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Deng Y, Ruan H, He S, Yang T, Guo D. A Biomimetic Visual Detection Model: Event-Driven LGMDs Implemented With Fractional Spiking Neuron Circuits. IEEE Trans Biomed Eng 2024; 71:2978-2990. [PMID: 38787675 DOI: 10.1109/tbme.2024.3404976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
OBJECTIVE Lobula giant motion detectors (LGMDs) in locusts effectively predict collisions and trigger avoidance, with potential applications in autonomous driving and UAVs. Research on LGMD characteristics splits into two views: one focusing on the presynaptic visual pathway, the other on the postsynaptic LGMD neurons. Both perspectives have support, leading to two computational models, but they lack a biophysical description of the individual LGMD neuron behavior. This paper aims to mimic and explain LGMD behavior based on fractional spiking neurons (FSNs) and construct a biomimetic visual model for the LGMD compatible with these characteristics. METHODS We implement the visual model using an event camera to simulate photoreceptors and follow the ON/OFF visual pathway, incorporating lateral inhibition to mimic the LGMD system from the bottom up. Second, most computational models of motion perception use only the dendrites within the LGMD neurons as the ideal pathway for linear summation, ignoring dendritic effects inducing neuronal properties. Thus, we introduce FSN circuits by altering dendritic morphological parameters to simulate multi-scale spike frequency adaptation (SFA) observed in LGMDs. Additionally, we add one more circuit of dendritic trees into the FSNs to be compatible with the postsynaptic feed-forward inhibition (FFI) in LGMD neurons, providing a novel explanatory and predictive model. RESULTS We test that the event-driven biomimetic visual model can achieve collision detection and looming selection in different complex scenes, especially fast-moving objects.
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Hu L, Li Z, Shao J, Cheng P, Wang J, Vasilakos AV, Zhang L, Chai Y, Ye Z, Zhuge F. Electronically Reconfigurable Memristive Neuron Capable of Operating in Both Excitation and Inhibition Modes. NANO LETTERS 2024; 24:10865-10873. [PMID: 39142648 PMCID: PMC11378334 DOI: 10.1021/acs.nanolett.4c02470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Threshold switching (TS) memristors are promising candidates for artificial neurons in neuromorphic systems. However, they often lack biological plausibility, typically functioning solely in an excitation mode. The absence of an inhibitory mode limits neurons' ability to synergistically process both excitatory and inhibitory synaptic signals. To address this limitation, we propose a novel memristive neuron capable of operating in both excitation and inhibition modes. The memristor's threshold voltage can be reversibly tuned using voltages of different polarities because of its bipolar TS behavior, enabling the device to function as an electronically reconfigurable bi-mode neuron. A variety of neuronal activities such as all-or-nothing behavior and tunable firing probability are mimicked under both excitatory and inhibitory stimuli. Furthermore, we develop a self-adaptive neuromorphic vision sensor based on bi-mode neurons, demonstrating effective object recognition in varied lighting conditions. Thus, our bi-mode neuron offers a versatile platform for constructing neuromorphic systems with rich functionality.
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Affiliation(s)
- Lingxiang Hu
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Zongxiao Li
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Jiale Shao
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Peihong Cheng
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo 315211, China
| | - Jingrui Wang
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo 315211, China
| | | | - Li Zhang
- Healthcare Engineering Centre, School of Engineering, Temasek Polytechnic, Tampines Avenue, 529757, Singapore
| | - Yang Chai
- Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong 999077, China
| | - Zhizhen Ye
- Institute of Wenzhou, Zhejiang University, Wenzhou 325006, China
- State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Fei Zhuge
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Institute of Wenzhou, Zhejiang University, Wenzhou 325006, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200072, China
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Shan X, Wang Z, Xie J, Han J, Tao Y, Lin Y, Zhao X, Ielmini D, Liu Y, Xu H. Hemispherical Retina Emulated by Plasmonic Optoelectronic Memristors with All-Optical Modulation for Neuromorphic Stereo Vision. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2405160. [PMID: 39049682 PMCID: PMC11423217 DOI: 10.1002/advs.202405160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Indexed: 07/27/2024]
Abstract
Binocular stereo vision relies on imaging disparity between two hemispherical retinas, which is essential to acquire image information in three dimensional environment. Therefore, retinomorphic electronics with structural and functional similarities to biological eyes are always highly desired to develop stereo vision perception system. In this work, a hemispherical optoelectronic memristor array based on Ag-TiO2 nanoclusters/sodium alginate film is developed to realize binocular stereo vision. All-optical modulation induced by plasmonic thermal effect and optical excitation in Ag-TiO2 nanoclusters is exploited to realize in-pixel image sensing and storage. Wide field of view (FOV) and spatial angle detection are experimentally demonstrated owing to the device arrangement and incident-angle-dependent characteristics in hemispherical geometry. Furthermore, depth perception and motion detection based on binocular disparity have been realized by constructing two retinomorphic memristive arrays. The results demonstrated in this work provide a promising strategy to develop all-optically controlled memristor and promote the future development of binocular vision system with in-sensor architecture.
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Affiliation(s)
- Xuanyu Shan
- Key Laboratory for UV Light‐Emitting Materials and Technology of Ministry of EducationNortheast Normal University5268 Renmin StreetChangchun130024China
| | - Zhongqiang Wang
- Key Laboratory for UV Light‐Emitting Materials and Technology of Ministry of EducationNortheast Normal University5268 Renmin StreetChangchun130024China
| | - Jun Xie
- Key Laboratory for UV Light‐Emitting Materials and Technology of Ministry of EducationNortheast Normal University5268 Renmin StreetChangchun130024China
| | - Jiaqi Han
- Key Laboratory for UV Light‐Emitting Materials and Technology of Ministry of EducationNortheast Normal University5268 Renmin StreetChangchun130024China
| | - Ye Tao
- Key Laboratory for UV Light‐Emitting Materials and Technology of Ministry of EducationNortheast Normal University5268 Renmin StreetChangchun130024China
| | - Ya Lin
- Key Laboratory for UV Light‐Emitting Materials and Technology of Ministry of EducationNortheast Normal University5268 Renmin StreetChangchun130024China
| | - Xiaoning Zhao
- Key Laboratory for UV Light‐Emitting Materials and Technology of Ministry of EducationNortheast Normal University5268 Renmin StreetChangchun130024China
| | - Daniele Ielmini
- Dipartimento di ElettronicaInformazione e BioingegneriaPolitecnico di MilanoPiazza L. da Vinci 32Milano20133Italy
| | - Yichun Liu
- Key Laboratory for UV Light‐Emitting Materials and Technology of Ministry of EducationNortheast Normal University5268 Renmin StreetChangchun130024China
| | - Haiyang Xu
- Key Laboratory for UV Light‐Emitting Materials and Technology of Ministry of EducationNortheast Normal University5268 Renmin StreetChangchun130024China
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4
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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; 11:4015-4036. [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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Li Z, Li Z, Tang W, Yao J, Dou Z, Gong J, Li Y, Zhang B, Dong Y, Xia J, Sun L, Jiang P, Cao X, Yang R, Miao X, Yang R. Crossmodal sensory neurons based on high-performance flexible memristors for human-machine in-sensor computing system. Nat Commun 2024; 15:7275. [PMID: 39179548 PMCID: PMC11344147 DOI: 10.1038/s41467-024-51609-x] [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: 03/05/2024] [Accepted: 08/13/2024] [Indexed: 08/26/2024] Open
Abstract
Constructing crossmodal in-sensor processing system based on high-performance flexible devices is of great significance for the development of wearable human-machine interfaces. A bio-inspired crossmodal in-sensor computing system can perform real-time energy-efficient processing of multimodal signals, alleviating data conversion and transmission between different modules in conventional chips. Here, we report a bio-inspired crossmodal spiking sensory neuron (CSSN) based on a flexible VO2 memristor, and demonstrate a crossmodal in-sensor encoding and computing system for wearable human-machine interfaces. We demonstrate excellent performance in the VO2 memristor including endurance (>1012), uniformity (0.72% for cycle-to-cycle variations and 3.73% for device-to-device variations), speed (<30 ns), and flexibility (bendable to a curvature radius of 1 mm). A flexible hardware processing system is implemented based on the CSSN, which can directly perceive and encode pressure and temperature bimodal information into spikes, and then enables the real-time haptic-feedback for human-machine interaction. We successfully construct a crossmodal in-sensor spiking reservoir computing system via the CSSNs, which can achieve dynamic objects identification with a high accuracy of 98.1% and real-time signal feedback. This work provides a feasible approach for constructing flexible bio-inspired crossmodal in-sensor computing systems for wearable human-machine interfaces.
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Affiliation(s)
- Zhiyuan Li
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
| | - Zhongshao Li
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, China
| | - Wei Tang
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China
| | - Jiaping Yao
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China
| | - Zhipeng Dou
- State Key Laboratory of Catalysis, CAS Center for Excellence in Nanoscience, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Junjie Gong
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China
| | - Yongfei Li
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China
| | - Beining Zhang
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China
| | - Yunxiao Dong
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China
| | - Jian Xia
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China
| | - Lin Sun
- State Key Laboratory of Catalysis, CAS Center for Excellence in Nanoscience, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Peng Jiang
- State Key Laboratory of Catalysis, CAS Center for Excellence in Nanoscience, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Xun Cao
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai, China.
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, China.
| | - Rui Yang
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China.
- Hubei Yangtze Memory Laboratories, Wuhan, China.
| | - Xiangshui Miao
- School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China.
- Hubei Yangtze Memory Laboratories, Wuhan, China.
| | - Ronggui Yang
- State Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, China
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Li H, Li Q, Sun T, Zhou Y, Han ST. Recent advances in artificial neuromorphic applications based on perovskite composites. MATERIALS HORIZONS 2024. [PMID: 39140168 DOI: 10.1039/d4mh00574k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
High-performance perovskite materials with excellent physical, electronic, and optical properties play a significant role in artificial neuromorphic devices. However, the development of perovskites in microelectronics is inevitably hindered by their intrinsic non-ideal properties, such as high defect density, environmental sensitivity, and toxicity. By leveraging materials engineering, integrating various materials with perovskites to leverage their mutual strengths presents great potential to enhance ion migration, energy level alignment, photoresponsivity, and surface passivation, thereby advancing optoelectronic and neuromorphic device development. This review initially provides an overview of perovskite materials across different dimensions, highlighting their physical properties and detailing their applications and metrics in two- and three-terminal devices. Subsequently, we comprehensively summarize the application of perovskites in combination with other materials, including organics, nanomaterials, oxides, ferroelectrics, and crystalline porous materials (CPMs), to develop advanced devices such as memristors, transistors, photodetectors, sensors, light-emitting diodes (LEDs), and artificial neuromorphic systems. Lastly, we outline the challenges and future research directions in synthesizing perovskite composites for neuromorphic devices. Through the review and analysis, we aim to broaden the utilization of perovskites and their composites in neuromorphic research, offering new insights and approaches for grasping the intricate physical working mechanisms and functionalities of perovskites.
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Affiliation(s)
- Huaxin Li
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, P. R. China
| | - Qingxiu Li
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, P. R. China
| | - Tao Sun
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen 518060, P. R. China
| | - Su-Ting Han
- Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, P. R. China.
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Lv Z, Zhu S, Wang Y, Ren Y, Luo M, Wang H, Zhang G, Zhai Y, Zhao S, Zhou Y, Jiang M, Leng YB, Han ST. Development of Bio-Voltage Operated Humidity-Sensory Neurons Comprising Self-Assembled Peptide Memristors. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2405145. [PMID: 38877385 DOI: 10.1002/adma.202405145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 06/11/2024] [Indexed: 06/16/2024]
Abstract
Biomimetic humidity sensors offer a low-power approach for respiratory monitoring in early lung-disease diagnosis. However, balancing miniaturization and energy efficiency remains challenging. This study addresses this issue by introducing a bioinspired humidity-sensing neuron comprising a self-assembled peptide nanowire (NW) memristor with unique proton-coupled ion transport. The proposed neuron shows a low Ag+ activation energy owing to the NW and redox activity of the tyrosine (Tyr)-rich peptide in the system, facilitating ultralow electric-field-driven threshold switching and a high energy efficiency. Additionally, Ag+ migration in the system can be controlled by a proton source owing to the hydrophilic nature of the phenolic hydroxyl group in Tyr, enabling the humidity-based control of the conductance state of the memristor. Furthermore, a memristor-based neuromorphic perception neuron that can encode humidity signals into spikes is proposed. The spiking characteristics of this neuron can be modulated to emulate the strength-modulated spike-frequency characteristics of biological neurons. A three-layer spiking neural network with input neurons comprising these highly tunable humidity perception neurons shows an accuracy of 92.68% in lung-disease diagnosis. This study paves the way for developing bioinspired self-assembly strategies to construct neuromorphic perception systems, bridging the gap between artificial and biological sensing and processing paradigms.
