1
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Boateng D, Li X, Zhu Y, Zhang H, Wu M, Liu J, Kang Y, Zeng H, Han L. Recent advances in flexible hydrogel sensors: Enhancing data processing and machine learning for intelligent perception. Biosens Bioelectron 2024; 261:116499. [PMID: 38896981 DOI: 10.1016/j.bios.2024.116499] [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: 03/27/2024] [Revised: 06/07/2024] [Accepted: 06/12/2024] [Indexed: 06/21/2024]
Abstract
With the advent of flexible electronics and sensing technology, hydrogel-based flexible sensors have exhibited considerable potential across a diverse range of applications, including wearable electronics and soft robotics. Recently, advanced machine learning (ML) algorithms have been integrated into flexible hydrogel sensing technology to enhance their data processing capabilities and to achieve intelligent perception. However, there are no reviews specifically focusing on the data processing steps and analysis based on the raw sensing data obtained by flexible hydrogel sensors. Here we provide a comprehensive review of the latest advancements and breakthroughs in intelligent perception achieved through the fusion of ML algorithms with flexible hydrogel sensors, across various applications. Moreover, this review thoroughly examines the data processing techniques employed in flexible hydrogel sensors, offering valuable perspectives expected to drive future data-driven applications in this field.
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Affiliation(s)
- Derrick Boateng
- College of Applied Sciences, Shenzhen University, Shenzhen, 518060, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, 518060, China; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518188, China
| | - Xukai Li
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518188, China
| | - Yuhan Zhu
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518188, China
| | - Hao Zhang
- School of Physics and Optoelectronic Engineering, Hainan University, Haikou, 570228, China.
| | - Meng Wu
- Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta, T6G 2V4, Canada
| | - Jifang Liu
- The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, 510700, China
| | - Yan Kang
- College of Applied Sciences, Shenzhen University, Shenzhen, 518060, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, 518060, China; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518188, China
| | - Hongbo Zeng
- Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta, T6G 2V4, Canada
| | - Linbo Han
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518188, China.
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2
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Fu X, Cheng W, Wan G, Yang Z, Tee BCK. Toward an AI Era: Advances in Electronic Skins. Chem Rev 2024. [PMID: 39198214 DOI: 10.1021/acs.chemrev.4c00049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2024]
Abstract
Electronic skins (e-skins) have seen intense research and rapid development in the past two decades. To mimic the capabilities of human skin, a multitude of flexible/stretchable sensors that detect physiological and environmental signals have been designed and integrated into functional systems. Recently, researchers have increasingly deployed machine learning and other artificial intelligence (AI) technologies to mimic the human neural system for the processing and analysis of sensory data collected by e-skins. Integrating AI has the potential to enable advanced applications in robotics, healthcare, and human-machine interfaces but also presents challenges such as data diversity and AI model robustness. In this review, we first summarize the functions and features of e-skins, followed by feature extraction of sensory data and different AI models. Next, we discuss the utilization of AI in the design of e-skin sensors and address the key topic of AI implementation in data processing and analysis of e-skins to accomplish a range of different tasks. Subsequently, we explore hardware-layer in-skin intelligence before concluding with an analysis of the challenges and opportunities in the various aspects of AI-enabled e-skins.
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Affiliation(s)
- Xuemei Fu
- Department of Materials Science and Engineering, National University of Singapore, Singapore 117575, Singapore
- Institute for Health Innovation & Technology, National University of Singapore, Singapore 119276, Singapore
| | - Wen Cheng
- Department of Materials Science and Engineering, National University of Singapore, Singapore 117575, Singapore
- Institute for Health Innovation & Technology, National University of Singapore, Singapore 119276, Singapore
- The N.1 Institute for Health, National University of Singapore, Singapore 117456, Singapore
| | - Guanxiang Wan
- Department of Materials Science and Engineering, National University of Singapore, Singapore 117575, Singapore
- Institute for Health Innovation & Technology, National University of Singapore, Singapore 119276, Singapore
| | - Zijie Yang
- Department of Materials Science and Engineering, National University of Singapore, Singapore 117575, Singapore
- Institute for Health Innovation & Technology, National University of Singapore, Singapore 119276, Singapore
| | - Benjamin C K Tee
- Department of Materials Science and Engineering, National University of Singapore, Singapore 117575, Singapore
- Institute for Health Innovation & Technology, National University of Singapore, Singapore 119276, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- The N.1 Institute for Health, National University of Singapore, Singapore 117456, Singapore
- Institute of Materials Research and Engineering, Agency for Science Technology and Research, Singapore 138634, Singapore
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3
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Jiang S, Sun J, Pei M, Peng L, Dai Q, Wu C, Gu J, Yang Y, Su J, Gu D, Zhang H, Guo H, Li Y. Energy-Efficient Reservoir Computing Based on Solution-Processed Electrolyte/Ferroelectric Memcapacitive Synapses for Biosignal Classification. J Phys Chem Lett 2024; 15:8501-8509. [PMID: 39133786 DOI: 10.1021/acs.jpclett.4c01896] [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: 08/23/2024]
Abstract
The classification of critical physiological signals using neuromorphic devices is essential for early disease detection. Physical reservoir computing (RC), a lightweight temporal processing neural network, offers a promising solution for low-power, resource-constrained hardware. Although solution-processed memcapacitive reservoirs have the potential to improve power efficiency as a result of their ultralow static power consumption, further advancements in synaptic tunability and reservoir states are imperative to enhance the capabilities of RC systems. This work presents solution-processed electrolyte/ferroelectric memcapacitive synapses. Leveraging the synergistic coupling of electrical double-layer (EDL) effects and ferroelectric polarization, these synapses exhibit tunable long- and short-term plasticity, ultralow power consumption (∼27 fJ per spike), and rich reservoir state dynamics, making them well-suited for energy-efficient RC systems. The classifications of critical electrocardiogram (ECG) signals, including arrhythmia and obstructive sleep apnea (OSA), are performed using the synapse-based RC system, demonstrating excellent accuracies of 97.8 and 80.0% for arrhythmia and OSA classifications, respectively. These findings pave the way for developing lightweight, energy-efficient machine-learning platforms for biosignal classification in wearable devices.
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Affiliation(s)
- Sai Jiang
- School of Integrated Circuits Industry, Wang Zheng School of Microelectronics, Changzhou University, Changzhou, Jiangsu 213164, People's Republic of China
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Jinrui Sun
- School of Integrated Circuits Industry, Wang Zheng School of Microelectronics, Changzhou University, Changzhou, Jiangsu 213164, People's Republic of China
| | - Mengjiao Pei
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Lichao Peng
- School of Integrated Circuits Industry, Wang Zheng School of Microelectronics, Changzhou University, Changzhou, Jiangsu 213164, People's Republic of China
| | - Qinyong Dai
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Chaoran Wu
- School of Integrated Circuits Industry, Wang Zheng School of Microelectronics, Changzhou University, Changzhou, Jiangsu 213164, People's Republic of China
| | - Jiahao Gu
- School of Integrated Circuits Industry, Wang Zheng School of Microelectronics, Changzhou University, Changzhou, Jiangsu 213164, People's Republic of China
| | - Yanqin Yang
- School of Integrated Circuits Industry, Wang Zheng School of Microelectronics, Changzhou University, Changzhou, Jiangsu 213164, People's Republic of China
| | - Jian Su
- School of Integrated Circuits Industry, Wang Zheng School of Microelectronics, Changzhou University, Changzhou, Jiangsu 213164, People's Republic of China
| | - Ding Gu
- School of Integrated Circuits Industry, Wang Zheng School of Microelectronics, Changzhou University, Changzhou, Jiangsu 213164, People's Republic of China
| | - Han Zhang
- School of Integrated Circuits Industry, Wang Zheng School of Microelectronics, Changzhou University, Changzhou, Jiangsu 213164, People's Republic of China
| | - Huafei Guo
- School of Integrated Circuits Industry, Wang Zheng School of Microelectronics, Changzhou University, Changzhou, Jiangsu 213164, People's Republic of China
| | - Yun Li
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
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4
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Xie X, Wang Q, Zhao C, Sun Q, Gu H, Li J, Tu X, Nie B, Sun X, Liu Y, Lim EG, Wen Z, Wang ZL. Neuromorphic Computing-Assisted Triboelectric Capacitive-Coupled Tactile Sensor Array for Wireless Mixed Reality Interaction. ACS NANO 2024; 18:17041-17052. [PMID: 38904995 PMCID: PMC11223466 DOI: 10.1021/acsnano.4c03554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 06/06/2024] [Accepted: 06/12/2024] [Indexed: 06/22/2024]
Abstract
Flexible tactile sensors show promise for artificial intelligence applications due to their biological adaptability and rapid signal perception. Triboelectric sensors enable active dynamic tactile sensing, while integrating static pressure sensing and real-time multichannel signal transmission is key for further development. Here, we propose an integrated structure combining a capacitive sensor for static spatiotemporal mapping and a triboelectric sensor for dynamic tactile recognition. A liquid metal-based flexible dual-mode triboelectric-capacitive-coupled tactile sensor (TCTS) array of 4 × 4 pixels achieves a spatial resolution of 7 mm, exhibiting a pressure detection limit of 0.8 Pa and a fast response of 6 ms. Furthermore, neuromorphic computing using the MXene-based synaptic transistor achieves 100% recognition accuracy of handwritten numbers/letters within 90 epochs based on dynamic triboelectric signals collected by the TCTS array, and cross-spatial information communication from the perceived multichannel tactile data is realized in the mixed reality space. The results illuminate considerable application possibilities of dual-mode tactile sensing technology in human-machine interfaces and advanced robotics.
