1361-6528/ad0410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 10/16/2023] [Indexed: 10/19/2023]
Abstract
In the dynamic landscape of the Internet of Things (IoT), where smart devices are reshaping our world, nanomaterials can play a pivotal role in ensuring the IoT's sustainability. These materials are poised to redefine the development of smart devices, not only enabling cost-effective fabrication but also unlocking novel functionalities. As the IoT is set to encompass an astounding number of interconnected devices, the demand for environmentally friendly nanomaterials takes center stage. ThisFocus Issuespotlights cutting-edge research that explores the intersection of nanomaterials and sustainability. The collection delves deep into this critical nexus, encompassing a wide range of topics, from fundamental properties to applications in devices (e.g. sensors, optoelectronic synapses, energy harvesters, memory components, energy storage devices, and batteries), aspects concerning circularity and green synthesis, and an array of materials comprising organic semiconductors, perovskites, quantum dots, nanocellulose, graphene, and two-dimensional semiconductors. Authors not only showcase advancements but also delve into the sustainability profile of these materials, fostering a responsible endeavour toward a green IoT future.
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Affiliation(s)
- Vincenzo Pecunia
- School of Sustainable Energy Engineering, Simon Fraser University, 5118 - 10285 University Drive, Surrey V3T 0N1, BC, Canada
| | - Luigi G Occhipinti
- Cambridge Graphene Centre, Department of Engineering, University of Cambridge, 9 J J Thomson Avenue, Cambridge CB3 0FA, United Kingdom
| | - Sylvain G Cloutier
- Department of Electrical Engineering, École de Technologie Supérieure, 1100 Notre Dame Street West, Montreal H3C 1K3, QC, Canada
| | - Shuhui Sun
- Institut National de la Recherche Scientifique (INRS)-Centre Énergie Matériaux et Télécommunications, 1650 Boul. Lionel-Boulet, Varennes J3X 1P7, QC, Canada
| | - Andrews Nirmala Grace
- Centre for Nanotechnology Research, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Wei Lin Leong
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore
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Luo J, Tian G, Zhang DG, Zhang XC, Lu ZN, Zhang ZD, Cai JW, Zhong YN, Xu JL, Gao X, Wang SD. Voltage-Mode Ferroelectric Synapse for Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2023; 15:48452-48461. [PMID: 37802499 DOI: 10.1021/acsami.3c09506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Ferroelectric materials with a modulable polarization extent hold promise for exploring voltage-driven neuromorphic hardware, in which direct current flow can be minimized. Utilizing a single active layer of an insulating ferroelectric polymer, we developed a voltage-mode ferroelectric synapse that can continuously and reversibly update its states. The device states are straightforwardly manifested in the form of variable output voltage, enabling large-scale direct cascading of multiple ferroelectric synapses to build a deep physical neural network. Such a neural network based on potential superposition rather than current flow is analogous to the biological counterpart driven by action potentials in the brain. A high accuracy of over 97% for the simulation of handwritten digit recognition is achieved using the voltage-mode neural network. The controlled ferroelectric polarization, revealed by piezoresponse force microscopy, turns out to be responsible for the synaptic weight updates in the ferroelectric synapses. The present work demonstrates an alternative strategy for the design and construction of emerging artificial neural networks.
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Affiliation(s)
- Jie Luo
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Guo Tian
- Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, P. R. China
| | - Ding-Guo Zhang
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Xing-Chen Zhang
- Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, P. R. China
| | - Zhen-Ni Lu
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Zhong-Da Zhang
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Jia-Wei Cai
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Ya-Nan Zhong
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Jian-Long Xu
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Xu Gao
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Sui-Dong Wang
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, P. R. China
- Macao Institute of Materials Science and Engineering (MIMSE), MUST-SUDA Joint Research Center for Advanced Functional Materials, Macau University of Science and Technology, Taipa, Macao 999078, P. R. China
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Zhang Y, Huang Z, Jiang J. Emerging photoelectric devices for neuromorphic vision applications: principles, developments, and outlooks. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2186689. [PMID: 37007672 PMCID: PMC10054230 DOI: 10.1080/14686996.2023.2186689] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 02/16/2023] [Accepted: 02/28/2023] [Indexed: 06/19/2023]
Abstract
The traditional von Neumann architecture is gradually failing to meet the urgent need for highly parallel computing, high-efficiency, and ultra-low power consumption for the current explosion of data. Brain-inspired neuromorphic computing can break the inherent limitations of traditional computers. Neuromorphic devices are the key hardware units of neuromorphic chips to implement the intelligent computing. In recent years, the development of optogenetics and photosensitive materials has provided new avenues for the research of neuromorphic devices. The emerging optoelectronic neuromorphic devices have received a lot of attentions because they have shown great potential in the field of visual bionics. In this paper, we summarize the latest visual bionic applications of optoelectronic synaptic memristors and transistors based on different photosensitive materials. The basic principle of bio-vision formation is first introduced. Then the device structures and operating mechanisms of optoelectronic memristors and transistors are discussed. Most importantly, the recent progresses of optoelectronic synaptic devices based on various photosensitive materials in the fields of visual perception are described. Finally, the problems and challenges of optoelectronic neuromorphic devices are summarized, and the future development of visual bionics is also proposed.