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Affiliation(s)
- Ziyu Lv
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Shirui Zhu
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Yan Wang
- School of Microelectronics, Hefei University of Technology, Hefei, 230009, P. R. China
| | - Yanyun Ren
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China
| | - Mingtao Luo
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Hanning Wang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Guohua Zhang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Yongbiao Zhai
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Shilong Zhao
- School of Electronic Information Engineering, Foshan University, Foshan, 528000, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Minghao Jiang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Yan-Bing Leng
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Su-Ting Han
- Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Kowloon, Hong Kong, 999077, P. R. China
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Shi J, Lin Y, Wang Z, Shan X, Tao Y, Zhao X, Xu H, Liu Y. Adaptive Processing Enabled by Sodium Alginate Based Complementary Memristor for Neuromorphic Sensory System. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2314156. [PMID: 38822705 DOI: 10.1002/adma.202314156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 05/20/2024] [Indexed: 06/03/2024]
Abstract
Adaptive processing allows sensory systems to autonomically adjust their sensitivity with exposure to a constant sensory stimulus and thus organisms to adapt to environmental variations. Bioinspired electronics with adaptive functions are highly desirable for the development of neuromorphic sensory systems (NSSs). Herein, the functions of desensitization and sensitivity changing with background intensity (i.e., Weber's law), as two fundamental cues of sensory adaptation, are biorealistically demonstrated in an Ag nanowire (NW)-embedded sodium alginate (SA) based complementary memristor. In particular, Weber's law is experimentally emulated in a single complementary memristor. Furthermore, three types of adaptive NSS unit are constructed to realize a multiple perceptual capability that processes the stimuli of illuminance, temperature, and pressure signals. Taking neuromorphic vision as an example, scotopic and photopic adaptation functions are well reproduced for image enhancement against dark and bright backgrounds. Importantly, an NSS system with multisensory integration function is demonstrated by combining light and pressure spikes, where the accuracy of pattern recognition is obviously enhanced relative to that of an individual sense. This work offers a new strategy for developing neuromorphic electronics with adaptive functions and paves the way toward developing a highly efficient NSS.
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Affiliation(s)
- Jiajuan Shi
- 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
| | - 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
| | - 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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Hu J, Jing MJ, Huang YT, Kou BH, Li Z, Xu YT, Yu SY, Zeng X, Jiang J, Lin P, Zhao WW. A Photoelectrochemical Retinomorphic Synapse. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2405887. [PMID: 39054924 DOI: 10.1002/adma.202405887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/28/2024] [Indexed: 07/27/2024]
Abstract
Reproducing human visual functions with artificial devices is a long-standing goal of the neuromorphic domain. However, emulating the chemical language communication of the visual system in fluids remains a grand challenge. Here, a "multi-color" hydrogel-based photoelectrochemical retinomorphic synapse is reported with unique chemical-ionic-electrical signaling in an aqueous electrolyte that enables, e.g., color perception and biomolecule-mediated synaptic plasticity. Based on the specific enzyme-catalyzed chromogenic reactions, three multifunctional colored hydrogels are developed, which can not only synergize with the Bi2S3 photogate to recognize the primary colors but also synergize with a given polymeric channel to promote the long-term memory of the system. A synaptic array is further constructed for sensing color images and biomolecule-coded information communication. Taking advantage of the versatile biochemistry, the biochemical-driven reversible photoelectric response of the cone cell is further mimicked. This work introduces rich chemical designs into retinomorphic devices, providing a perspective for replicating the human visual system in fluids.
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Affiliation(s)
- Jin Hu
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, P. R. China
- Guangdong Provincial Key Laboratory of New Energy Materials Service Safety, Shenzhen Key Laboratory of Special Functional Materials, College of Materials Science and Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
- State Key Laboratory of Solidification Processing, Carbon/Carbon Composites Research Center, Northwestern Polytechnical University, Xi'an, 710072, P. R. China
| | - Ming-Jian Jing
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, P. R. China
| | - Yu-Ting Huang
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, P. R. China
| | - Bo-Han Kou
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, P. R. China
| | - Zheng Li
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, P. R. China
| | - Yi-Tong Xu
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, P. R. China
| | - Si-Yuan Yu
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, P. R. China
| | - Xierong Zeng
- Guangdong Provincial Key Laboratory of New Energy Materials Service Safety, Shenzhen Key Laboratory of Special Functional Materials, College of Materials Science and Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Jie Jiang
- Hunan Key Laboratory of Nanophotonics and Devices, School of Physics and Electronics, Central South University, 932 South Lushan Road, Changsha, Hunan, 410083, P. R. China
| | - Peng Lin
- Guangdong Provincial Key Laboratory of New Energy Materials Service Safety, Shenzhen Key Laboratory of Special Functional Materials, College of Materials Science and Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Wei-Wei Zhao
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, P. R. China
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10
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Liao C, Liu D, Liu Z, Wang J, Xie X, Li J, Zhou G. Coexistance of the Negative Photoconductance Effect and Analogue Switching Memory in the CuPc Organic Memristor for Neuromorphic Vision Computing. J Phys Chem Lett 2024; 15:6230-6236. [PMID: 38840314 DOI: 10.1021/acs.jpclett.4c01071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
A bioinspired in-sensing computing paradigm using emerging photoelectronic memristors pursues multifunctionality with low power consumption and high efficiency for processing large amounts of sensing information. An organic semiconductor memristor strategy based on the CuPc functional layer integrates a negative photoconductance (NPC) effect and an analogue switching memory (ASM) effect in the same pixel. The NPC effect, present in the pure capacitance state at low bias voltage, provides high-performance short/long-term synaptic plasticity modulable by light pulse parameters. The interface charge effect along with defeat site trapping and detrapping is responsible for the pure capacitance effect and the NPC effect, with electron tunneling and electric-field-driven band dynamics responsible for ASM. This work reveals an organic memristor approach for hardware implementation of a neuromorphic vision computing system, emulating retinal bipolar cells via light-dominated NPC and electrically induced ASM with stable, tunable conductance states.
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Affiliation(s)
- Changrong Liao
- School of Electronic Information and Electrical Engineering, Chongqing University of Arts and Sciences, Chongqing 402160, People's Republic of China
| | - Dong Liu
- School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Zheng Liu
- College of Artificial Intelligence, Southwest University, Chongqing 400715, People's Republic of China
| | - Jinchengyan Wang
- College of Artificial Intelligence, Southwest University, Chongqing 400715, People's Republic of China
| | - Xuesen Xie
- College of Artificial Intelligence, Southwest University, Chongqing 400715, People's Republic of China
| | - Jie Li
- College of Artificial Intelligence, Southwest University, Chongqing 400715, People's Republic of China
| | - Guangdong Zhou
- College of Artificial Intelligence, Southwest University, Chongqing 400715, People's Republic of China
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11
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Li F, Li D, Wang C, Liu G, Wang R, Ren H, Tang Y, Wang Y, Chen Y, Liang K, Huang Q, Sawan M, Qiu M, Wang H, Zhu B. An artificial visual neuron with multiplexed rate and time-to-first-spike coding. Nat Commun 2024; 15:3689. [PMID: 38693165 PMCID: PMC11063071 DOI: 10.1038/s41467-024-48103-9] [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: 10/02/2023] [Accepted: 04/17/2024] [Indexed: 05/03/2024] Open
Abstract
Human visual neurons rely on event-driven, energy-efficient spikes for communication, while silicon image sensors do not. The energy-budget mismatch between biological systems and machine vision technology has inspired the development of artificial visual neurons for use in spiking neural network (SNN). However, the lack of multiplexed data coding schemes reduces the ability of artificial visual neurons in SNN to emulate the visual perception ability of biological systems. Here, we present an artificial visual spiking neuron that enables rate and temporal fusion (RTF) coding of external visual information. The artificial neuron can code visual information at different spiking frequencies (rate coding) and enables precise and energy-efficient time-to-first-spike (TTFS) coding. This multiplexed sensory coding scheme could improve the computing capability and efficacy of artificial visual neurons. A hardware-based SNN with the RTF coding scheme exhibits good consistency with real-world ground truth data and achieves highly accurate steering and speed predictions for self-driving vehicles in complex conditions. The multiplexed RTF coding scheme demonstrates the feasibility of developing highly efficient spike-based neuromorphic hardware.
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Affiliation(s)
- Fanfan Li
- School of Materials Science and Engineering, Zhejiang University, Hangzhou, China
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Dingwei Li
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Chuanqing Wang
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, China
| | - Guolei Liu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Rui Wang
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an, China
| | - Huihui Ren
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Yingjie Tang
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Yan Wang
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Yitong Chen
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Kun Liang
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
| | - Qi Huang
- Westlake Institute for Optoelectronics, Westlake University, Hangzhou, China
| | - Mohamad Sawan
- CenBRAIN Neurotech, School of Engineering, Westlake University, Hangzhou, China
- Westlake Institute for Optoelectronics, Westlake University, Hangzhou, China
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Min Qiu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China
- Westlake Institute for Optoelectronics, Westlake University, Hangzhou, China
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Hong Wang
- Key Laboratory of Wide Band Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an, China.
| | - Bowen Zhu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China.
- Westlake Institute for Optoelectronics, Westlake University, Hangzhou, China.
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China.
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12
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Liu X, Dai S, Zhao W, Zhang J, Guo Z, Wu Y, Xu Y, Sun T, Li L, Guo P, Yang J, Hu H, Zhou J, Zhou P, Huang J. All-Photolithography Fabrication of Ion-Gated Flexible Organic Transistor Array for Multimode Neuromorphic Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2312473. [PMID: 38385598 DOI: 10.1002/adma.202312473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/17/2024] [Indexed: 02/23/2024]
Abstract
Organic ion-gated transistors (OIGTs) demonstrate commendable performance for versatile neuromorphic systems. However, due to the fragility of organic materials to organic solvents, efficient and reliable all-photolithography methods for scalable manufacturing of high-density OIGT arrays with multimode neuromorphic functions are still missing, especially when all active layers are patterned in high-density. Here, a flexible high-density (9662 devices per cm2) OIGT array with high yield and minimal device-to-device variation is fabricated by a modified all-photolithography method. The unencapsulated flexible array can withstand 1000 times' bending at a radius of 1 mm, and 3 months' storage test in air, without obvious performance degradation. More interesting, the OIGTs can be configured between volatile and nonvolatile modes, suitable for constructing reservoir computing systems to achieve high accuracy in classifying handwritten digits with low training costs. This work proposes a promising design of organic and flexible electronics for affordable neuromorphic systems, encompassing both array and algorithm aspects.
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Affiliation(s)
- Xu Liu
- 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
| | - Weidong Zhao
- School of Electronic and Information Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Junyao Zhang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Ziyi Guo
- 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
| | - Yutong Xu
- 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
| | - Li Li
- 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
| | - Jie Yang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Huawei Hu
- State Key Laboratory for Modification of Chemical Fibers and Polymer Materials, College of Materials Science and Engineering, Donghua University, Shanghai, 201620, P. R. China
| | - Junhe Zhou
- School of Electronic and Information Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Peng Zhou
- State Key Laboratory of ASIC and System, School of Microelectronics, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 200433, P. R. China
| | - Jia Huang
- School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
- National Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai, 201804, P. R. China
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13
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Chen J, Liu X, Liu C, Tang L, Bu T, Jiang B, Qing Y, Xie Y, Wang Y, Shan Y, Li R, Ye C, Liao L. Reconfigurable Ag/HfO 2/NiO/Pt Memristors with Stable Synchronous Synaptic and Neuronal Functions for Renewable Homogeneous Neuromorphic Computing System. NANO LETTERS 2024; 24:5371-5378. [PMID: 38647348 DOI: 10.1021/acs.nanolett.4c01319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Artificial synapses and bionic neurons offer great potential in highly efficient computing paradigms. However, complex requirements for specific electronic devices in neuromorphic computing have made memristors face the challenge of process simplification and universality. Herein, reconfigurable Ag/HfO2/NiO/Pt memristors are designed for feasible switching between volatile and nonvolatile modes by compliance current controlled Ag filaments, which enables stable and reconfigurable synaptic and neuronal functions. A neuromorphic computing system effectively replicates the biological synaptic weight alteration and continuously accomplishes excitation and reset of artificial neurons, which consist of bionic synapses and artificial neurons based on isotype Ag/HfO2/NiO/Pt memristors. This reconfigurable electrical performance of the Ag/HfO2/NiO/Pt memristors takes advantage of simplified hardware design and delivers integrated circuits with high density, which exhibits great potency for future neural networks.