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Affiliation(s)
- Xinkai Xie
- Institute
of Functional Nano and Soft Materials (FUNSOM), Joint International
Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123, P. R. China
- Department
of Electrical and Electronic Engineering, School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, P. R. China
- Department
of Electrical and Electronic Engineering, University of Liverpool, Liverpool L693GJ, U.K.
- Joint
International Research Laboratory of Information Display and Visualization,
School of Electronic Science and Engineering, Southeast University, Nanjing 210096, P. R. China
| | - Qinan Wang
- Department
of Electrical and Electronic Engineering, School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, P. R. China
- Department
of Electrical and Electronic Engineering, University of Liverpool, Liverpool L693GJ, U.K.
| | - Chun Zhao
- Department
of Electrical and Electronic Engineering, School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, P. R. China
| | - Qilei Sun
- Department
of Electrical and Electronic Engineering, School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, P. R. China
| | - Haicheng Gu
- Institute
of Functional Nano and Soft Materials (FUNSOM), Joint International
Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123, P. R. China
| | - Junyan Li
- Department
of Electrical and Electronic Engineering, School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, P. R. China
- Department
of Electrical and Electronic Engineering, University of Liverpool, Liverpool L693GJ, U.K.
| | - Xin Tu
- Department
of Electrical and Electronic Engineering, University of Liverpool, Liverpool L693GJ, U.K.
| | - Baoqing Nie
- School
of Electronic and Information Engineering, Soochow University, Suzhou 215006, P. R. China
| | - Xuhui Sun
- Institute
of Functional Nano and Soft Materials (FUNSOM), Joint International
Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123, P. R. China
| | - Yina Liu
- Department
of Applied Mathematics, School of Mathematics and Physics, Xi’an Jiaotong-Liverpool University, Suzhou 215123, P. R. China
| | - Eng Gee Lim
- Department
of Electrical and Electronic Engineering, School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, P. R. China
| | - Zhen Wen
- Institute
of Functional Nano and Soft Materials (FUNSOM), Joint International
Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123, P. R. China
| | - Zhong Lin Wang
- Beijing
Institute
of Nanoenergy and Nanosystems, Chinese Academy
of Sciences, Beijing 101400, P. R. China
- School
of Materials Science and Engineering, Georgia
Institute of Technology, Atlanta, Georgia 30332-0245, United States
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5
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Leng K, Wan Y, Fu Y, Wang L, Wang Q. Si/CuO Heterojunction-Based Photomemristor for Reconfigurable, Non-Volatile, and Self-Powered In-Sensor Computing. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2309945. [PMID: 38400705 DOI: 10.1002/smll.202309945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/16/2024] [Indexed: 02/25/2024]
Abstract
In-sensor computing has attracted considerable interest as a solution for overcoming the energy efficiency and response time limitations of the traditional von Neumann architecture. Recently, emerging memristors based on transition-metal oxides (TMOs) have attracted attention as promising candidates for in-memory computing owing to their tunable conductance, high speed, and low operational energy. However, the poor photoresponse of TMOs presents challenges for integrating sensing and processing units into a single device. This integration is crucial for eliminating the need for a sensor/processor interface and achieving energy-efficient in-sensor computing systems. In this study, a Si/CuO heterojunction-based photomemristor is proposed that combines the reversible resistive switching behavior of CuO with the appropriate optical absorption bandgap of the Si substrate. The proposed photomemristor demonstrates a simultaneous reconfigurable, non-volatile, and self-powered photoresponse, producing a microampere-level photocurrent at zero bias. The controlled migration of oxygen vacancies in CuO result in distinct energy-band bending at the interface, enabling multiple levels of photoresponsivity. Additionally, the device exhibits high stability and ultrafast response speed to the built-in electric field. Furthermore, the prototype photomemristor can be trained to emulate the attention-driven nature of the human visual system, indicating the tremendous potential of TMO-based photomemristors as hardware foundations for in-sensor computing.
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Affiliation(s)
- Kangmin Leng
- Department of Physics, School of Physics and Materials Science, Nanchang University, Nanchang, 330031, China
| | - Yu Wan
- 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
| | - 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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6
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Guo L, Sun H, Min L, Wang M, Cao F, Li L. Two-Terminal Perovskite Optoelectronic Synapse for Rapid Trained Neuromorphic Computation with High Accuracy. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2402253. [PMID: 38553842 DOI: 10.1002/adma.202402253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 03/16/2024] [Indexed: 04/09/2024]
Abstract
Emerging neural morphological vision sensors inspired by biological systems that integrate image perception, memory, and information computing are expected to transform the landscape of machine vision and artificial intelligence. However, stable and reconfigurable light-induced synaptic behavior always relies on independent gateport modulation. Despite its potential, the limitations of uncontrollable defects and ionic characteristics have led to simpler, smaller, and more integration-friendly two-terminal devices being used as sidelines. In this work, the synergy between ion migration barriers and readout voltage is proven to be the key to realizing stable, reconfigurable, and precisely controllable postsynaptic current in two-terminal devices. Following the same mechanism, optical and electrical signal synchronous triggering is proposed to serve as a preprocessing method to achieve a recognition accuracy of 96.5%. Impressively, the gradual ion accumulation during the training process induces photocurrent evolution, serving as a reference for the dynamic learning rate and boosting accuracy to 97.8% in just 10 epochs. The PSC modulation potential under short optical pulse of 20 ns is also revealed. This optoelectronic device with perception, memory, and computation capabilities can promote the development of new devices for future photonic neural morphological circuits and artificial vision.
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Affiliation(s)
- Linqi Guo
- School of Physical Science and Technology, Jiangsu Key Laboratory of Thin Films, Center for Energy Conversion Materials & Physics (CECMP), Soochow University, Suzhou, 215006, P. R. China
| | - Haoxuan Sun
- School of Physical Science and Technology, Jiangsu Key Laboratory of Thin Films, Center for Energy Conversion Materials & Physics (CECMP), Soochow University, Suzhou, 215006, P. R. China
| | - Liangliang Min
- School of Physical Science and Technology, Jiangsu Key Laboratory of Thin Films, Center for Energy Conversion Materials & Physics (CECMP), Soochow University, Suzhou, 215006, P. R. China
| | - Meng Wang
- School of Physical Science and Technology, Jiangsu Key Laboratory of Thin Films, Center for Energy Conversion Materials & Physics (CECMP), Soochow University, Suzhou, 215006, P. R. China
| | - Fengren Cao
- School of Physical Science and Technology, Jiangsu Key Laboratory of Thin Films, Center for Energy Conversion Materials & Physics (CECMP), Soochow University, Suzhou, 215006, P. R. China
| | - Liang Li
- School of Physical Science and Technology, Jiangsu Key Laboratory of Thin Films, Center for Energy Conversion Materials & Physics (CECMP), Soochow University, Suzhou, 215006, P. R. China
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7
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Lu P, Liao X, Guo X, Cai C, Liu Y, Chi M, Du G, Wei Z, Meng X, Nie S. Gel-Based Triboelectric Nanogenerators for Flexible Sensing: Principles, Properties, and Applications. NANO-MICRO LETTERS 2024; 16:206. [PMID: 38819527 PMCID: PMC11143175 DOI: 10.1007/s40820-024-01432-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 04/30/2024] [Indexed: 06/01/2024]
Abstract
The rapid development of the Internet of Things and artificial intelligence technologies has increased the need for wearable, portable, and self-powered flexible sensing devices. Triboelectric nanogenerators (TENGs) based on gel materials (with excellent conductivity, mechanical tunability, environmental adaptability, and biocompatibility) are considered an advanced approach for developing a new generation of flexible sensors. This review comprehensively summarizes the recent advances in gel-based TENGs for flexible sensors, covering their principles, properties, and applications. Based on the development requirements for flexible sensors, the working mechanism of gel-based TENGs and the characteristic advantages of gels are introduced. Design strategies for the performance optimization of hydrogel-, organogel-, and aerogel-based TENGs are systematically summarized. In addition, the applications of gel-based TENGs in human motion sensing, tactile sensing, health monitoring, environmental monitoring, human-machine interaction, and other related fields are summarized. Finally, the challenges of gel-based TENGs for flexible sensing are discussed, and feasible strategies are proposed to guide future research.