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Affiliation(s)
- Yi Zhang
- Hunan Key Laboratory of Nanophotonics and Devices, Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, China
| | - Zhuohui Huang
- Hunan Key Laboratory of Nanophotonics and Devices, Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, China
| | - Jie Jiang
- Hunan Key Laboratory of Nanophotonics and Devices, Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, China
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Shen R, Jiang Y, Li Z, Tian J, Li S, Li T, Chen Q. Near-Infrared Artificial Optical Synapse Based on the P(VDF-TrFE)-Coated InAs Nanowire Field-Effect Transistor. MATERIALS (BASEL, SWITZERLAND) 2022; 15:8247. [PMID: 36431733 PMCID: PMC9698720 DOI: 10.3390/ma15228247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/27/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
Optical synapse is the basic component for optical neuromorphic computing and is attracting great attention, mainly due to its great potential in many fields, such as image recognition, artificial intelligence and artificial visual perception systems. However, optical synapse with infrared (IR) response has rarely been reported. InAs nanowires (NWs) have a direct narrow bandgap and a large surface to volume ratio, making them a promising material for IR detection. Here, we demonstrate a near-infrared (NIR) (750 to 1550 nm) optical synapse for the first time based on a poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE))-coated InAs NW field-effect transistor (FET). The responsivity of the P(VDF-TrFE)-coated InAs NW FET reaches 839.3 A/W under 750 nm laser illumination, demonstrating the advantage of P(VDF-TrFE) coverage. The P(VDF-TrFE)-coated InAs NW device exhibits optical synaptic behaviors in response to NIR light pulses, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF) and a transformation from short-term plasticity (STP) to long-term plasticity (LTP). The working mechanism is attributed to the polarization effect in the ferroelectric P(VDF-TrFE) layer, which dominates the trapping and de-trapping characteristics of photogenerated holes. These findings have significant implications for the development of artificial neural networks.
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Affiliation(s)
- Rui Shen
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
| | - Yifan Jiang
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
| | - Zhiwei Li
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
| | - Jiamin Tian
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
| | - Shuo Li
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
| | - Tong Li
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Qing Chen
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
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Liu F, Deswal S, Christou A, Sandamirskaya Y, Kaboli M, Dahiya R. Neuro-inspired electronic skin for robots. Sci Robot 2022; 7:eabl7344. [PMID: 35675450 DOI: 10.1126/scirobotics.abl7344] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Touch is a complex sensing modality owing to large number of receptors (mechano, thermal, pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather and encode the large tactile data, allowing us to feel and perceive the real world. This efficient somatosensation far outperforms the touch-sensing capability of most of the state-of-the-art robots today and suggests the need for neural-like hardware for electronic skin (e-skin). This could be attained through either innovative schemes for developing distributed electronics or repurposing the neuromorphic circuits developed for other sensory modalities such as vision and audio. This Review highlights the hardware implementations of various computational building blocks for e-skin and the ways they can be integrated to potentially realize human skin-like or peripheral nervous system-like functionalities. The neural-like sensing and data processing are discussed along with various algorithms and hardware architectures. The integration of ultrathin neuromorphic chips for local computation and the printed electronics on soft substrate used for the development of e-skin over large areas are expected to advance robotic interaction as well as open new avenues for research in medical instrumentation, wearables, electronics, and neuroprosthetics.
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Affiliation(s)
- Fengyuan Liu
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Sweety Deswal
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Adamos Christou
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | | | - Mohsen Kaboli
- Department of Research, New Technologies, Innovation, BMW Group, Parkring 19, 85748 Garching bei Munchen, Germany.,Cognitive Robotics and Tactile Intelligence Group, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Ravinder Dahiya
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
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