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Affiliation(s)
- Jiaqi Chen
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Xingqiang Liu
- Changsha Semiconductor Technology and Application Research Institute, Engineering Research Center of Advanced Semiconductor Technology, College of Semiconductor (College of Integrated Circuit), Hunan University, Changsha 410082, China
| | - Chang Liu
- Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices, School of Physics and Electronics, Hunan University, Changsha 410082, China
| | - Lin Tang
- Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices, School of Physics and Electronics, Hunan University, Changsha 410082, China
| | - Tong Bu
- Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices, School of Physics and Electronics, Hunan University, Changsha 410082, China
| | - Bei Jiang
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
- Changsha Semiconductor Technology and Application Research Institute, Engineering Research Center of Advanced Semiconductor Technology, College of Semiconductor (College of Integrated Circuit), Hunan University, Changsha 410082, China
| | - Yahui Qing
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Yulu Xie
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Yong Wang
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Yongtao Shan
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Ruxin Li
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Cong Ye
- Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, School of Microelectronics, Hubei University, Wuhan 430062, China
| | - Lei Liao
- Changsha Semiconductor Technology and Application Research Institute, Engineering Research Center of Advanced Semiconductor Technology, College of Semiconductor (College of Integrated Circuit), Hunan University, Changsha 410082, China
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14
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Song H, Lee MG, Kim G, Kim DH, Kim G, Park W, Rhee H, In JH, Kim KM. Fully Memristive Elementary Motion Detectors for a Maneuver Prediction. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2309708. [PMID: 38251443 DOI: 10.1002/adma.202309708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/09/2024] [Indexed: 01/23/2024]
Abstract
Insects can efficiently perform object motion detection via a specialized neural circuit, called an elementary motion detector (EMD). In contrast, conventional machine vision systems require significant computational resources for dynamic motion processing. Here, a fully memristive EMD (M-EMD) is presented that implements the Hassenstein-Reichardt (HR) correlator, a biological model of the EMD. The M-EMD consists of a simple Wye (Y) configuration, including a static resistor, a dynamic memristor, and a Mott memristor. The resistor and dynamic memristor introduce different signal delays, enabling spatio-temporal signal integration in the subsequent Mott memristor, resulting in a direction-selective response. In addition, a neuromorphic system is developed employing the M-EMDs to predict a lane-changing maneuver by vehicles on the road. The system achieved a high accuracy (> 87%) in predicting future lane-changing maneuvers on the Next Generation Simulation (NGSIM) dataset while reducing the computational cost by 92.9% compared to the conventional neuromorphic system without the M-EMD, suggesting its strong potential for edge-level computing.
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Affiliation(s)
- Hanchan Song
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Min Gu Lee
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Gwangmin Kim
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Do Hoon Kim
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Geunyoung Kim
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Woojoon Park
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Hakseung Rhee
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Jae Hyun In
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Kyung Min Kim
- Department of Materials Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
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15
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Luo X, Chen C, He Z, Wang M, Pan K, Dong X, Li Z, Liu B, Zhang Z, Wu Y, Ban C, Chen R, Zhang D, Wang K, Wang Q, Li J, Lu G, Liu J, Liu Z, Huang W. A bionic self-driven retinomorphic eye with ionogel photosynaptic retina. Nat Commun 2024; 15:3086. [PMID: 38600063 PMCID: PMC11006927 DOI: 10.1038/s41467-024-47374-6] [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: 09/26/2023] [Accepted: 03/27/2024] [Indexed: 04/12/2024] Open
Abstract
Bioinspired bionic eyes should be self-driving, repairable and conformal to arbitrary geometries. Such eye would enable wide-field detection and efficient visual signal processing without requiring external energy, along with retinal transplantation by replacing dysfunctional photoreceptors with healthy ones for vision restoration. A variety of artificial eyes have been constructed with hemispherical silicon, perovskite and heterostructure photoreceptors, but creating zero-powered retinomorphic system with transplantable conformal features remains elusive. By combining neuromorphic principle with retinal and ionoelastomer engineering, we demonstrate a self-driven hemispherical retinomorphic eye with elastomeric retina made of ionogel heterojunction as photoreceptors. The receptor driven by photothermoelectric effect shows photoperception with broadband light detection (365 to 970 nm), wide field-of-view (180°) and photosynaptic (paired-pulse facilitation index, 153%) behaviors for biosimilar visual learning. The retinal photoreceptors are transplantable and conformal to any complex surface, enabling visual restoration for dynamic optical imaging and motion tracking.
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Affiliation(s)
- Xu Luo
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Chen Chen
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Zixi He
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Min Wang
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Keyuan Pan
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Xuemei Dong
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Zifan Li
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Bin Liu
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Zicheng Zhang
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Yueyue Wu
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Chaoyi Ban
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Rong Chen
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Dengfeng Zhang
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Kaili Wang
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Qiye Wang
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Junyue Li
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Gang Lu
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China
| | - Juqing Liu
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China.
| | - Zhengdong Liu
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China.
| | - Wei Huang
- Key Laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM), School of Flexible Electronics (Future Technologies), Nanjing Tech University (NanjingTech), Nanjing, China.
- Frontiers Science Center for Flexible Electronics, Institute of Flexible Electronics (IFE), Northwestern Polytechnical University, Xi'an, China.
- State Key Laboratory of Organic Electronics and Information Displays, Nanjing University of Posts and Telecommunications, Nanjing, China.
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16
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Jiang C, Xu H, Yang L, Liu J, Li Y, Takei K, Xu W. Neuromorphic antennal sensory system. Nat Commun 2024; 15:2109. [PMID: 38453967 PMCID: PMC10920631 DOI: 10.1038/s41467-024-46393-7] [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: 10/16/2023] [Accepted: 02/26/2024] [Indexed: 03/09/2024] Open
Abstract
Insect antennae facilitate the nuanced detection of vibrations and deflections, and the non-contact perception of magnetic or chemical stimuli, capabilities not found in mammalian skin. Here, we report a neuromorphic antennal sensory system that emulates the structural, functional, and neuronal characteristics of ant antennae. Our system comprises electronic antennae sensor with three-dimensional flexible structures that detects tactile and magnetic stimuli. The integration of artificial synaptic devices adsorbed with solution-processable MoS2 nanoflakes enables synaptic processing of sensory information. By emulating the architecture of receptor-neuron pathway, our system realizes hardware-level, spatiotemporal perception of tactile contact, surface pattern, and magnetic field (detection limits: 1.3 mN, 50 μm, 9.4 mT). Vibrotactile-perception tasks involving profile and texture classifications were accomplished with high accuracy (> 90%), surpassing human performance in "blind" tactile explorations. Magneto-perception tasks including magnetic navigation and touchless interaction were successfully completed. Our work represents a milestone for neuromorphic sensory systems and biomimetic perceptual intelligence.
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Affiliation(s)
- Chengpeng Jiang
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, China
- Shenzhen Research Institute of Nankai University, Shenzhen, China
| | - Honghuan Xu
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, China
- Shenzhen Research Institute of Nankai University, Shenzhen, China
| | - Lu Yang
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, China
- Shenzhen Research Institute of Nankai University, Shenzhen, China
| | - Jiaqi Liu
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, China
- Shenzhen Research Institute of Nankai University, Shenzhen, China
| | - Yue Li
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, China
- Shenzhen Research Institute of Nankai University, Shenzhen, China
| | - Kuniharu Takei
- Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan.
| | - Wentao Xu
- Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Engineering Research Center of Thin Film Photoelectronic Technology of Ministry of Education, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, China.
- Shenzhen Research Institute of Nankai University, Shenzhen, China.
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17
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Wang J, Ilyas N, Ren Y, Ji Y, Li S, Li C, Liu F, Gu D, Ang KW. Technology and Integration Roadmap for Optoelectronic Memristor. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307393. [PMID: 37739413 DOI: 10.1002/adma.202307393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/10/2023] [Indexed: 09/24/2023]
Abstract
Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.
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Affiliation(s)
- Jinyong Wang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Nasir Ilyas
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Yujing Ren
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, 117585, Singapore
| | - Yun Ji
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Changcun Li
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Fucai Liu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Deen Gu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
- Institute of Materials Research and Engineering, A*STAR, Singapore, 138634, Singapore
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18
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Jing X, Li S, Zhu R, Ning X, Lin J. Miniature bioinspired artificial compound eyes: microfabrication technologies, photodetection and applications. Front Bioeng Biotechnol 2024; 12:1342120. [PMID: 38433824 PMCID: PMC10905626 DOI: 10.3389/fbioe.2024.1342120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/11/2024] [Indexed: 03/05/2024] Open
Abstract
As an outstanding visual system for insects and crustaceans to cope with the challenges of survival, compound eye has many unique advantages, such as wide field of view, rapid response, infinite depth of field, low aberration and fast motion capture. However, the complex composition of their optical systems also presents significant challenges for manufacturing. With the continuous development of advanced materials, complex 3D manufacturing technologies and flexible electronic detectors, various ingenious and sophisticated compound eye imaging systems have been developed. This paper provides a comprehensive review on the microfabrication technologies, photoelectric detection and functional applications of miniature artificial compound eyes. Firstly, a brief introduction to the types and structural composition of compound eyes in the natural world is provided. Secondly, the 3D forming manufacturing techniques for miniature compound eyes are discussed. Subsequently, some photodetection technologies for miniature curved compound eye imaging are introduced. Lastly, with reference to the existing prototypes of functional applications for miniature compound eyes, the future development of compound eyes is prospected.
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Affiliation(s)
- Xian Jing
- College of Electronic Science and Engineering, Jilin University, Changchun, China
- Jilin Provincial Key Laboratory of Micro/Nano and Ultra-precision Manufacturing, School of Mechatronic Engineering, Changchun University of Technology, Changchun, China
| | - Shitao Li
- Jilin Provincial Key Laboratory of Micro/Nano and Ultra-precision Manufacturing, School of Mechatronic Engineering, Changchun University of Technology, Changchun, China
| | - Rongxin Zhu
- Jilin Provincial Key Laboratory of Micro/Nano and Ultra-precision Manufacturing, School of Mechatronic Engineering, Changchun University of Technology, Changchun, China
| | - Xiaochen Ning
- Jilin Provincial Key Laboratory of Micro/Nano and Ultra-precision Manufacturing, School of Mechatronic Engineering, Changchun University of Technology, Changchun, China
| | - Jieqiong Lin
- Jilin Provincial Key Laboratory of Micro/Nano and Ultra-precision Manufacturing, School of Mechatronic Engineering, Changchun University of Technology, Changchun, China
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19
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Leng K, Guo Z, Chen J, Fu Y, Ma R, Yu X, Wang L, Wang Q. PbS/CsPbBr 3 Heterojunction for Broadband Neuromorphic Vision Sensing. ACS APPLIED MATERIALS & INTERFACES 2024; 16:7470-7479. [PMID: 38299515 DOI: 10.1021/acsami.3c17935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2024]
Abstract
Neuromorphic light sensors with analogue-domain image processing capability hold promise for overcoming the energy efficiency limitations and latency of von Neumann architecture-based vision chips. Recently, metal halide perovskites, with strong light-matter interaction, long carrier diffusion length, and exceptional photoelectric conversion efficiencies, exhibit reconfigurable photoresponsivity due to their intrinsic ion migration effect, which is expected to advance the development of visual sensors. However, suffering from a large bandgap, it is challenging to achieve highly tunable responsivity simultaneously with a wide-spectrum response in perovskites, which will significantly enhance the image recognition accuracy through the machine learning algorithm. Herein, we demonstrate a broadband neuromorphic visual sensor from visible (Vis) to near-infrared (NIR) by coupling all-inorganic metal halide perovskites (CsPbBr3) with narrow-bandgap lead sulfide (PbS). The PbS/CsPbBr3 heterostructure is composed of high-quality single crystals of PbS and CsPbBr3. Interestingly, the ion migration of CsPbBr3 with the implementation of an electric field induces the energy band dynamic bending at the interface of the PbS/CsPbBr3 heterojunction, leading to reversible, multilevel, and linearly tunable photoresponsivity. Furthermore, the reconfigurable and broadband photoresponse in the PbS/CsPbBr3 heterojunction allows convolutional neuronal network processing for pattern recognition and edge enhancements from the Vis to the NIR waveband, suggesting the great potential of the PbS/CsPbBr3 heterostructure in artificial intelligent vision sensing.