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Affiliation(s)
- Peng Lu
- School of Light Industry and Food Engineering, Guangxi University, Nanning, 530004, People's Republic of China
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, People's Republic of China
| | - Xiaofang Liao
- School of Light Industry and Food Engineering, Guangxi University, Nanning, 530004, People's Republic of China
| | - Xiaoyao Guo
- School of Light Industry and Food Engineering, Guangxi University, Nanning, 530004, People's Republic of China
| | - Chenchen Cai
- School of Light Industry and Food Engineering, Guangxi University, Nanning, 530004, People's Republic of China
| | - Yanhua Liu
- School of Light Industry and Food Engineering, Guangxi University, Nanning, 530004, People's Republic of China
| | - Mingchao Chi
- School of Light Industry and Food Engineering, Guangxi University, Nanning, 530004, People's Republic of China
| | - Guoli Du
- School of Light Industry and Food Engineering, Guangxi University, Nanning, 530004, People's Republic of China
| | - Zhiting Wei
- School of Light Industry and Food Engineering, Guangxi University, Nanning, 530004, People's Republic of China
| | - Xiangjiang Meng
- School of Light Industry and Food Engineering, Guangxi University, Nanning, 530004, People's Republic of China
| | - Shuangxi Nie
- School of Light Industry and Food Engineering, Guangxi University, Nanning, 530004, People's Republic of China.
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8
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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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9
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Qi Y, Tang J, Fan S, An C, Wu E, Liu J. Dual Interactive Mode Human-Machine Interfaces Based on Triboelectric Nanogenerator and IGZO/In 2O 3 Heterojunction Synaptic Transistor. SMALL METHODS 2024:e2301698. [PMID: 38607954 DOI: 10.1002/smtd.202301698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 03/29/2024] [Indexed: 04/14/2024]
Abstract
Imitating human neural networks via bio-inspired electronics advances human-machine interfaces (HMI), overcoming von Neumann limitations and enabling efficient, low-energy data processing in the big data era. However, single-contact mode HMIs have inherent limitations in terms of their capabilities and performances, such as constrained adaptability to dynamic environments, and reduced cognitive processing capabilities. Here, a dual-interactive-mode HMI system based on a triboelectric nanogenerator (TENG) and heterojunction synaptic transistor (HJST) is proposed for both contact and non-contact applications. The TENG incorporates a poly-methyl meth-acrylate (PMMA)-NiCo2S4/S film, in which the NiCo2S4/S composite traps and blocks electrons to optimize charge generation and storage. The heterojunction structure, mitigates the Debye screening effect, thereby improving transistor characteristics and reliability. The integrated TENG-HJST system exhibits synaptic functions, including excitatory/inhibitory postsynaptic current (EPSC/IPSC), paired-pulse facilitation/depression (PPF/PPD), and synaptic plasticity, enabling emulation of neural behavior and advanced information processing. Moreover, neural morphology manipulation is demonstrated in practical tasks, such as controlling international chess games. By integrating the TENG-HJST device with a robotic hand, conscious artificial responses are generated, enhancing event accuracy. This breakthrough in dual-interactive-mode interfacing holds promise for HMI systems and neural prostheses.
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Affiliation(s)
- Yashuai Qi
- College of Electronics & Information, Qingdao University, Qingdao, 266071, China
| | - Jing Tang
- China National Chemical Communications Construction Group Second Engineering Co., Ltd, Qingdao, 266555, China
| | - Shuangqing Fan
- College of Electronics & Information, Qingdao University, Qingdao, 266071, China
| | - Chunhua An
- State Key Laboratory of Precision Measurement Technology and Instruments, School of Precision Instruments and Opto-electronics Engineering, Tianjin University, No. 92 Weijin Road, Tianjin, 300072, China
| | - Enxiu Wu
- State Key Laboratory of Precision Measurement Technology and Instruments, School of Precision Instruments and Opto-electronics Engineering, Tianjin University, No. 92 Weijin Road, Tianjin, 300072, China
| | - Jing Liu
- State Key Laboratory of Precision Measurement Technology and Instruments, School of Precision Instruments and Opto-electronics Engineering, Tianjin University, No. 92 Weijin Road, Tianjin, 300072, China
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10
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Lee JH, Cho K, Kim JK. Age of Flexible Electronics: Emerging Trends in Soft Multifunctional Sensors. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2310505. [PMID: 38258951 DOI: 10.1002/adma.202310505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/27/2023] [Indexed: 01/24/2024]
Abstract
With the commercialization of first-generation flexible mobiles and displays in the late 2010s, humanity has stepped into the age of flexible electronics. Inevitably, soft multifunctional sensors, as essential components of next-generation flexible electronics, have attracted tremendous research interest like never before. This review is dedicated to offering an overview of the latest emerging trends in soft multifunctional sensors and their accordant future research and development (R&D) directions for the coming decade. First, key characteristics and the predominant target stimuli for soft multifunctional sensors are highlighted. Second, important selection criteria for soft multifunctional sensors are introduced. Next, emerging materials/structures and trends for soft multifunctional sensors are identified. Specifically, the future R&D directions of these sensors are envisaged based on their emerging trends, namely i) decoupling of multiple stimuli, ii) data processing, iii) skin conformability, and iv) energy sources. Finally, the challenges and potential opportunities for these sensors in future are discussed, offering new insights into prospects in the fast-emerging technology.
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Affiliation(s)
- Jeng-Hun Lee
- Department of Chemical Engineering, Pohang University of Science and Technology, Pohang, 37673, South Korea
| | - Kilwon Cho
- Department of Chemical Engineering, Pohang University of Science and Technology, Pohang, 37673, South Korea
| | - Jang-Kyo Kim
- Department of Mechanical Engineering, Khalifa University, P. O. Box 127788, Abu Dhabi, United Arab Emirates
- School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, 2052, Australia
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11
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Hui Z, Zhang Z, Wang Y, Zhang R, Liu X, Jiang M, Ju F, Hou W, Xia Z, Wang D, Wang P, Pei Y, Yan R, Zhang Y, Chen Q, Huang W, Sun G. Gradiently Foaming Ultrasoft Hydrogel with Stop Holes for Highly Deformable, Crack-Resistant and Sensitive Conformal Human-Machine Interfaces. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2314163. [PMID: 38423019 DOI: 10.1002/adma.202314163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 02/26/2024] [Indexed: 03/02/2024]
Abstract
Hydrogels are considered as promising materials for human-machine interfaces (HMIs) owing to their merits of tailorable mechanical and electrical properties; nevertheless, it remains challenging to simultaneously achieve ultrasoftness, good mechanical robustness and high sensitivity, which are the pre-requisite requirements for wearable sensing applications. Herein, for the first time, this work proposes a universal phase-transition-induced bubbling strategy to fabricate ultrasoft gradient foam-shaped hydrogels (FSHs) with stop holes for high deformability, crack-resistance and sensitive conformal HMIs. As a typical system, the FSH based on polyacrylamide/sodium alginate system shows an ultralow Young's modulus (1.68 kPa), increased sustainable strain (1411%), enhanced fracture toughness (915.6 J m-2 ), improved tensile sensitivity (21.77), and compressive sensitivity (65.23 kPa-1 ). The FSHs are used for precisely acquiring and identifying gesture commands of the operator to remotely control a surgical robot for endoscopy and an electric ship in a first-person perspective for cruising, feeding crabs and monitoring the environmental change in real-time.
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Affiliation(s)
- Zengyu Hui
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, P. R. China
- Institute of Advanced Materials (IAM) & Key Laboratory of Flexible Electronics (KLoFE), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing Tech University (Nanjing Tech), 30 South Puzhu Road, Nanjing, 211816, P. R. China
| | - Zhao Zhang
- College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing, 211199, P. R. China
| | - Yurong Wang
- Institute of Advanced Materials (IAM) & Key Laboratory of Flexible Electronics (KLoFE), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing Tech University (Nanjing Tech), 30 South Puzhu Road, Nanjing, 211816, P. R. China
| | - Runrun Zhang
- College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing, 211199, P. R. China
| | - Xin Liu
- College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing, 211199, P. R. China
| | - Mingjie Jiang
- School of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, P. R. China
| | - Feng Ju
- School of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, P. R. China
| | - Wenteng Hou
- Institute of Advanced Materials (IAM) & Key Laboratory of Flexible Electronics (KLoFE), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing Tech University (Nanjing Tech), 30 South Puzhu Road, Nanjing, 211816, P. R. China
| | - Zhongming Xia
- Institute of Advanced Materials (IAM) & Key Laboratory of Flexible Electronics (KLoFE), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing Tech University (Nanjing Tech), 30 South Puzhu Road, Nanjing, 211816, P. R. China
| | - Deya Wang
- CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics, University of Science and Technology of China, Hefei, 230027, P. R. China
| | - Pengfei Wang
- CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics, University of Science and Technology of China, Hefei, 230027, P. R. China
| | - Yangyang Pei
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, P. R. China
| | - Ren Yan
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, P. R. China
| | - Yan Zhang
- College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing, 211199, P. R. China
| | - Qiang Chen
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 352001, P. R. China
| | - Wei Huang
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, P. R. China
- Institute of Advanced Materials (IAM) & Key Laboratory of Flexible Electronics (KLoFE), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing Tech University (Nanjing Tech), 30 South Puzhu Road, Nanjing, 211816, P. R. China
| | - Gengzhi Sun
- Institute of Advanced Materials (IAM) & Key Laboratory of Flexible Electronics (KLoFE), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing Tech University (Nanjing Tech), 30 South Puzhu Road, Nanjing, 211816, P. R. China
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12
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Liu X, Sun C, Ye X, Zhu X, Hu C, Tan H, He S, Shao M, Li RW. Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2311472. [PMID: 38421081 DOI: 10.1002/adma.202311472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/06/2024] [Indexed: 03/02/2024]
Abstract
Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.
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Affiliation(s)
- Xuerong Liu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Cui Sun
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Xiaoyu Ye
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Xiaojian Zhu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Cong Hu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Hongwei Tan
- Department of Applied Physics, Aalto University, Aalto, FI-00076, Finland
| | - Shang He
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Mengjie Shao
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Run-Wei Li
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
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13
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Wan C, Pei M, Shi K, Cui H, Long H, Qiao L, Xing Q, Wan Q. Toward a Brain-Neuromorphics Interface. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2311288. [PMID: 38339866 DOI: 10.1002/adma.202311288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/17/2024] [Indexed: 02/12/2024]
Abstract
Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.
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Affiliation(s)
- Changjin Wan
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, China
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
| | - Mengjiao Pei
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Kailu Shi
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Hangyuan Cui
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Haotian Long
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Lesheng Qiao
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qianye Xing
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
| | - Qing Wan
- Yongjiang Laboratory (Y-LAB), Ningbo, Zhejiang, 315202, China
- School of Electronic Science and Engineering, National Laboratory of Solid-State Microstructures, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China
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14
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Sun C, Liu X, Yao Q, Jiang Q, Xia X, Shen Y, Ye X, Tan H, Gao R, Zhu X, Li RW. A Discolorable Flexible Synaptic Transistor for Wearable Health Monitoring. ACS NANO 2024; 18:515-525. [PMID: 38126328 DOI: 10.1021/acsnano.3c08357] [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: 12/23/2023]
Abstract
Multifunctional intelligent wearable electronics, providing integrated physiological signal analysis, storage, and display for real-time and on-site health status diagnosis, have great potential to revolutionize health monitoring technologies. Advanced wearable systems combine isolated digital processor, memory, and display modules for function integration; however, they suffer from compatibility and reliability issues. Here, we introduce a flexible multifunctional electrolyte-gated transistor (EGT) that integrates synaptic learning, memory, and autonomous discoloration functionalities for intelligent wearable application. This device exhibits synergistic light absorption coefficient changes during voltage-gated ion doping that modulate the electrical conductance changes for synaptic function implementation. By adaptively changing color, the EGT can differentiate voltage pulse inputs with different frequency, amplitude, and duration parameters, exhibiting excellent reversibility and reliability. We developed a smart wearable monitoring system that incorporates EGT devices and sensors for respiratory and electrocardiogram signal analysis, providing health warnings through real-time and on-site discoloration. This study represents a significant step toward smart wearable technologies for health management, offering health evaluation through intelligent displays.
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Affiliation(s)
- Cui Sun
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Xuerong Liu
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Quanxing Yao
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Qian Jiang
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- College of Materials Sciences and Optoelectronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiangling Xia
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- College of Materials Sciences and Optoelectronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Youfeng Shen
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- College of Materials Sciences and Optoelectronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoyu Ye
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- College of Materials Sciences and Optoelectronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hongwei Tan
- Department of Applied Physics, Aalto University, Aalto FI-00076, Finland
| | - Runsheng Gao
- National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan
| | - Xiaojian Zhu
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
| | - Run-Wei Li
- CAS Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
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Abstract
Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.
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Affiliation(s)
- Ke He
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Cong Wang
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Yongli He
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Jiangtao Su
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Xiaodong Chen
- Innovative Centre for Flexible Devices (iFLEX), Max Planck-NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore 636921, Singapore
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Fan S, Wu E, Cao M, Xu T, Liu T, Yang L, Su J, Liu J. Flexible In-Ga-Zn-N-O synaptic transistors for ultralow-power neuromorphic computing and EEG-based brain-computer interfaces. MATERIALS HORIZONS 2023; 10:4317-4328. [PMID: 37431592 DOI: 10.1039/d3mh00759f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/12/2023]
Abstract
Designing low-power and flexible artificial neural devices with artificial neural networks is a promising avenue for creating brain-computer interfaces (BCIs). Herein, we report the development of flexible In-Ga-Zn-N-O synaptic transistors (FISTs) that can simulate essential and advanced biological neural functions. These FISTs are optimized to achieve ultra-low power consumption under a super-low or even zero channel bias, making them suitable for wearable BCI applications. The effective tunability of synaptic behaviors promotes the realization of associative and non-associative learning, facilitating Covid-19 chest CT edge detection. Importantly, FISTs exhibit high tolerance to long-term exposure under an ambient environment and bending deformation, indicating their suitability for wearable BCI systems. We demonstrate that an array of FISTs can classify vision-evoked EEG signals with up to ∼87.9% and 94.8% recognition accuracy for EMNIST-Digits and MindBigdata, respectively. Thus, FISTs have enormous potential to significantly impact the development of various BCI techniques.
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Affiliation(s)
- Shuangqing Fan
- College of Electronics and Information, Qingdao University, Qingdao 266071, China.
| | - Enxiu Wu
- State Key Laboratory of Precision Measurement Technology and Instruments, School of Precision Instruments and Opto-electronics Engineering, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China.
| | - Minghui Cao
- College of Electronics and Information, Qingdao University, Qingdao 266071, China.
| | - Ting Xu
- State Key Laboratory of Precision Measurement Technology and Instruments, School of Precision Instruments and Opto-electronics Engineering, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China.
| | - Tong Liu
- State Key Laboratory of Precision Measurement Technology and Instruments, School of Precision Instruments and Opto-electronics Engineering, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China.
| | - Lijun Yang
- Key Laboratory of Radiopharmacokinetics for Innovative Drugs, Chinese Academy of Medical Sciences, Tianjin Key Laboratory of Radiation Medicine and Molecular Nuclear Medicine, Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, P. R. China.
| | - Jie Su
- College of Electronics and Information, Qingdao University, Qingdao 266071, China.
| | - Jing Liu
- State Key Laboratory of Precision Measurement Technology and Instruments, School of Precision Instruments and Opto-electronics Engineering, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China.
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Kim H, Oh S, Choo H, Kang DH, Park JH. Tactile Neuromorphic System: Convergence of Triboelectric Polymer Sensor and Ferroelectric Polymer Synapse. ACS NANO 2023; 17:17332-17341. [PMID: 37611149 DOI: 10.1021/acsnano.3c05337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Abstract
Sensory neuromorphic systems are a promising technology, because they can replicate the way the human peripheral nervous system processes signals from the five sensory organs. Despite this potential, there are limited studies on how to implement these systems on a hardware neural network platform. In our research, we propose a tactile neuromorphic system that uses a poly(dimethylsiloxane) (PDMS)-based triboelectric sensor and a molybdenum disulfide (MoS2)/poly(vinylidene fluoride-trifluoro ethylene) (P(VDF-TrFE)) heterostructure-based ferroelectric synapse. The triboelectric sensor mimics a human tactile organ by converting tactile stimuli into electrical signals in real time. The ferroelectric synapse we developed demonstrates exceptional long-term potentiation/depression characteristics with a maximum dynamic range of 78 and a symmetrical value of 4.7. To assess the practicality of our proposed system, we conducted training and recognition simulations using Morse code alphabets and MNIST handwritten digits. The maximum recognition rate that we achieved was 96.17%.