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Affiliation(s)
- Kangmin Leng
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang 330031, China
| | - Zhiqiang Guo
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang 330031, China
| | - Junming Chen
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang 330031, China
| | - Yao Fu
- Department of Materials, School of Physics and Materials Science, Nanchang University, Nanchang 330031, China
| | - Ruihua Ma
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang 330031, China
| | - Xuechao Yu
- Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, Jiangsu, China
| | - Li Wang
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang 330031, China
| | - Qisheng Wang
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang 330031, China
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20
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Zhu S, Xie T, Lv Z, Leng YB, Zhang YQ, Xu R, Qin J, Zhou Y, Roy VAL, Han ST. Hierarchies in Visual Pathway: Functions and Inspired Artificial Vision. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2301986. [PMID: 37435995 DOI: 10.1002/adma.202301986] [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: 03/02/2023] [Revised: 06/28/2023] [Accepted: 07/10/2023] [Indexed: 07/13/2023]
Abstract
The development of artificial intelligence has posed a challenge to machine vision based on conventional complementary metal-oxide semiconductor (CMOS) circuits owing to its high latency and inefficient power consumption originating from the data shuffling between memory and computation units. Gaining more insights into the function of every part of the visual pathway for visual perception can bring the capabilities of machine vision in terms of robustness and generality. Hardware acceleration of more energy-efficient and biorealistic artificial vision highly necessitates neuromorphic devices and circuits that are able to mimic the function of each part of the visual pathway. In this paper, we review the structure and function of the entire class of visual neurons from the retina to the primate visual cortex within reach (Chapter 2) are reviewed. Based on the extraction of biological principles, the recent hardware-implemented visual neurons located in different parts of the visual pathway are discussed in detail in Chapters 3 and 4. Furthermore, valuable applications of inspired artificial vision in different scenarios (Chapter 5) are provided. The functional description of the visual pathway and its inspired neuromorphic devices/circuits are expected to provide valuable insights for the design of next-generation artificial visual perception systems.
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Affiliation(s)
- Shirui Zhu
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Tao Xie
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Ziyu Lv
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Yan-Bing Leng
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Yu-Qi Zhang
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Runze Xu
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Jingrun Qin
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Vellaisamy A L Roy
- School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, 999077, P. R. China
| | - Su-Ting Han
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
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21
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Xu K, Peng B, Mao H, Wang Z, Gong H, Fu C, Ren FF, Yang Y, Wan C, Wan Q, Ye J. Ga 2O 3 Bipolar Heterojunction-Based Optoelectronic Synapse Array with Visual Attention. J Phys Chem Lett 2024; 15:556-564. [PMID: 38198134 DOI: 10.1021/acs.jpclett.3c02898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
The human brain efficiently processes only a fraction of visual information, a phenomenon termed attentional control, resulting in energy savings and heightened adaptability. Translating this mechanism into artificial visual neurons holds promise for constructing energy-efficient, bioinspired visual systems. Here, we propose a self-rectifying artificial visual neuron (SEVN) based on a NiO/Ga2O3 bipolar heterojunction with attentional control on patterns with a target color. The device exhibits short-term potentiation (STP) with quantum point contact (QPC) traits at low bias and transitions to long-term potentiation (LTP) at high bias, particularly facilitated by electron capture in deep defects upon ultraviolet (UV) exposure. With the utilization of two wavelengths of light upon the target and interference part of CAPTCHA to simulate top-down attentional control, the recognition accuracy is enhanced from 74 to 84%. These findings have the potential to augment the visual capability of neuromorphic systems with implications for diverse applications, including cybersecurity, healthcare, and machine vision.
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Affiliation(s)
- Ke Xu
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Baocheng Peng
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Huiwu Mao
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Zhengpeng Wang
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Hehe Gong
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Chuanyu Fu
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Fang-Fang Ren
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Yi Yang
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Changjin Wan
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Qing Wan
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang 315202, People's Republic of China
| | - Jiandong Ye
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
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22
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Chen H, Cai Y, Han Y, Huang H. Towards Artificial Visual Sensory System: Organic Optoelectronic Synaptic Materials and Devices. Angew Chem Int Ed Engl 2024; 63:e202313634. [PMID: 37783656 DOI: 10.1002/anie.202313634] [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: 09/13/2023] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 10/04/2023]
Abstract
Developing an artificial visual sensory system requires optoelectronic materials and devices that can mimic the behavior of biological synapses. Organic/polymeric semiconductors have emerged as promising candidates for optoelectronic synapses due to their tunable optoelectronic properties, mechanic flexibility, and biological compatibility. In this review, we discuss the recent progress in organic optoelectronic synaptic materials and devices, including their design principles, working mechanisms, and applications. We also highlight the challenges and opportunities in this field and provide insights into potential applications of these materials and devices in next-generation artificial visual systems. By leveraging the advances in organic optoelectronic materials and devices, we can envision its future development in artificial intelligence.
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Affiliation(s)
- Hao Chen
- College of Materials Science and Opto-Electronic Technology &, Center of Materials Science and Optoelectronics Engineering &, College of Resources and Environment &, CAS Center for Excellence in Topological Quantum Computation &, CAS Key Laboratory of Vacuum Physic, University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Yunhao Cai
- College of Materials Science and Opto-Electronic Technology &, Center of Materials Science and Optoelectronics Engineering &, College of Resources and Environment &, CAS Center for Excellence in Topological Quantum Computation &, CAS Key Laboratory of Vacuum Physic, University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Yinghui Han
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Hui Huang
- College of Materials Science and Opto-Electronic Technology &, Center of Materials Science and Optoelectronics Engineering &, College of Resources and Environment &, CAS Center for Excellence in Topological Quantum Computation &, CAS Key Laboratory of Vacuum Physic, University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
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23
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Li Z, Wang J, Xu L, Wang L, Shang H, Ying H, Zhao Y, Wen L, Guo C, Zheng X. Achieving Reliable and Ultrafast Memristors via Artificial Filaments in Silk Fibroin. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2308843. [PMID: 37934889 DOI: 10.1002/adma.202308843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/28/2023] [Indexed: 11/09/2023]
Abstract
The practical implementation of memristors in neuromorphic computing and biomimetic sensing suffers from unexpected temporal and spatial variations due to the stochastic formation and rupture of conductive filaments (CFs). Here, the biocompatible silk fibroin (SF) is patterned with an on-demand nanocone array by using thermal scanning probe lithography (t-SPL) to guide and confine the growth of CFs in the silver/SF/gold (Ag/SF/Au) memristor. Benefiting from the high fabrication controllability, cycle-to-cycle (temporal) standard deviation of the set voltage for the structured memristor is significantly reduced by ≈95.5% (from 1.535 to 0.0686 V) and the device-to-device (spatial) standard deviation is also reduced to 0.0648 V. Besides, the statistical relationship between the structural nanocone design and the resultant performance is confirmed, optimizing at the small operation voltage (≈0.5 V) and current (100 nA), ultrafast switching speed (sub-100 ns), large on/off ratio (104 ), and the smallest switching slope (SS < 0.01 mV dec-1 ). Finally, the short-term plasticity and leaky integrated-and-fire behavior are emulated, and a reliable thermal nociceptor system is demonstrated for practical neuromorphic applications.
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Affiliation(s)
- Zishun Li
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China
| | - Jiaqi Wang
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China
| | - Lanxin Xu
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China
| | - Li Wang
- Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Hongpeng Shang
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China
| | - Haoting Ying
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China
| | - Yingjie Zhao
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China
| | - Liaoyong Wen
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, 310030, China
| | - Chengchen Guo
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, 310030, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, 310024, China
| | - Xiaorui Zheng
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China
- Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, 310030, China
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24
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Ren SG, Dong AW, Yang L, Xue YB, Li JC, Yu YJ, Zhou HJ, Zuo WB, Li Y, Cheng WM, Miao XS. Self-Rectifying Memristors for Three-Dimensional In-Memory Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307218. [PMID: 37972344 DOI: 10.1002/adma.202307218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/13/2023] [Indexed: 11/19/2023]
Abstract
Costly data movement in terms of time and energy in traditional von Neumann systems is exacerbated by emerging information technologies related to artificial intelligence. In-memory computing (IMC) architecture aims to address this problem. Although the IMC hardware prototype represented by a memristor is developed rapidly and performs well, the sneak path issue is a critical and unavoidable challenge prevalent in large-scale and high-density crossbar arrays, particularly in three-dimensional (3D) integration. As a perfect solution to the sneak-path issue, a self-rectifying memristor (SRM) is proposed for 3D integration because of its superior integration density. To date, SRMs have performed well in terms of power consumption (aJ level) and scalability (>102 Mbit). Moreover, SRM-configured 3D integration is considered an ideal hardware platform for 3D IMC. This review focuses on the progress in SRMs and their applications in 3D memory, IMC, neuromorphic computing, and hardware security. The advantages, disadvantages, and optimization strategies of SRMs in diverse application scenarios are illustrated. Challenges posed by physical mechanisms, fabrication processes, and peripheral circuits, as well as potential solutions at the device and system levels, are also discussed.
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Affiliation(s)
- Sheng-Guang Ren
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - A-Wei Dong
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Ling Yang
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yi-Bai Xue
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Jian-Cong Li
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yin-Jie Yu
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Hou-Ji Zhou
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Wen-Bin Zuo
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yi Li
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan, 430205, China
| | - Wei-Ming Cheng
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan, 430205, China
| | - Xiang-Shui Miao
- School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China
- Hubei Yangtze Memory Laboratories, Wuhan, 430205, China
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25
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Zhou G, Li J, Song Q, Wang L, Ren Z, Sun B, Hu X, Wang W, Xu G, Chen X, Cheng L, Zhou F, Duan S. Full hardware implementation of neuromorphic visual system based on multimodal optoelectronic resistive memory arrays for versatile image processing. Nat Commun 2023; 14:8489. [PMID: 38123562 PMCID: PMC10733375 DOI: 10.1038/s41467-023-43944-2] [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: 06/07/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
In-sensor and near-sensor computing are becoming the next-generation computing paradigm for high-density and low-power sensory processing. To fulfil a high-density and efficient neuromorphic visual system with fully hierarchical emulation of the retina and visual cortex, emerging multimodal neuromorphic devices for multi-stage processing and a fully hardware-implemented system with versatile image processing functions are still lacking and highly desirable. Here we demonstrate an emerging multimodal-multifunctional resistive random-access memory (RRAM) device array based on modified silk fibroin protein (MSFP), exhibiting both optoelectronic RRAM (ORRAM) mode featured by unique negative and positive photoconductance memory and electrical RRAM (ERRAM) mode featured by analogue resistive switching. A full hardware implementation of the artificial visual system with versatile image processing functions is realised for the first time, including ORRAM mode array for the in-sensor image pre-processing (contrast enhancement, background denoising, feature extraction) and ERRAM mode array for near-sensor high-level image recognition, which hugely improves the integration density, and simply the circuit design and the fabrication and integration complexity.
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Affiliation(s)
- Guangdong Zhou
- College of Artificial Intelligence, Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips, Key Laboratory of Luminescence Analysis and Molecular Sensors (Ministry of Education), Southwest University, Chongqing, 400715, China
| | - Jie Li
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Qunliang Song
- Faculty of Materials and Energy, Southwest University, Chongqing, 400715, China
| | - Lidan Wang
- College of Artificial Intelligence, Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips, Key Laboratory of Luminescence Analysis and Molecular Sensors (Ministry of Education), Southwest University, Chongqing, 400715, China
| | - Zhijun Ren
- College of Artificial Intelligence, Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips, Key Laboratory of Luminescence Analysis and Molecular Sensors (Ministry of Education), Southwest University, Chongqing, 400715, China
| | - Bai Sun
- Frontier Institute of Science and Technology, Xi'an Jiaotong University, Shanxi, 710049, China
| | - Xiaofang Hu
- College of Artificial Intelligence, Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips, Key Laboratory of Luminescence Analysis and Molecular Sensors (Ministry of Education), Southwest University, Chongqing, 400715, China
| | - Wenhua Wang
- Faculty of Materials and Energy, Southwest University, Chongqing, 400715, China
| | - Gaobo Xu
- Faculty of Materials and Energy, Southwest University, Chongqing, 400715, China
| | - Xiaodie Chen
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 999077, China
| | - Lan Cheng
- State Key Laboratory of Silkworm Genome, College of Sericulture, Textile and Biomass Sciences, Southwest University, Chongqing, 400715, China
| | - Feichi Zhou
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, 518055, China.
| | - Shukai Duan
- College of Artificial Intelligence, Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips, Key Laboratory of Luminescence Analysis and Molecular Sensors (Ministry of Education), Southwest University, Chongqing, 400715, China.
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26
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Li Y, Shan X, Li S, Wang J, Li Z, Wang Z, Li X, Hong W, Li M, Ma Y. Nanoarchitectonics on Electrosynthesis and Assembly of Conjugated Metallopolymers. Angew Chem Int Ed Engl 2023; 62:e202311778. [PMID: 37933712 DOI: 10.1002/anie.202311778] [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: 08/13/2023] [Revised: 11/06/2023] [Accepted: 11/07/2023] [Indexed: 11/08/2023]
Abstract
In contrast to edge-on and face-on orientations, end-on uniaxial conjugated polymers have the theoretical possibility of providing a macroscopic crystalline film. However, their fabrication is insurmountable due to sluggishly thermodynamic equilibrium states. Herein, we report the programmatic pathway to fabricate nanoarchitectonics on end-on uniaxial conjugated metallopolymers by surface-initiated simultaneous electrosynthesis and assembly. Self-assembled monolayer (SAM) with bottom-up oriented electroactive molecules as a temple allows orientation, stacking, and reactive addition of monomers triggered by switching alternative redox reactions as well as crystallization of small molecules. Repeating the same reaction can repair the unreactive site on the SAM and dynamically and statistically ensure maximum iterative coverage with ideal linear coefficients between optical or electrical responses and iterative times. The resulting nanoarchitectonics on uniaxially assembled end-on polymers over centimeter-sized areas have a subnanometer-uniform morphology and exhibit ultrahigh modulus as well as an inorganic indium tin oxides and the highest conductance among conjugated molecular monolayers. Their memristive devices provide quantitative electrical and optical responses as a function of molecular length, bias, and iterative junctions. Precise processing of nanoarchitectonics as an electrically assisted assembly or printing technique can present sophisticated optoelectric functions and dimensional batch-to-batch consistency for micro-sized organic materials and electronics.
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Affiliation(s)
- Yongfang Li
- State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun, 130012, China
- State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022, China
| | - Xuanyu Shan
- Centre for Advanced Optoelectronic Functional Materials Research, Northeast Normal University, Changchun, 130000, China
| | - Shumu Li
- Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing Mass Spectrum Center, Beijing, 100190, China
| | - Jinxin Wang
- State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022, China
| | - Zhikai Li
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, Guangdong, 518055, China
| | - Zhongqiang Wang
- Centre for Advanced Optoelectronic Functional Materials Research, Northeast Normal University, Changchun, 130000, China
| | - Xiaopeng Li
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, Guangdong, 518055, China
| | - Wenjing Hong
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Mao Li
- State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun, 130012, China
- State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022, China
| | - Yuguang Ma
- State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun, 130012, China
- State Key Laboratory of Luminescent Materials and Devices, South China University of Technology, Guangzhou, 510640, China
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27
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Wang H, Lu Y, Liu S, Yu J, Hu M, Li S, Yang R, Watanabe K, Taniguchi T, Ma Y, Miao X, Zhuge F, He Y, Zhai T. Adaptive Neural Activation and Neuromorphic Processing via Drain-Injection Threshold-Switching Float Gate Transistor Memory. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2309099. [PMID: 37953691 DOI: 10.1002/adma.202309099] [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: 09/05/2023] [Revised: 11/06/2023] [Indexed: 11/14/2023]
Abstract
Hetero-modulated neural activation is vital for adaptive information processing and learning that occurs in brain. To implement brain-inspired adaptive processing, previously various neurotransistors oriented for synaptic functions are extensively explored, however, the emulation of nonlinear neural activation and hetero-modulated behaviors are not possible due to the lack of threshold switching behavior in a conventional transistor structure. Here, a 2D van der Waals float gate transistor (FGT) that exhibits steep threshold switching behavior, and the emulation of hetero-modulated neuron functions (integrate-and-fire, sigmoid type activation) for adaptive sensory processing, are reported. Unlike conventional FGTs, the threshold switching behavior stems from impact ionization in channel and the coupled charge injection to float gate. When a threshold is met, a sub-30 mV dec-1 increase of transistor conductance by more than four orders is triggered with a typical switch time of approximately milliseconds. Essentially, by feeding light sensing signal as the modulation input, it is demonstrated that two typical tasks that rely on adaptive neural activation, including collision avoidance and adaptive visual perception, can be realized. These results may shed light on the emulation of rich hetero-modulating behaviors in biological neurons and the realization of biomimetic neuromorphic processing at low hardware cost.
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Affiliation(s)
- Han Wang
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Yuanlong Lu
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Shangbo Liu
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Jun Yu
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Man Hu
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Sainan Li
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Rui Yang
- Hubei Yangtze Memory Laboratory, School of Integrated circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Kenji Watanabe
- Research Center for Electronic and Optical Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba, 305-0044, Japan
| | - Takashi Taniguchi
- Research Center for Materials Nanoarchitectonics, National Institute for Materials Science, Namiki, Tsukuba, 305-0044, Japan
| | - Ying Ma
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Xiangshui Miao
- Hubei Yangtze Memory Laboratory, School of Integrated circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Fuwei Zhuge
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
| | - Yuhui He
- Hubei Yangtze Memory Laboratory, School of Integrated circuits, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Tianyou Zhai
- State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China
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Liu JY, Zhang XH, Fang H, Zhang SQ, Chen Y, Liao Q, Chen HM, Chen HP, Lin MJ. Novel Semiconductive Ternary Hybrid Heterostructures for Artificial Optoelectronic Synapses. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2302197. [PMID: 37403302 DOI: 10.1002/smll.202302197] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 06/12/2023] [Indexed: 07/06/2023]
Abstract
Synaptic devices that mimic biological synapses are considered as promising candidates for brain-inspired devices, offering the functionalities in neuromorphic computing. However, modulation of emerging optoelectronic synaptic devices has rarely been reported. Herein, a semiconductive ternary hybrid heterostructure is prepared with a D-D'-A configuration by introducing polyoxometalate (POM) as an additional electroactive donor (D') into a metalloviologen-based D-A framework. The obtained material features an unprecedented porous 8-connected bcu-net that accommodates nanoscale [α-SiW12 O40 ]4- counterions, displaying uncommon optoelectronic responses. Besides, the fabricated synaptic device based on this material can achieve dual-modulation of synaptic plasticity due to the synergetic effect of electron reservoir POM and photoinduced electron transfer. And it can successfully simulate learning and memory processes similar to those in biological systems. The result provides a facile and effective strategy to customize multi-modality artificial synapses in the field of crystal engineering, which opens a new direction for developing high-performance neuromorphic devices.
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Affiliation(s)
- Jing-Yan Liu
- Key Laboratory of Molecule Synthesis and Function Discovery, and Fujian Provincial Key Laboratory of Advanced Inorganic Oxygenated Materials, College of Chemistry, Fuzhou University, Fuzhou, 350116, P. R. China
| | - Xiang-Hong Zhang
- Institure of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, P. R. China
| | - Hua Fang
- Key Laboratory of Molecule Synthesis and Function Discovery, and Fujian Provincial Key Laboratory of Advanced Inorganic Oxygenated Materials, College of Chemistry, Fuzhou University, Fuzhou, 350116, P. R. China
| | - Shu-Quan Zhang
- College of Zhicheng, Fuzhou University, Fuzhou, 350002, P. R. China
| | - Yong Chen
- Key Laboratory of Molecule Synthesis and Function Discovery, and Fujian Provincial Key Laboratory of Advanced Inorganic Oxygenated Materials, College of Chemistry, Fuzhou University, Fuzhou, 350116, P. R. China
| | - Qing Liao
- College of Materials Science and Engineering, Fuzhou University, Fuzhou, 350116, P. R. China
| | - Hong-Ming Chen
- College of Materials Science and Engineering, Fuzhou University, Fuzhou, 350116, P. R. China
| | - Hui-Peng Chen
- Institure of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, P. R. China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, P. R. China
| | - Mei-Jin Lin
- Key Laboratory of Molecule Synthesis and Function Discovery, and Fujian Provincial Key Laboratory of Advanced Inorganic Oxygenated Materials, College of Chemistry, Fuzhou University, Fuzhou, 350116, P. R. China
- College of Materials Science and Engineering, Fuzhou University, Fuzhou, 350116, P. R. China
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Han MJ, Tsukruk VV. Trainable Bilingual Synaptic Functions in Bio-enabled Synaptic Transistors. ACS NANO 2023; 17:18883-18892. [PMID: 37721448 PMCID: PMC10569090 DOI: 10.1021/acsnano.3c04113] [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: 05/08/2023] [Accepted: 09/14/2023] [Indexed: 09/19/2023]
Abstract
The signal transmission of the nervous system is regulated by neurotransmitters. Depending on the type of neurotransmitter released by presynaptic neurons, neuron cells can either be excited or inhibited. Maintaining a balance between excitatory and inhibitory synaptic responses is crucial for the nervous system's versatility, elasticity, and ability to perform parallel computing. On the way to mimic the brain's versatility and plasticity traits, creating a preprogrammed balance between excitatory and inhibitory responses is required. Despite substantial efforts to investigate the balancing of the nervous system, a complex circuit configuration has been suggested to simulate the interaction between excitatory and inhibitory synapses. As a meaningful approach, an optoelectronic synapse for balancing the excitatory and inhibitory responses assisted by light mediation is proposed here by deploying humidity-sensitive chiral nematic phases of known polysaccharide cellulose nanocrystals. The environment-induced pitch tuning changes the polarization of the helicoidal organization, affording different hysteresis effects with the subsequent excitatory and inhibitory nonvolatile behavior in the bio-electrolyte-gated transistors. By applying voltage pulses combined with stimulation of chiral light, the artificial optoelectronic synapse tunes not only synaptic functions but also learning pathways and color recognition. These multifunctional bio-based synaptic field-effect transistors exhibit potential for enhanced parallel neuromorphic computing and robot vision technology.
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Affiliation(s)
- Moon Jong Han
- Department
of Electronic Engineering, Gachon University, Seongnam 13120, Republic of Korea
| | - Vladimir V. Tsukruk
- School
of Materials Science and Engineering, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
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30
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Chun SY, Song YG, Kim JE, Kwon JU, Soh K, Kwon JY, Kang CY, Yoon JH. An Artificial Olfactory System Based on a Chemi-Memristive Device. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2302219. [PMID: 37116944 DOI: 10.1002/adma.202302219] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/18/2023] [Indexed: 06/19/2023]
Abstract
Technologies based on the fusion of gas sensors and neuromorphic computing to mimic the olfactory system have immense potential. However, the implementation of neuromorphic olfactory systems remains in a state of infancy because conventional gas sensors lack the necessary functions. Therefore, this study proposes a hysteretic "chemi-memristive gas sensor" based on oxygen vacancy chemi-memristive dynamics that differ from that of conventional gas sensors. After the memristive switching operation, the redox reaction with the external gas molecules is enhanced, resulting in the generation and elimination of oxygen vacancies that induce rapid current changes. In addition, the pre-generated oxygen vacancies enhance the post-sensing properties. Therefore, fast responses, short recovery times, and hysteretic gas response are achieved by the proposed sensor at room temperature. Based on the advantageous functionality of the sensor, device-level olfactory systems that can monitor the history of input gas stimuli are experimentally demonstrated as a potential application. Moreover, analog conductance modulation induced by oxidizing and reducing gases enables the conversion of external gas stimuli into synaptic weights and hence the realization of typical synaptic functionalities without an additional device or circuit. The proposed chemi-memristive device represents an advance in the bioinspired technology adopted in creating artificial intelligence systems.