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Affiliation(s)
- Hyeongjun Kim
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon 16419, South Korea
| | - Seyong Oh
- Division of Electrical Engineering, Hanyang University ERICA, Ansan 15588, South Korea
| | - Hyongsuk Choo
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon 16419, South Korea
| | - Dong-Ho Kang
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, South Korea
| | - Jin-Hong Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon 16419, South Korea
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University (SKKU), Suwon 16417, Korea
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Assi DS, Huang H, Karthikeyan V, Theja VCS, de Souza MM, Xi N, Li WJ, Roy VAL. Quantum Topological Neuristors for Advanced Neuromorphic Intelligent Systems. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2300791. [PMID: 37340871 PMCID: PMC10460853 DOI: 10.1002/advs.202300791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 04/02/2023] [Indexed: 06/22/2023]
Abstract
Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning-relearning-forgetting stages is demonstrated. Critically, to emulate the real-time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision-making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next-gen neuromorphic computing for the development of intelligent machines and humanoids.
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Affiliation(s)
- Dani S. Assi
- Electronics and Nanoscale EngineeringJames Watt School of EngineeringUniversity of GlasgowGlasgowG12 8QQUK
| | - Hongli Huang
- Electronics and Nanoscale EngineeringJames Watt School of EngineeringUniversity of GlasgowGlasgowG12 8QQUK
| | - Vaithinathan Karthikeyan
- Electronics and Nanoscale EngineeringJames Watt School of EngineeringUniversity of GlasgowGlasgowG12 8QQUK
| | - Vaskuri C. S. Theja
- Materials Science and EngineeringCity University of Hong KongTat Chee AvenueHong KongHong Kong
| | | | - Ning Xi
- Industrial and Manufacturing Systems EngineeringThe University of Hong KongPokfulam RoadHong KongHong Kong
| | - Wen Jung Li
- Mechanical EngineeringCity University of Hong KongTat Chee AvenueHong KongHong Kong
| | - Vellaisamy A. L. Roy
- School of Science and TechnologyHong Kong Metropolitan UniversityHo Man TinHong KongHong Kong
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19
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Levin A, Gong S, Cheng W. Wearable Smart Bandage-Based Bio-Sensors. BIOSENSORS 2023; 13:bios13040462. [PMID: 37185537 PMCID: PMC10136806 DOI: 10.3390/bios13040462] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 05/17/2023]
Abstract
Bandage is a well-established industry, whereas wearable electronics is an emerging industry. This review presents the bandage as the base of wearable bioelectronics. It begins with introducing a detailed background to bandages and the development of bandage-based smart sensors, which is followed by a sequential discussion of the technical characteristics of the existing bandages, a more practical methodology for future applications, and manufacturing processes of bandage-based wearable biosensors. The review then elaborates on the advantages of basing the next generation of wearables, such as acceptance by the customers and system approvals, and disposal.
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Affiliation(s)
- Arie Levin
- Department of Chemical & Biological Engineering, Faculty of Engineering, Monash University, Clayton, VIC 3168, Australia
| | - Shu Gong
- Department of Chemical & Biological Engineering, Faculty of Engineering, Monash University, Clayton, VIC 3168, Australia
| | - Wenlong Cheng
- Department of Chemical & Biological Engineering, Faculty of Engineering, Monash University, Clayton, VIC 3168, Australia
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20
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Luo Y, Abidian MR, Ahn JH, Akinwande D, Andrews AM, Antonietti M, Bao Z, Berggren M, Berkey CA, Bettinger CJ, Chen J, Chen P, Cheng W, Cheng X, Choi SJ, Chortos A, Dagdeviren C, Dauskardt RH, Di CA, Dickey MD, Duan X, Facchetti A, Fan Z, Fang Y, Feng J, Feng X, Gao H, Gao W, Gong X, Guo CF, Guo X, Hartel MC, He Z, Ho JS, Hu Y, Huang Q, Huang Y, Huo F, Hussain MM, Javey A, Jeong U, Jiang C, Jiang X, Kang J, Karnaushenko D, Khademhosseini A, Kim DH, Kim ID, Kireev D, Kong L, Lee C, Lee NE, Lee PS, Lee TW, Li F, Li J, Liang C, Lim CT, Lin Y, Lipomi DJ, Liu J, Liu K, Liu N, Liu R, Liu Y, Liu Y, Liu Z, Liu Z, Loh XJ, Lu N, Lv Z, Magdassi S, Malliaras GG, Matsuhisa N, Nathan A, Niu S, Pan J, Pang C, Pei Q, Peng H, Qi D, Ren H, Rogers JA, Rowe A, Schmidt OG, Sekitani T, Seo DG, Shen G, Sheng X, Shi Q, Someya T, Song Y, Stavrinidou E, Su M, Sun X, Takei K, Tao XM, Tee BCK, Thean AVY, Trung TQ, Wan C, Wang H, Wang J, Wang M, Wang S, Wang T, Wang ZL, Weiss PS, Wen H, Xu S, Xu T, Yan H, Yan X, Yang H, Yang L, Yang S, Yin L, Yu C, Yu G, Yu J, Yu SH, Yu X, Zamburg E, Zhang H, Zhang X, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhao S, Zhao X, Zheng Y, Zheng YQ, Zheng Z, Zhou T, Zhu B, Zhu M, Zhu R, Zhu Y, Zhu Y, Zou G, Chen X. Technology Roadmap for Flexible Sensors. ACS NANO 2023; 17:5211-5295. [PMID: 36892156 PMCID: PMC11223676 DOI: 10.1021/acsnano.2c12606] [Citation(s) in RCA: 194] [Impact Index Per Article: 194.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.
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Affiliation(s)
- Yifei Luo
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
- Innovative Centre for Flexible Devices (iFLEX), School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Mohammad Reza Abidian
- Department of Biomedical Engineering, University of Houston, Houston, Texas 77024, United States
| | - Jong-Hyun Ahn
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Deji Akinwande
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Anne M Andrews
- Department of Chemistry and Biochemistry, California NanoSystems Institute, and Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, and Hatos Center for Neuropharmacology, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Markus Antonietti
- Colloid Chemistry Department, Max Planck Institute of Colloids and Interfaces, 14476 Potsdam, Germany
| | - Zhenan Bao
- Department of Chemical Engineering, Stanford University, Stanford, California 94305, United States
| | - Magnus Berggren
- Laboratory of Organic Electronics, Department of Science and Technology, Campus Norrköping, Linköping University, 83 Linköping, Sweden
- Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC), SE-100 44 Stockholm, Sweden
| | - Christopher A Berkey
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94301, United States
| | - Christopher John Bettinger
- Department of Biomedical Engineering and Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Jun Chen
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Peng Chen
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637457, Singapore
| | - Wenlong Cheng
- Nanobionics Group, Department of Chemical and Biological Engineering, Monash University, Clayton, Australia, 3800
- Monash Institute of Medical Engineering, Monash University, Clayton, Australia3800
| | - Xu Cheng
- Applied Mechanics Laboratory, Department of Engineering Mechanics, Laboratory of Flexible Electronics Technology, Tsinghua University, Beijing 100084, PR China
| | - Seon-Jin Choi
- Division of Materials of Science and Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Alex Chortos
- School of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Canan Dagdeviren
- Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Reinhold H Dauskardt
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94301, United States
| | - Chong-An Di
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - Michael D Dickey
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Xiangfeng Duan
- Department of Chemistry and Biochemistry, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Antonio Facchetti
- Department of Chemistry and the Materials Research Center, Northwestern University, Evanston, Illinois 60208, United States
| | - Zhiyong Fan
- Department of Electronic and Computer Engineering and Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
| | - Yin Fang
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637457, Singapore
| | - Jianyou Feng
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, Shanghai 200438, PR China
| | - Xue Feng
- Laboratory of Flexible Electronics Technology, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China
| | - Huajian Gao
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Republic of Singapore
| | - Wei Gao
- Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, California, 91125, United States
| | - Xiwen Gong
- Department of Chemical Engineering, Department of Materials Science and Engineering, Department of Electrical Engineering and Computer Science, Applied Physics Program, and Macromolecular Science and Engineering Program, University of Michigan, Ann Arbor, Michigan, 48109 United States
| | - Chuan Fei Guo
- Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Xiaojun Guo
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Martin C Hartel
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Zihan He
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - John S Ho
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 117599, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- The N.1 Institute for Health, National University of Singapore, Singapore 117456, Singapore
| | - Youfan Hu
- School of Electronics and Center for Carbon-Based Electronics, Peking University, Beijing 100871, China
| | - Qiyao Huang
- School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Yu Huang
- Department of Materials Science and Engineering, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Fengwei Huo
- Key Laboratory of Flexible Electronics (KLOFE) and Institute of Advanced Materials (IAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing 211816, PR China
| | - Muhammad M Hussain
- mmh Labs, Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Ali Javey
- Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, United States
- Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Unyong Jeong
- Department of Materials Science and Engineering, Pohang University of Science and Engineering (POSTECH), Pohang, Gyeong-buk 37673, Korea
| | - Chen Jiang
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
| | - Xingyu Jiang
- Department of Biomedical Engineering, Southern University of Science and Technology, No 1088, Xueyuan Road, Xili, Nanshan District, Shenzhen, Guangdong 518055, PR China
| | - Jiheong Kang