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Affiliation(s)
- Suk Yeop Chun
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, 02841, Republic of Korea
| | - Young Geun Song
- 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
- Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Jae Uk Kwon
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
- Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Keunho Soh
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
- Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Ju Young Kwon
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
| | - Chong-Yun Kang
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, 02841, Republic of Korea
| | - Jung Ho Yoon
- Electronic Materials Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02791, Republic of Korea
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31
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Dai S, Liu X, Liu Y, Xu Y, Zhang J, Wu Y, Cheng P, Xiong L, Huang J. Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2300329. [PMID: 36891745 DOI: 10.1002/adma.202300329] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/22/2023] [Indexed: 06/18/2023]
Abstract
Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic-electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron-based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low-level and high-level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.
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Affiliation(s)
- Shilei Dai
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, 999077, China
| | - Xu Liu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Youdi Liu
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, State College, PA, 16802, USA
| | - Yutong Xu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Junyao Zhang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Yue Wu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
| | - Ping Cheng
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, IL, 60637, USA
| | - Lize Xiong
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
| | - Jia Huang
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, Tongji University, Shanghai, 200434, P. R. China
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai, 201804, P. R. China
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32
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Rehman S, Khan MF, Kim HD, Kim S. A self-tuning PID controller based on analog-digital hybrid computing with a double-gate SnS 2 memtransistor. NANOSCALE 2023; 15:13675-13684. [PMID: 37554054 DOI: 10.1039/d2nr06853b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/10/2023]
Abstract
Most commercial drones utilize a traditional proportional-integral-derivative (PID) controller because of its design simplicity. However, the traditional PID controller has certain limitations in terms of optimality and robustness; it is difficult to actively adjust the PID gains under some disturbances. In this study, we demonstrated an analog-digital hybrid computing platform based on double-gate SnS2 memtransistors to implement a self-tuning/energy-efficient PID controller in drones. The customized analog circuit with memtransistors executes the PID control algorithm with low power consumption; we experimentally verified that the energy consumption of the proposed hybrid computing-based PID controller is only 63% of that of the traditional PID controller. In addition, the precise tunability of analog conductance states in the memtransistor proved to be capable of reconfiguring the performance of the PID controller, where the developed self-tuning algorithm can automatically find the optimal PID control performance.
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Affiliation(s)
- Shania Rehman
- Department of Semiconductor Systems Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea.
| | | | - Hee-Dong Kim
- Department of Semiconductor Systems Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea.
| | - Sungho Kim
- Department of Semiconductor Systems Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea.
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33
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Feng X, Lv X, Dong J, Liu Y, Shu F, Wu Y. Double-Glued Multi-Focal Bionic Compound Eye Camera. MICROMACHINES 2023; 14:1548. [PMID: 37630084 PMCID: PMC10456709 DOI: 10.3390/mi14081548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 07/25/2023] [Accepted: 07/29/2023] [Indexed: 08/27/2023]
Abstract
Compound eye cameras are a vital component of bionics. Compound eye lenses are currently used in light field cameras, monitoring imaging, medical endoscopes, and other fields. However, the resolution of the compound eye lens is still low at the moment, which has an impact on the application scene. Photolithography and negative pressure molding were used to create a double-glued multi-focal bionic compound eye camera in this study. The compound eye camera has 83 microlenses, with ommatidium diameters ranging from 400 μm to 660 μm, and a 92.3 degree field-of-view angle. The double-gluing structure significantly improves the optical performance of the compound eye lens, and the spatial resolution of the ommatidium is 57.00 lp mm-1. Additionally, the measurement of speed is investigated. This double-glue compound eye camera has numerous potential applications in the military, machine vision, and other fields.
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Affiliation(s)
- Xin Feng
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (X.F.); (X.L.); (J.D.); (Y.W.)
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun 130033, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiao Lv
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (X.F.); (X.L.); (J.D.); (Y.W.)
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun 130033, China
| | - Junyu Dong
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (X.F.); (X.L.); (J.D.); (Y.W.)
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun 130033, China
| | - Yongshun Liu
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (X.F.); (X.L.); (J.D.); (Y.W.)
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun 130033, China
| | - Fengfeng Shu
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (X.F.); (X.L.); (J.D.); (Y.W.)
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun 130033, China
| | - Yihui Wu
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (X.F.); (X.L.); (J.D.); (Y.W.)
- Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun 130033, China
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34
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Wang X, Chen C, Zhu L, Shi K, Peng B, Zhu Y, Mao H, Long H, Ke S, Fu C, Zhu Y, Wan C, Wan Q. Vertically integrated spiking cone photoreceptor arrays for color perception. Nat Commun 2023; 14:3444. [PMID: 37301894 PMCID: PMC10257685 DOI: 10.1038/s41467-023-39143-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 05/31/2023] [Indexed: 06/12/2023] Open
Abstract
The cone photoreceptors in our eyes selectively transduce the natural light into spiking representations, which endows the brain with high energy-efficiency color vision. However, the cone-like device with color-selectivity and spike-encoding capability remains challenging. Here, we propose a metal oxide-based vertically integrated spiking cone photoreceptor array, which can directly transduce persistent lights into spike trains at a certain rate according to the input wavelengths. Such spiking cone photoreceptors have an ultralow power consumption of less than 400 picowatts per spike in visible light, which is very close to biological cones. In this work, lights with three wavelengths were exploited as pseudo-three-primary colors to form 'colorful' images for recognition tasks, and the device with the ability to discriminate mixed colors shows better accuracy. Our results would enable hardware spiking neural networks with biologically plausible visual perception and provide great potential for the development of dynamic vision sensors.
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Affiliation(s)
- Xiangjing Wang
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Chunsheng Chen
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Li Zhu
- College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
| | - Kailu Shi
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Baocheng Peng
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Yixin Zhu
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Huiwu Mao
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Haotian Long
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Shuo Ke
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Chuanyu Fu
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Ying Zhu
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Changjin Wan
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China.
| | - Qing Wan
- School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China.
- School of Micro Nanoelectronics, Zhejiang University, ZJU-Hangzhou Global Scientific and Technological Innovation Centre, 310027, Hangzhou, PR China.
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35
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Zhang C, Chen M, Pan Y, Li Y, Wang K, Yuan J, Sun Y, Zhang Q. Carbon Nanodots Memristor: An Emerging Candidate toward Artificial Biosynapse and Human Sensory Perception System. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2207229. [PMID: 37072642 PMCID: PMC10238223 DOI: 10.1002/advs.202207229] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 03/09/2023] [Indexed: 05/03/2023]
Abstract
In the era of big data and artificial intelligence (AI), advanced data storage and processing technologies are in urgent demand. The innovative neuromorphic algorithm and hardware based on memristor devices hold a promise to break the von Neumann bottleneck. In recent years, carbon nanodots (CDs) have emerged as a new class of nano-carbon materials, which have attracted widespread attention in the applications of chemical sensors, bioimaging, and memristors. The focus of this review is to summarize the main advances of CDs-based memristors, and their state-of-the-art applications in artificial synapses, neuromorphic computing, and human sensory perception systems. The first step is to systematically introduce the synthetic methods of CDs and their derivatives, providing instructive guidance to prepare high-quality CDs with desired properties. Then, the structure-property relationship and resistive switching mechanism of CDs-based memristors are discussed in depth. The current challenges and prospects of memristor-based artificial synapses and neuromorphic computing are also presented. Moreover, this review outlines some promising application scenarios of CDs-based memristors, including neuromorphic sensors and vision, low-energy quantum computation, and human-machine collaboration.
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Affiliation(s)
- Cheng Zhang
- Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy ApplicationSchool of Physical Science and TechnologySuzhou University of Science and TechnologySuzhouJiangsu215009China
| | - Mohan Chen
- Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy ApplicationSchool of Physical Science and TechnologySuzhou University of Science and TechnologySuzhouJiangsu215009China
| | - Yelong Pan
- Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy ApplicationSchool of Physical Science and TechnologySuzhou University of Science and TechnologySuzhouJiangsu215009China
| | - Yang Li
- Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy ApplicationSchool of Physical Science and TechnologySuzhou University of Science and TechnologySuzhouJiangsu215009China
| | - Kuaibing Wang
- Jiangsu Key Laboratory of Pesticide SciencesDepartment of ChemistryCollege of ScienceNanjing Agricultural UniversityNanjing210095China
| | - Junwei Yuan
- School of Chemistry and Life SciencesSuzhou University of Science and TechnologySuzhouJiangsu215009China
| | - Yanqiu Sun
- School of Chemistry and Life SciencesSuzhou University of Science and TechnologySuzhouJiangsu215009China
| | - Qichun Zhang
- Department of Materials Science and EngineeringDepartment of Chemistry and Center of Super‐Diamond and Advanced Films (COSDAF)City University of Hong Kong83 Tat Chee AvenueHong Kong999077China
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36
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Liu G, Lv Z, Batool S, Li MZ, Zhao P, Guo L, Wang Y, Zhou Y, Han ST. Biocompatible Material-Based Flexible Biosensors: From Materials Design to Wearable/Implantable Devices and Integrated Sensing Systems. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023:e2207879. [PMID: 37009995 DOI: 10.1002/smll.202207879] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 02/28/2023] [Indexed: 06/19/2023]
Abstract
Human beings have a greater need to pursue life and manage personal or family health in the context of the rapid growth of artificial intelligence, big data, the Internet of Things, and 5G/6G technologies. The application of micro biosensing devices is crucial in connecting technology and personalized medicine. Here, the progress and current status from biocompatible inorganic materials to organic materials and composites are reviewed and the material-to-device processing is described. Next, the operating principles of pressure, chemical, optical, and temperature sensors are dissected and the application of these flexible biosensors in wearable/implantable devices is discussed. Different biosensing systems acting in vivo and in vitro, including signal communication and energy supply are then illustrated. The potential of in-sensor computing for applications in sensing systems is also discussed. Finally, some essential needs for commercial translation are highlighted and future opportunities for flexible biosensors are considered.
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Affiliation(s)
- Gang Liu
- Institute of Microscale Optoelectronics and College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Ziyu Lv
- Institute of Microscale Optoelectronics and College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Saima Batool
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | | | - Pengfei Zhao
- Institute of Microscale Optoelectronics and College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Liangchao Guo
- College of Mechanical Engineering, Yangzhou University, Yangzhou, 225127, P. R. China
| | - Yan Wang
- School of Microelectronics, Hefei University of Technology, Hefei, 230009, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Su-Ting Han
- Institute of Microscale Optoelectronics and College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
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37
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Fu J, Wang J, He X, Ming J, Wang L, Wang Y, Shao H, Zheng C, Xie L, Ling H. Pseudo-transistors for emerging neuromorphic electronics. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2180286. [PMID: 36970452 PMCID: PMC10035954 DOI: 10.1080/14686996.2023.2180286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/15/2023] [Accepted: 02/10/2023] [Indexed: 06/18/2023]
Abstract
Artificial synaptic devices are the cornerstone of neuromorphic electronics. The development of new artificial synaptic devices and the simulation of biological synaptic computational functions are important tasks in the field of neuromorphic electronics. Although two-terminal memristors and three-terminal synaptic transistors have exhibited significant capabilities in the artificial synapse, more stable devices and simpler integration are needed in practical applications. Combining the configuration advantages of memristors and transistors, a novel pseudo-transistor is proposed. Here, recent advances in the development of pseudo-transistor-based neuromorphic electronics in recent years are reviewed. The working mechanisms, device structures and materials of three typical pseudo-transistors, including tunneling random access memory (TRAM), memflash and memtransistor, are comprehensively discussed. Finally, the future development and challenges in this field are emphasized.
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Affiliation(s)
- Jingwei Fu
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China
| | - Jie Wang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China
| | - Xiang He
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China
| | - Jianyu Ming
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China
| | - Le Wang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China
| | - Yiru Wang
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China
| | - He Shao
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China
| | - Chaoyue Zheng
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China
| | - Linghai Xie
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China
| | - Haifeng Ling
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China
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Seok H, Son S, Jathar SB, Lee J, Kim T. Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:3118. [PMID: 36991829 PMCID: PMC10058286 DOI: 10.3390/s23063118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/11/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.
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Affiliation(s)
- Hyunho Seok
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Shihoon Son
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Sagar Bhaurao Jathar
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Jaewon Lee
- School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Taesung Kim
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
- School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
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39
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Chen H, Li H, Ma T, Han S, Zhao Q. Biological function simulation in neuromorphic devices: from synapse and neuron to behavior. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2183712. [PMID: 36926202 PMCID: PMC10013381 DOI: 10.1080/14686996.2023.2183712] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 02/06/2023] [Accepted: 02/11/2023] [Indexed: 06/18/2023]
Abstract
As the boom of data storage and processing, brain-inspired computing provides an effective approach to solve the current problem. Various emerging materials and devices have been reported to promote the development of neuromorphic computing. Thereinto, the neuromorphic device represented by memristor has attracted extensive research due to its outstanding property to emulate the brain's functions from synaptic plasticity, sensory-memory neurons to some intelligent behaviors of living creatures. Herein, we mainly review the progress of these brain functions mimicked by neuromorphic devices, concentrating on synapse (i.e. various synaptic plasticity trigger by electricity and/or light), neurons (including the various sensory nervous system) and intelligent behaviors (such as conditioned reflex represented by Pavlov's dog experiment). Finally, some challenges and prospects related to neuromorphic devices are presented.
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Affiliation(s)
- Hui Chen
- Heart Center of Henan Provincial People’s Hospital, Central China Fuwai Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, P. R. China
| | - Huilin Li
- Henan Key Laboratory of Photovoltaic Materials, Henan University, Kaifeng, P. R. China
| | - Ting Ma
- Henan Key Laboratory of Photovoltaic Materials, Henan University, Kaifeng, P. R. China
| | - Shuangshuang Han
- Henan Key Laboratory of Photovoltaic Materials, Henan University, Kaifeng, P. R. China
| | - Qiuping Zhao
- Heart Center of Henan Provincial People’s Hospital, Central China Fuwai Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, P. R. China
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40
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Guo T, Ge J, Jiao Y, Teng Y, Sun B, Huang W, Asgarimoghaddam H, Musselman KP, Fang Y, Zhou YN, Wu YA. Intelligent matter endows reconfigurable temperature and humidity sensations for in-sensor computing. MATERIALS HORIZONS 2023; 10:1030-1041. [PMID: 36692087 DOI: 10.1039/d2mh01491b] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Data-centric tactics with in-sensor computing go beyond the conventional computing-centric tactic that is suffering from processing latency and excessive energy consumption. The multifunctional intelligent matter with dynamic smart responses to environmental variations paves the way to implement data-centric tactics with high computing efficiency. However, intelligent matter with humidity and temperature sensitivity has not been reported. In this work, a design is demonstrated based on a single memristive device to achieve reconfigurable temperature and humidity sensations. Opposite temperature sensations at the low resistance state (LRS) and high resistance state (HRS) were observed for low-level sensory data processing. Integrated devices mimicking intelligent electronic skin (e-skin) can work in three modes to adapt to different scenarios. Additionally, the device acts as a humidity-sensory artificial synapse that can implement high-level cognitive in-sensor computing. The intelligent matter with reconfigurable temperature and humidity sensations is promising for energy-efficient artificial intelligence (AI) systems.
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Affiliation(s)
- Tao Guo
- Department of Mechanical and Mechatronics Engineering, and Waterloo Institute of Nanotechnology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
| | - Jiawei Ge
- Department of Mechanical and Mechatronics Engineering, and Waterloo Institute of Nanotechnology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
- College of Materials Science and Technology, Jiangsu Key Laboratory of Materials and Technology for Energy Conversion, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
| | - Yixuan Jiao
- Department of Mechanical and Mechatronics Engineering, and Waterloo Institute of Nanotechnology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
| | - Youchao Teng
- Department of Mechanical and Mechatronics Engineering, and Waterloo Institute of Nanotechnology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
| | - Bai Sun
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, No. 28, Xianning West Road, Xi'an, Shaanxi 710049, P. R. China
| | - Wen Huang
- New Energy Technology Engineering Laboratory of Jiangsu Province, School of Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
- State Key Laboratory of Silicon Materials, Zhejiang University, Hangzhou 310027, China
| | - Hatameh Asgarimoghaddam
- Department of Mechanical and Mechatronics Engineering, and Waterloo Institute of Nanotechnology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
| | - Kevin P Musselman
- Department of Mechanical and Mechatronics Engineering, and Waterloo Institute of Nanotechnology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
| | - Yin Fang
- School of Chemical and Biomedical engineering, Nanyang Technological University, Singapore
| | - Y Norman Zhou
- Department of Mechanical and Mechatronics Engineering, and Waterloo Institute of Nanotechnology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
| | - Yimin A Wu
- Department of Mechanical and Mechatronics Engineering, and Waterloo Institute of Nanotechnology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
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41
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Xiao Y, Jiang B, Zhang Z, Ke S, Jin Y, Wen X, Ye C. A review of memristor: material and structure design, device performance, applications and prospects. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2162323. [PMID: 36872944 PMCID: PMC9980037 DOI: 10.1080/14686996.2022.2162323] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 12/09/2022] [Accepted: 12/21/2022] [Indexed: 06/18/2023]
Abstract
With the booming growth of artificial intelligence (AI), the traditional von Neumann computing architecture based on complementary metal oxide semiconductor devices are facing memory wall and power wall. Memristor based in-memory computing can potentially overcome the current bottleneck of computer and achieve hardware breakthrough. In this review, the recent progress of memory devices in material and structure design, device performance and applications are summarized. Various resistive switching materials, including electrodes, binary oxides, perovskites, organics, and two-dimensional materials, are presented and their role in the memristor are discussed. Subsequently, the construction of shaped electrodes, the design of functional layer and other factors influencing the device performance are analyzed. We focus on the modulation of the resistances and the effective methods to enhance the performance. Furthermore, synaptic plasticity, optical-electrical properties, the fashionable applications in logic operation and analog calculation are introduced. Finally, some critical issues such as the resistive switching mechanism, multi-sensory fusion, system-level optimization are discussed.
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Affiliation(s)
- Yongyue Xiao
- Hubei Key Laboratory of Ferro-& Piezoelectric Materials and Devices, Faculty of Physics and Electronic Science, Hubei University, Wuhan, China
| | - Bei Jiang
- Hubei Key Laboratory of Ferro-& Piezoelectric Materials and Devices, Faculty of Physics and Electronic Science, Hubei University, Wuhan, China
| | - Zihao Zhang
- Hubei Key Laboratory of Ferro-& Piezoelectric Materials and Devices, Faculty of Physics and Electronic Science, Hubei University, Wuhan, China
| | - Shanwu Ke
- Hubei Key Laboratory of Ferro-& Piezoelectric Materials and Devices, Faculty of Physics and Electronic Science, Hubei University, Wuhan, China
| | - Yaoyao Jin
- Hubei Key Laboratory of Ferro-& Piezoelectric Materials and Devices, Faculty of Physics and Electronic Science, Hubei University, Wuhan, China
| | - Xin Wen
- Faculty of Chemical Technology and Engineering, West Pomeranian University of Technology in Szczecin, Szczecin, Poland
| | - Cong Ye
- Hubei Key Laboratory of Ferro-& Piezoelectric Materials and Devices, Faculty of Physics and Electronic Science, Hubei University, Wuhan, China
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42
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Li Z, Tang W, Zhang B, Yang R, Miao X. Emerging memristive neurons for neuromorphic computing and sensing. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2188878. [PMID: 37090846 PMCID: PMC10120469 DOI: 10.1080/14686996.2023.2188878] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems.
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Affiliation(s)
- Zhiyuan Li
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
| | - Wei Tang
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
| | - Beining Zhang
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
| | - Rui Yang
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
- CONTACT Rui Yang School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan430074, China; Hubei Yangtze Memory Laboratories, Wuhan 430205, China
| | - Xiangshui Miao
- School of Integrated Circuits, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China
- Hubei Yangtze Memory Laboratories, Wuhan, China
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Kim Y, Zhu C, Lee WY, Smith A, Ma H, Li X, Son D, Matsuhisa N, Kim J, Bae WG, Cho SH, Kim MG, Kurosawa T, Katsumata T, To JWF, Oh JY, Paik S, Kim SJ, Jin L, Yan F, Tok JBH, Bao Z. A Hemispherical Image Sensor Array Fabricated with Organic Photomemory Transistors. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2203541. [PMID: 36281793 DOI: 10.1002/adma.202203541] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 08/01/2022] [Indexed: 06/16/2023]
Abstract
Hemispherical image sensors simplify lens designs, reduce optical aberrations, and improve image resolution for compact wide-field-of-view cameras. To achieve hemispherical image sensors, organic materials are promising candidates due to the following advantages: tunability of optoelectronic/spectral response and low-temperature low-cost processes. Here, a photolithographic process is developed to prepare a hemispherical image sensor array using organic thin film photomemory transistors with a density of 308 pixels per square centimeter. This design includes only one photomemory transistor as a single active pixel, in contrast to the conventional pixel architecture, consisting of select/readout/reset transistors and a photodiode. The organic photomemory transistor, comprising light-sensitive organic semiconductor and charge-trapping dielectric, is able to achieve a linear photoresponse (light intensity range, from 1 to 50 W m-2 ), along with a responsivity as high as 1.6 A W-1 (wavelength = 465 nm) for a dark current of 0.24 A m-2 (drain voltage = -1.5 V). These observed values represent the best responsivity for similar dark currents among all the reported hemispherical image sensor arrays to date. A transfer method was further developed that does not damage organic materials for hemispherical organic photomemory transistor arrays. These developed techniques are scalable and are amenable for other high-resolution 3D organic semiconductor devices.
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Affiliation(s)
- Yeongin Kim
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Chenxin Zhu
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
- Department of Chemical Engineering, Stanford University, Stanford, CA, 94305, USA
- School of Electronic Science and Engineering, Nanjing University, Nanjing, 210093, China
| | - Wen-Ya Lee
- Department of Chemical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Anna Smith
- Department of Chemical and Biological Engineering, University at Buffalo, Buffalo, NY, 14260, USA
| | - Haowen Ma
- School of Electronic Science and Engineering, Nanjing University, Nanjing, 210093, China
| | - Xiang Li
- School of Electronic Science and Engineering, Nanjing University, Nanjing, 210093, China
| | - Donghee Son
- Department of Chemical Engineering, Stanford University, Stanford, CA, 94305, USA
- Department of Electrical and Computer Engineering, Sungkyunkwan University, 16419, Suwon, South Korea
| | - Naoji Matsuhisa
- Department of Chemical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Jaemin Kim
- Department of Chemical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Won-Gyu Bae
- Department of Chemical Engineering, Stanford University, Stanford, CA, 94305, USA
| | | | - Myung-Gil Kim
- Department of Chemical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Tadanori Kurosawa
- Department of Chemical Engineering, Stanford University, Stanford, CA, 94305, USA
| | | | - John W F To
- Department of Chemical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Jin Young Oh
- Department of Chemical Engineering, Stanford University, Stanford, CA, 94305, USA
- Department of Chemical Engineering (Integrated Engineering Program), Kyung Hee University, Yongin, 17104, South Korea
| | - Seonghyun Paik
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Soo Jin Kim
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Lihua Jin
- Department of Chemical Engineering, Stanford University, Stanford, CA, 94305, USA
- Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Feng Yan
- School of Electronic Science and Engineering, Nanjing University, Nanjing, 210093, China
| | - Jeffrey B-H Tok
- Department of Chemical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Zhenan Bao
- Department of Chemical Engineering, Stanford University, Stanford, CA, 94305, USA
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Huang CY, Li H, Wu Y, Lin CH, Guan X, Hu L, Kim J, Zhu X, Zeng H, Wu T. Inorganic Halide Perovskite Quantum Dots: A Versatile Nanomaterial Platform for Electronic Applications. NANO-MICRO LETTERS 2022; 15:16. [PMID: 36580150 PMCID: PMC9800676 DOI: 10.1007/s40820-022-00983-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 10/31/2022] [Indexed: 05/19/2023]
Abstract
Metal halide perovskites have generated significant attention in recent years because of their extraordinary physical properties and photovoltaic performance. Among these, inorganic perovskite quantum dots (QDs) stand out for their prominent merits, such as quantum confinement effects, high photoluminescence quantum yield, and defect-tolerant structures. Additionally, ligand engineering and an all-inorganic composition lead to a robust platform for ambient-stable QD devices. This review presents the state-of-the-art research progress on inorganic perovskite QDs, emphasizing their electronic applications. In detail, the physical properties of inorganic perovskite QDs will be introduced first, followed by a discussion of synthesis methods and growth control. Afterwards, the emerging applications of inorganic perovskite QDs in electronics, including transistors and memories, will be presented. Finally, this review will provide an outlook on potential strategies for advancing inorganic perovskite QD technologies.