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Daniil Karnaushenko
- Research Center for Materials, Architectures and Integration of Nanomembranes (MAIN), Chemnitz University of Technology, Chemnitz 09126, Germany
| | | | - Dae-Hyeong Kim
- Center for Nanoparticle Research, Institute for Basic Science (IBS), School of Chemical and Biological Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Il-Doo Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Dmitry Kireev
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Lingxuan Kong
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637457, Singapore
| | - Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore 117608, Singapore
- National University of Singapore Suzhou Research Institute (NUSRI), Suzhou Industrial Park, Suzhou 215123, China
- NUS Graduate School-Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore 119077, Singapore
| | - Nae-Eung Lee
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, Kyunggi-do 16419, Republic of Korea
| | - Pooi See Lee
- School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
- Singapore-HUJ Alliance for Research and Enterprise (SHARE), Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Tae-Woo Lee
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
- School of Chemical and Biological Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Institute of Engineering Research, Research Institute of Advanced Materials, Seoul National University, Soft Foundry, Seoul 08826, Republic of Korea
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Fengyu Li
- College of Chemistry and Materials Science, Jinan University, Guangzhou, Guangdong 510632, China
| | - Jinxing Li
- Department of Biomedical Engineering, Department of Electrical and Computer Engineering, Neuroscience Program, BioMolecular Science Program, and Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan 48823, United States
| | - Cuiyuan Liang
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Chwee Teck Lim
- Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore
- Mechanobiology Institute, National University of Singapore, Singapore 117411, Singapore
- Institute for Health Innovation and Technology, National University of Singapore, Singapore 119276, Singapore
| | - Yuanjing Lin
- School of Microelectronics, Southern University of Science and Technology, Shenzhen 518055, China
| | - Darren J Lipomi
- Department of Nano and Chemical Engineering, University of California, San Diego, La Jolla, California 92093-0448, United States
| | - Jia Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, 02134, United States
| | - Kai Liu
- School of Chemistry and Chemical Engineering, Frontiers Science Center for Transformative Molecules, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Nan Liu
- Beijing Key Laboratory of Energy Conversion and Storage Materials, College of Chemistry, Beijing Normal University, Beijing 100875, PR China
| | - Ren Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, 02134, United States
| | - Yuxin Liu
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
- Department of Biomedical Engineering, N.1 Institute for Health, Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore 119077, Singapore
| | - Yuxuan Liu
- Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Zhiyuan Liu
- Neural Engineering Centre, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China 518055
| | - Zhuangjian Liu
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Republic of Singapore
| | - Xian Jun Loh
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
| | - Nanshu Lu
- Department of Aerospace Engineering and Engineering Mechanics, Department of Electrical and Computer Engineering, Department of Mechanical Engineering, Department of Biomedical Engineering, Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Zhisheng Lv
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
| | - Shlomo Magdassi
- Institute of Chemistry and the Center for Nanoscience and Nanotechnology, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - George G Malliaras
- Electrical Engineering Division, Department of Engineering, University of Cambridge CB3 0FA, Cambridge United Kingdom
| | - Naoji Matsuhisa
- Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
| | - Arokia Nathan
- Darwin College, University of Cambridge, Cambridge CB3 9EU, United Kingdom
| | - Simiao Niu
- Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Jieming Pan
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
| | - Changhyun Pang
- School of Chemical Engineering and Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Qibing Pei
- Department of Materials Science and Engineering, Department of Mechanical and Aerospace Engineering, California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Huisheng Peng
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, Shanghai 200438, PR China
| | - Dianpeng Qi
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Huaying Ren
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, 90095, United States
| | - John A Rogers
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, Illinois 60208, United States
- Department of Materials Science and Engineering, Department of Mechanical Engineering, Department of Biomedical Engineering, Departments of Electrical and Computer Engineering and Chemistry, and Department of Neurological Surgery, Northwestern University, Evanston, Illinois 60208, United States
| | - Aaron Rowe
- Becton, Dickinson and Company, 1268 N. Lakeview Avenue, Anaheim, California 92807, United States
- Ready, Set, Food! 15821 Ventura Blvd #450, Encino, California 91436, United States
| | - Oliver G Schmidt
- Research Center for Materials, Architectures and Integration of Nanomembranes (MAIN), Chemnitz University of Technology, Chemnitz 09126, Germany
- Material Systems for Nanoelectronics, Chemnitz University of Technology, Chemnitz 09107, Germany
- Nanophysics, Faculty of Physics, TU Dresden, Dresden 01062, Germany
| | - Tsuyoshi Sekitani
- The Institute of Scientific and Industrial Research (SANKEN), Osaka University, Osaka, Japan 5670047
| | - Dae-Gyo Seo
- Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Guozhen Shen
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Xing Sheng
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Institute for Precision Medicine, Center for Flexible Electronics Technology, and IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, China
| | - Qiongfeng Shi
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore 117608, Singapore
- National University of Singapore Suzhou Research Institute (NUSRI), Suzhou Industrial Park, Suzhou 215123, China
| | - Takao Someya
- Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
| | - Yanlin Song
- Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences, Beijing, Beijing 100190, China
| | - Eleni Stavrinidou
- Laboratory of Organic Electronics, Department of Science and Technology, Linköping University, SE-601 74 Norrkoping, Sweden
| | - Meng Su
- Key Laboratory of Green Printing, Institute of Chemistry, Chinese Academy of Sciences, Beijing, Beijing 100190, China
| | - Xuemei Sun
- State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, Shanghai 200438, PR China
| | - Kuniharu Takei
- Department of Physics and Electronics, Osaka Metropolitan University, Sakai, Osaka 599-8531, Japan
| | - Xiao-Ming Tao
- Research Institute for Intelligent Wearable Systems, School of Fashion and Textiles, Hong Kong Polytechnic University, Hong Kong, China
| | - Benjamin C K Tee
- Materials Science and Engineering, National University of Singapore, Singapore 117575, Singapore
- iHealthtech, National University of Singapore, Singapore 119276, Singapore
| | - Aaron Voon-Yew Thean
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Singapore Hybrid-Integrated Next-Generation μ-Electronics Centre (SHINE), Singapore 117583, Singapore
| | - Tran Quang Trung
- School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, Kyunggi-do 16419, Republic of Korea
| | - Changjin Wan
- School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China
| | - Huiliang Wang
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas 78712, United States
| | - Joseph Wang
- Department of Nanoengineering, University of California, San Diego, California 92093, United States
| | - Ming Wang
- Frontier Institute of Chip and System, State Key Laboratory of Integrated Chip and Systems, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 200433, China
- the Shanghai Qi Zhi Institute, 41th Floor, AI Tower, No.701 Yunjin Road, Xuhui District, Shanghai 200232, China
| | - Sihong Wang
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois, 60637, United States
| | - Ting Wang
- State Key Laboratory of Organic Electronics and Information Displays and Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Nanjing University of Posts and Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Zhong Lin Wang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 100083, China
- Georgia Institute of Technology, Atlanta, Georgia 30332-0245, United States
| | - Paul S Weiss
- California NanoSystems Institute, Department of Chemistry and Biochemistry, Department of Bioengineering, and Department of Materials Science and Engineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Hanqi Wen
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637457, Singapore
- Institute of Flexible Electronics Technology of THU, Jiaxing, Zhejiang, China 314000
| | - Sheng Xu
- Department of Nanoengineering, Department of Electrical and Computer Engineering, Materials Science and Engineering Program, and Department of Bioengineering, University of California San Diego, La Jolla, California, 92093, United States
| | - Tailin Xu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, 518060, PR China
| | - Hongping Yan
- Department of Chemical Engineering, Stanford University, Stanford, California 94305, United States
| | - Xuzhou Yan
- School of Chemistry and Chemical Engineering, Frontiers Science Center for Transformative Molecules, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Hui Yang
- Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin, China, 300072
| | - Le Yang
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
- Department of Materials Science and Engineering, National University of Singapore (NUS), 9 Engineering Drive 1, #03-09 EA, Singapore 117575, Singapore
| | - Shuaijian Yang
- School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, United Kingdom
| | - Lan Yin
- School of Materials Science and Engineering, The Key Laboratory of Advanced Materials of Ministry of Education, State Key Laboratory of New Ceramics and Fine Processing, and Center for Flexible Electronics Technology, Tsinghua University, Beijing, 100084, China
| | - Cunjiang Yu
- Department of Engineering Science and Mechanics, Department of Biomedical Engineering, Department of Material Science and Engineering, Materials Research Institute, Pennsylvania State University, University Park, Pennsylvania, 16802, United States
| | - Guihua Yu
- Materials Science and Engineering Program and Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas, 78712, United States
| | - Jing Yu
- School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Shu-Hong Yu
- Department of Chemistry, Institute of Biomimetic Materials and Chemistry, Hefei National Research Center for Physical Science at the Microscale, University of Science and Technology of China, Hefei 230026, China
| | - Xinge Yu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Evgeny Zamburg
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Singapore Hybrid-Integrated Next-Generation μ-Electronics Centre (SHINE), Singapore 117583, Singapore
| | - Haixia Zhang
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication; Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing 100871, China
| | - Xiangyu Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Singapore Hybrid-Integrated Next-Generation μ-Electronics Centre (SHINE), Singapore 117583, Singapore
| | - Xiaosheng Zhang
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xueji Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, PR China
| | - Yihui Zhang
- Applied Mechanics Laboratory, Department of Engineering Mechanics; Laboratory of Flexible Electronics Technology, Tsinghua University, Beijing 100084, PR China
| | - Yu Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Singapore Hybrid-Integrated Next-Generation μ-Electronics Centre (SHINE), Singapore 117583, Singapore
| | - Siyuan Zhao
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, 02134, United States
| | - Xuanhe Zhao
- Department of Mechanical Engineering, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, United States
| | - Yuanjin Zheng
- Center for Integrated Circuits and Systems, School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Yu-Qing Zheng
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication; School of Integrated Circuits, Peking University, Beijing 100871, China
| | - Zijian Zheng
- Department of Applied Biology and Chemical Technology, Faculty of Science, Research Institute for Intelligent Wearable Systems, Research Institute for Smart Energy, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Tao Zhou
- Center for Neural Engineering, Department of Engineering Science and Mechanics, The Huck Institutes of the Life Sciences, Materials Research Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Bowen Zhu
- Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Ming Zhu
- Institute for Digital Molecular Analytics and Science (IDMxS), Nanyang Technological University, 59 Nanyang Drive, Singapore 636921, Singapore
| | - Rong Zhu
- Department of Precision Instrument, Tsinghua University, Beijing 100084, China
| | - Yangzhi Zhu
- Terasaki Institute for Biomedical Innovation, Los Angeles, California, 90064, United States
| | - Yong Zhu
- Department of Mechanical and Aerospace Engineering, Department of Materials Science and Engineering, and Department of Biomedical Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Guijin Zou
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Republic of Singapore
| | - Xiaodong Chen
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore
- Innovative Center for Flexible Devices (iFLEX), Max Planck-NTU Joint Laboratory for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
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Hwang Y, Park B, Hwang S, Choi SW, Kim HS, Kim AR, Choi JW, Yoon J, Kwon JD, Kim Y. A Bioinspired Ultra Flexible Artificial van der Waals 2D-MoS 2 Channel/LiSiO x Solid Electrolyte Synapse Arrays via Laser-Lift Off Process for Wearable Adaptive Neuromorphic Computing. SMALL METHODS 2023:e2201719. [PMID: 36960927 DOI: 10.1002/smtd.202201719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 02/06/2023] [Indexed: 06/18/2023]
Abstract
Wearable electronic devices with next-generation biocompatible, mechanical, ultraflexible, and portable sensors are a fast-growing technology. Hardware systems enabling artificial neural networks while consuming low power and processing massive in situ personal data are essential for adaptive wearable neuromorphic edging computing. Herein, the development of an ultraflexible artificial-synaptic array device with concrete-mechanical cyclic endurance consisting of a novel heterostructure with an all-solid-state 2D MoS2 channel and LiSiOx (lithium silicate) is demonstrated. Enabled by the sequential fabrication process of all layers, by excluding the transfer process, artificial van der Waals devices combined with the 2D-MoS2 channel and LiSiOx solid electrolyte exhibit excellent neuromorphic synaptic characteristics with a nonlinearity of 0.55 and asymmetry ratio of 0.22. Based on the excellent flexibility of colorless polyimide substrates and thin-layered structures, the fabricated flexible neuromorphic synaptic devices exhibit superior long-term potentiation and long-term depression cyclic endurance performance, even when bent over 700 times or on curved surfaces with a diameter of 10 mm. Thus, a high classification accuracy of 95% is achieved without any noticeable performance degradation in the Modified National Institute of Standards and Technology. These results are promising for the development of personalized wearable artificial neural systems in the future.
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Affiliation(s)
- Yunjeong Hwang
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
| | - Byeongjin Park
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan, 46241, Republic of Korea
| | - Seungkwon Hwang
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan, 46241, Republic of Korea
| | - Soo-Won Choi
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan, 46241, Republic of Korea
| | - Han Seul Kim
- Department of Advanced Materials Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, 28644, Republic of Korea
| | - Ah Ra Kim
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
| | - Jin Woo Choi
- Department of Data Information and Physics, Kongju National University, 56 Gongjudaehak-ro, Gongju, Chungcheongnam-do, 32588, Republic ofKorea
| | - Jongwon Yoon
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
| | - Jung-Dae Kwon
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
| | - Yonghun Kim
- Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea
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22
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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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23
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Jiang C, Liu J, Ni Y, Qu S, Liu L, Li Y, Yang L, Xu W. Mammalian-brain-inspired neuromorphic motion-cognition nerve achieves cross-modal perceptual enhancement. Nat Commun 2023; 14:1344. [PMID: 36906637 PMCID: PMC10008641 DOI: 10.1038/s41467-023-36935-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 02/21/2023] [Indexed: 03/13/2023] Open
Abstract
Perceptual enhancement of neural and behavioral response due to combinations of multisensory stimuli are found in many animal species across different sensory modalities. By mimicking the multisensory integration of ocular-vestibular cues for enhanced spatial perception in macaques, a bioinspired motion-cognition nerve based on a flexible multisensory neuromorphic device is demonstrated. A fast, scalable and solution-processed fabrication strategy is developed to prepare a nanoparticle-doped two-dimensional (2D)-nanoflake thin film, exhibiting superior electrostatic gating capability and charge-carrier mobility. The multi-input neuromorphic device fabricated using this thin film shows history-dependent plasticity, stable linear modulation, and spatiotemporal integration capability. These characteristics ensure parallel, efficient processing of bimodal motion signals encoded as spikes and assigned with different perceptual weights. Motion-cognition function is realized by classifying the motion types using mean firing rates of encoded spikes and postsynaptic current of the device. Demonstrations of recognition of human activity types and drone flight modes reveal that the motion-cognition performance match the bio-plausible principles of perceptual enhancement by multisensory integration. Our system can be potentially applied in sensory robotics and smart wearables.
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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, School of Materials Science and Engineering, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, 300350, China.,Shenzhen Research Institute of Nankai University, Shenzhen, 518000, China.,Research Center for Intelligent Sensing, Zhejiang Lab, Hangzhou, 311100, 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, School of Materials Science and Engineering, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, 300350, China.,Shenzhen Research Institute of Nankai University, Shenzhen, 518000, China
| | - Yao Ni
- 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, School of Materials Science and Engineering, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, 300350, China.,Shenzhen Research Institute of Nankai University, Shenzhen, 518000, China
| | - Shangda Qu
- 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, School of Materials Science and Engineering, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, 300350, China.,Shenzhen Research Institute of Nankai University, Shenzhen, 518000, China
| | - Lu 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, School of Materials Science and Engineering, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, 300350, China.,Shenzhen Research Institute of Nankai University, Shenzhen, 518000, 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, School of Materials Science and Engineering, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, 300350, China.,Shenzhen Research Institute of Nankai University, Shenzhen, 518000, 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, School of Materials Science and Engineering, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, 300350, China.,Shenzhen Research Institute of Nankai University, Shenzhen, 518000, China
| | - 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, School of Materials Science and Engineering, Smart Sensing Interdisciplinary Science Center, Nankai University, Tianjin, 300350, China. .,Shenzhen Research Institute of Nankai University, Shenzhen, 518000, China.