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Affiliation(s)
- Chien-Yu Huang
- School of Materials Science and Engineering, University of New South Wales, Sydney, 2052, Australia
| | - Hanchen Li
- School of Materials Science and Engineering, University of New South Wales, Sydney, 2052, Australia
| | - Ye Wu
- MIIT Key Laboratory of Advanced Display Materials and Devices, Institute of Optoelectronics and Nanomaterials, College of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, People's Republic of China
| | - Chun-Ho Lin
- School of Materials Science and Engineering, University of New South Wales, Sydney, 2052, Australia
| | - Xinwei Guan
- School of Materials Science and Engineering, University of New South Wales, Sydney, 2052, Australia
| | - Long Hu
- School of Materials Science and Engineering, University of New South Wales, Sydney, 2052, Australia
| | - Jiyun Kim
- School of Materials Science and Engineering, University of New South Wales, Sydney, 2052, Australia
| | - Xiaoming Zhu
- School of Materials Science and Engineering, University of New South Wales, Sydney, 2052, Australia
| | - Haibo Zeng
- MIIT Key Laboratory of Advanced Display Materials and Devices, Institute of Optoelectronics and Nanomaterials, College of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, People's Republic of China.
| | - Tom Wu
- School of Materials Science and Engineering, University of New South Wales, Sydney, 2052, Australia.
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45
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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: 26] [Impact Index Per Article: 13.0] [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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46
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Kim S, Kim S, Ho DH, Roe DG, Choi YJ, Kim MJ, Kim UJ, Le ML, Kim J, Kim SH, Cho JH. Neurorobotic approaches to emulate human motor control with the integration of artificial synapse. SCIENCE ADVANCES 2022; 8:eabo3326. [PMID: 36170364 PMCID: PMC9519054 DOI: 10.1126/sciadv.abo3326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 08/11/2022] [Indexed: 06/16/2023]
Abstract
The advancement of electronic devices has enabled researchers to successfully emulate human synapses, thereby promoting the development of the research field of artificial synapse integrated soft robots. This paper proposes an artificial reciprocal inhibition system that can successfully emulate the human motor control mechanism through the integration of artificial synapses. The proposed system is composed of artificial synapses, load transistors, voltage/current amplifiers, and a soft actuator to demonstrate the muscle movement. The speed, range, and direction of the soft actuator movement can be precisely controlled via the preset input voltages with different amplitudes, numbers, and signs (positive or negative). The artificial reciprocal inhibition system can impart lifelike motion to soft robots and is a promising tool to enable the successful integration of soft robots or prostheses in a living body.
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Affiliation(s)
- Seonkwon Kim
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Seongchan Kim
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Dong Hae Ho
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Dong Gue Roe
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Young Jin Choi
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Min Je Kim
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Ui Jin Kim
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Manh Linh Le
- Department of Advanced Materials Engineering, Kangwon National University, Samcheok 25931, Republic of Korea
| | - Juyoung Kim
- Department of Advanced Materials Engineering, Kangwon National University, Samcheok 25931, Republic of Korea
| | - Se Hyun Kim
- Division of Chemical Engineering, Konkuk University, Seoul 05029, Republic of Korea
| | - Jeong Ho Cho
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 03722, Republic of Korea
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47
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Kim D, Lee JS. Emulating the Signal Transmission in a Neural System Using Polymer Membranes. ACS APPLIED MATERIALS & INTERFACES 2022; 14:42308-42316. [PMID: 36069456 DOI: 10.1021/acsami.2c12166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Neurons are vital components of the brain. When stimulated by neurotransmitters at the dendrites, neurons deliver signals as changes in the membrane potential by ion movement. The signal transmission of a nervous system exhibits a high energy efficiency. These characteristics of neurons are being exploited to develop efficient neuromorphic computing systems. In this study, we develop chemical synapses for neuromorphic devices and emulate the signaling processes in a nervous system using a polymer membrane, in which the ionic permeability can be controlled. The polymer membrane comprises poly(diallyl-dimethylammonium chloride) and poly(3-sulfopropyl acrylate potassium salt), which have positive and negative charges, respectively. The ionic permeability of the polymer membrane is controlled by the injection of a neurotransmitter solution. This device emulates the signal transmission behavior of biological neurons depending on the concentration of the injected neurotransmitter solution. The proposed artificial neuronal signaling device can facilitate the development of bio-realistic neuromorphic devices.
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Affiliation(s)
- Dongshin Kim
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
| | - Jang-Sik Lee
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
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48
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Li N, Okmi A, Jabegu T, Zheng H, Chen K, Lomashvili A, Williams W, Maraba D, Kravchenko I, Xiao K, He K, Lei S. van der Waals Semiconductor Empowered Vertical Color Sensor. ACS NANO 2022; 16:8619-8629. [PMID: 35436098 DOI: 10.1021/acsnano.1c09875] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Biomimetic artificial vision is receiving significant attention nowadays, particularly for the development of neuromorphic electronic devices, artificial intelligence, and microrobotics. Nevertheless, color recognition, the most critical vision function, is missed in the current research due to the difficulty of downscaling of the prevailing color sensing devices. Conventional color sensors typically adopt a lateral color sensing channel layout and consume a large amount of physical space, whereas compact designs suffer from an unsatisfactory color detection accuracy. In this work, we report a van der Waals semiconductor-empowered vertical color sensing structure with the emphasis on compact device profile and precise color recognition capability. More attractive, we endow color sensor hardware with the function of chromatic aberration correction, which can simplify the design of an optical lens system and, in turn, further downscales the artificial vision systems. Also, the dimension of a multiple pixel prototype device in our study confirms the scalability and practical potentials of our developed device architecture toward the above applications.
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Affiliation(s)
- Ningxin Li
- Department of Physics and Astronomy, Georgia State University, Atlanta, Georgia 30303, United States
| | - Aisha Okmi
- Department of Physics and Astronomy, Georgia State University, Atlanta, Georgia 30303, United States
- Department of Physics, Jazan University, Jazan 45142, Saudi Arabia
| | - Tara Jabegu
- Department of Physics and Astronomy, Georgia State University, Atlanta, Georgia 30303, United States
| | - Hongkui Zheng
- Department of Materials Science and Engineering, Clemson University, Clemson, South Carolina 29634, United States
| | - Kuangcai Chen
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30303, United States
| | - Alexander Lomashvili
- Department of Physics and Astronomy, Georgia State University, Atlanta, Georgia 30303, United States
| | - Westley Williams
- Department of Physics and Astronomy, Georgia State University, Atlanta, Georgia 30303, United States
| | - Diren Maraba
- Department of Physics and Astronomy, Georgia State University, Atlanta, Georgia 30303, United States
| | - Ivan Kravchenko
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Kai Xiao
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Kai He
- Department of Materials Science and Engineering, Clemson University, Clemson, South Carolina 29634, United States
- Department of Material Science and Engineering, University of California, Irvine, California 92697, United States
| | - Sidong Lei
- Department of Physics and Astronomy, Georgia State University, Atlanta, Georgia 30303, United States
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49
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Sun L, Du Y, Yu H, Wei H, Xu W, Xu W. An Artificial Reflex Arc That Perceives Afferent Visual and Tactile Information and Controls Efferent Muscular Actions. Research (Wash D C) 2022; 2022:9851843. [PMID: 35252874 PMCID: PMC8858381 DOI: 10.34133/2022/9851843] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 01/10/2022] [Indexed: 01/01/2023] Open
Abstract
Neural perception and action-inspired electronics is becoming important for interactive human-machine interfaces and intelligent robots. A system that implements neuromorphic environmental information coding, synaptic signal processing, and motion control is desired. We report a neuroinspired artificial reflex arc that possesses visual and somatosensory dual afferent nerve paths and an efferent nerve path to control artificial muscles. A self-powered photoelectric synapse between the afferent and efferent nerves was used as the key information processor. The artificial reflex arc successfully responds to external visual and tactile information and controls the actions of artificial muscle in response to these external stimuli and thus emulates reflex activities through a full reflex arc. The visual and somatosensory information is encoded as impulse spikes, the frequency of which exhibited a sublinear dependence on the obstacle proximity or pressure stimuli. The artificial reflex arc suggests a promising strategy toward developing soft neurorobotic systems and prostheses.
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Affiliation(s)
- Lin Sun
- Institute of Photoelectronic Thin Film Devices and Technology of Nankai University, Tianjin 300350, China.,Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, Tianjin 300350, China.,Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, Tianjin 300350, China.,National Institute for Advanced Materials, Tianjin 300350, China
| | - Yi Du
- Institute of Photoelectronic Thin Film Devices and Technology of Nankai University, Tianjin 300350, China.,Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, Tianjin 300350, China.,Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, Tianjin 300350, China.,National Institute for Advanced Materials, Tianjin 300350, China
| | - Haiyang Yu
- Institute of Photoelectronic Thin Film Devices and Technology of Nankai University, Tianjin 300350, China.,Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, Tianjin 300350, China.,Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, Tianjin 300350, China.,National Institute for Advanced Materials, Tianjin 300350, China
| | - Huanhuan Wei
- Institute of Photoelectronic Thin Film Devices and Technology of Nankai University, Tianjin 300350, China.,Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, Tianjin 300350, China.,Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, Tianjin 300350, China.,National Institute for Advanced Materials, Tianjin 300350, China
| | - Wenlong Xu
- Institute of Photoelectronic Thin Film Devices and Technology of Nankai University, Tianjin 300350, China.,Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, Tianjin 300350, China.,Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, Tianjin 300350, China.,National Institute for Advanced Materials, Tianjin 300350, China
| | - Wentao Xu
- Institute of Photoelectronic Thin Film Devices and Technology of Nankai University, Tianjin 300350, China.,Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, Tianjin 300350, China.,Engineering Research Center of Thin Film Photoelectronic Technology, Ministry of Education, Tianjin 300350, China.,National Institute for Advanced Materials, Tianjin 300350, China
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50
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Guo T, Pan K, Jiao Y, Sun B, Du C, Mills JP, Chen Z, Zhao X, Wei L, Zhou YN, Wu YA. Versatile memristor for memory and neuromorphic computing. NANOSCALE HORIZONS 2022; 7:299-310. [PMID: 35064257 DOI: 10.1039/d1nh00481f] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The memristor is a promising candidate to implement high-density memory and neuromorphic computing. Based on the characteristic retention time, memristors are classified into volatile and non-volatile types. However, a single memristor generally provides a specific function based on electronic performances, which poses roadblocks for further developing novel circuits. Versatile memristors exhibiting both volatile and non-volatile properties can provide multiple functions covering non-volatile memory and neuromorphic computing. In this work, a versatile memristor with volatile/non-volatile bifunctional properties was developed. Non-volatile functionality with a storage window of 4.0 × 105 was obtained. Meanwhile, the device can provide threshold volatile functionalities with a storage window of 7.0 × 104 and a rectification ratio of 4.0 × 104. The leaky integrate-and-fire (LIF) neuron model and artificial synapse based on the device have been studied. Such a versatile memristor enables non-volatile memory, selectors, artificial neurons, and artificial synapses, which will provide advantages regarding circuit simplification, fabrication processes, and manufacturing costs.
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Affiliation(s)
- Tao Guo
- Department of Mechanical and Mechatronics Engineering, Waterloo Institute of Nanotechnology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
| | - Kangqiang Pan
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
| | - Yixuan Jiao
- Department of Mechanical and Mechatronics Engineering, Waterloo Institute of Nanotechnology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
| | - Bai Sun
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials (Ministry of Education of China), Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Cheng Du
- Department of Mechanical and Mechatronics Engineering, Waterloo Institute of Nanotechnology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
| | - Joel P Mills
- Department of Mechanical and Mechatronics Engineering, Waterloo Institute of Nanotechnology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
| | - Zuolong Chen
- Department of Mechanical and Mechatronics Engineering, Waterloo Institute of Nanotechnology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
| | - Xiaoye Zhao
- Department of Mechanical and Mechatronics Engineering, Waterloo Institute of Nanotechnology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
- State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Harbin 150001, China
| | - Lan Wei
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
| | - Y Norman Zhou
- Department of Mechanical and Mechatronics Engineering, Waterloo Institute of Nanotechnology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
| | - Yimin A Wu
- Department of Mechanical and Mechatronics Engineering, Waterloo Institute of Nanotechnology, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
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