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24
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Wang Y, Adam ML, Zhao Y, Zheng W, Gao L, Yin Z, Zhao H. Machine Learning-Enhanced Flexible Mechanical Sensing. NANO-MICRO LETTERS 2023; 15:55. [PMID: 36800133 PMCID: PMC9936950 DOI: 10.1007/s40820-023-01013-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/08/2023] [Indexed: 05/31/2023]
Abstract
To realize a hyperconnected smart society with high productivity, advances in flexible sensing technology are highly needed. Nowadays, flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device's software. Significant research efforts have been devoted to improving materials, sensing mechanism, and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology. Meanwhile, advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors. Machine learning (ML) as an important branch of artificial intelligence can efficiently handle such complex data, which can be multi-dimensional and multi-faceted, thus providing a powerful tool for easy interpretation of sensing data. In this review, the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented. Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated, which includes health monitoring, human-machine interfaces, object/surface recognition, pressure prediction, and human posture/motion identification. Finally, the advantages, challenges, and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed. These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.
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Affiliation(s)
- Yuejiao Wang
- Applied Mechanics Laboratory, Department of Engineering Mechanics, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Mukhtar Lawan Adam
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China
| | - Yunlong Zhao
- Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen, 361102, People's Republic of China
| | - Weihao Zheng
- School of Mechano-Electronic Engineering, Xidian University, Xi'an , 710071, People's Republic of China
| | - Libo Gao
- Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen, 361102, People's Republic of China.
| | - Zongyou Yin
- Research School of Chemistry, Australian National University, Canberra, ACT, 2601, Australia.
| | - Haitao Zhao
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China.
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25
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Sun C, Liu X, Jiang Q, Ye X, Zhu X, Li RW. Emerging electrolyte-gated transistors for neuromorphic perception. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2162325. [PMID: 36684849 PMCID: PMC9848240 DOI: 10.1080/14686996.2022.2162325] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/18/2022] [Accepted: 12/21/2022] [Indexed: 05/31/2023]
Abstract
With the rapid development of intelligent robotics, the Internet of Things, and smart sensor technologies, great enthusiasm has been devoted to developing next-generation intelligent systems for the emulation of advanced perception functions of humans. Neuromorphic devices, capable of emulating the learning, memory, analysis, and recognition functions of biological neural systems, offer solutions to intelligently process sensory information. As one of the most important neuromorphic devices, Electrolyte-gated transistors (EGTs) have shown great promise in implementing various vital neural functions and good compatibility with sensors. This review introduces the materials, operating principle, and performances of EGTs, followed by discussing the recent progress of EGTs for synapse and neuron emulation. Integrating EGTs with sensors that faithfully emulate diverse perception functions of humans such as tactile and visual perception is discussed. The challenges of EGTs for further development are given.
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Affiliation(s)
- Cui Sun
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Xuerong Liu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Qian Jiang
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoyu Ye
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaojian Zhu
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Run-Wei Li
- CAS Key Laboratory of Magnetic Materials and Devices, and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- College of Materials Sciences and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
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26
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Roe DG, Ho DH, Choi YY, Choi YJ, Kim S, Jo SB, Kang MS, Ahn JH, Cho JH. Humanlike spontaneous motion coordination of robotic fingers through spatial multi-input spike signal multiplexing. Nat Commun 2023; 14:5. [PMID: 36596783 PMCID: PMC9810717 DOI: 10.1038/s41467-022-34324-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 10/19/2022] [Indexed: 01/05/2023] Open
Abstract
With advances in robotic technology, the complexity of control of robot has been increasing owing to fundamental signal bottlenecks and limited expressible logic state of the von Neumann architecture. Here, we demonstrate coordinated movement by a fully parallel-processable synaptic array with reduced control complexity. The synaptic array was fabricated by connecting eight ion-gel-based synaptic transistors to an ion gel dielectric. Parallel signal processing and multi-actuation control could be achieved by modulating the ionic movement. Through the integration of the synaptic array and a robotic hand, coordinated movement of the fingers was achieved with reduced control complexity by exploiting the advantages of parallel multiplexing and analog logic. The proposed synaptic control system provides considerable scope for the advancement of robotic control systems.
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Affiliation(s)
- Dong Gue Roe
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Dong Hae Ho
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Yoon Young Choi
- Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Young Jin Choi
- 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
| | - Sae Byeok Jo
- School of Chemical Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Moon Sung Kang
- Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul, 04107, Republic of Korea
| | - Jong-Hyun Ahn
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, 03722, Republic of Korea
| | - Jeong Ho Cho
- Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
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27
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Zheng C, Liao Y, Wang J, Zhou Y, Han ST. Flexible Floating-Gate Electric-Double-Layer Organic Transistor for Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2022; 14:57102-57112. [PMID: 36516355 DOI: 10.1021/acsami.2c20925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The key to the study of flexible neuromorphic computing is the excellent weight update characteristic of neuromorphic devices. Electric-double-layer transistors (EDLTs) include high transconductance, excellent stability of threshold voltage, linear weight updates, and repetitive ion-concentration-dependent switching properties. However, up to now, there is no report on a flexible EDLT that provides all the aforementioned performance characteristics. Here, a planar flexible floating-gate EDLT including an excellent linear/symmetric weight update, a large number (>800) of conductance states, repetitive switching endurance (>100 cycles), and low variation in weight update is reported. After 800 signal stimulations, it is found that the nonlinearity values of LTP are between 0.20 and 0.85, those of LTD fall between 0.66 and 1.55, the symmetricity values are between 120.7 and 639.8, and the dynamic range is between 150 and 352 nS. The study of 8 × 8 flexible floating-gate EDLT arrays shows that the average deviation and standard deviation between the experimental and theoretical values are 1.36 and 1.93, respectively, indicating that the conductance regulation in the array has a relatively small deviation. The different bending angles and the mechanical stability of the floating-gate EDLT are also studied, which exhibit the excellent bending properties. Furthermore, we studied the recognition of MNIST handwritten digit images by a three-layer perceptron artificial neural network with the experimental weight update, and the maximal recognition accuracy is up to 87.8%.
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Affiliation(s)
- Chaoyue Zheng
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou313001, P.R. China
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen518060, P.R. China
| | - Yuan Liao
- Institute for Advanced Study, Shenzhen University, Shenzhen518060, P.R. China
| | - Junjie Wang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen518060, P.R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen518060, P.R. China
| | - Su-Ting Han
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen518060, P.R. China
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28
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Park K, Kang K, Kim J, Kim SD, Jin S, Shin M, Son D. Balanced Coexistence of Reversible and Irreversible Covalent Bonds in a Conductive Triple Polymeric Network Enables Stretchable Hydrogels with High Toughness and Adhesiveness. ACS APPLIED MATERIALS & INTERFACES 2022; 14:56395-56406. [PMID: 36484343 DOI: 10.1021/acsami.2c17676] [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: 06/17/2023]
Abstract
The application of soft hydrogels to stretchable devices has attracted increasing attention in deformable bioelectronics owing to their unique characteristic, "modulus matching between materials and organs". Despite considerable progress, their low toughness, low conductivity, and absence of tissue adhesiveness remain substantial challenges associated with unstable skin-interfacing, where body movements undesirably disturb electrical signal acquisitions. Herein, we report a material design of a highly tough strain-dissipative and skin-adhesive conducting hydrogel fabricated through a facile one-step sol-gel transition and its application to an interactive human-machine interface. The hydrogel comprises a triple polymeric network where irreversible amide linkage of polyacrylamide with alginate and dynamic covalent bonds entailing conjugated polymer chains of poly(3,4-ethylenedioxythiophene)-co-(3-thienylboronic acid) are simultaneously capable of high stretchability (1300% strain), efficient strain dissipation (36,209 J/m2), low electrical resistance (590 Ω), and even robust skin adhesiveness (35.0 ± 5.6 kPa). Based on such decent characteristics, the hydrogel was utilized as a multifunctional layer for successfully performing either electrophysiological cardiac/muscular on-skin sensors or an interactive stretchable human-machine interface.
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Affiliation(s)
- Kyuha Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon16419, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon16419, Republic of Korea
| | - Kyumin Kang
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon16419, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon16419, Republic of Korea
| | - Jungwoo Kim
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon16419, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University (SKKU), Suwon16419, Republic of Korea
| | - Sung Dong Kim
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon16419, Republic of Korea
- Department of Biomedical Engineering, Sungkyunkwan University (SKKU), Institute for Convergence, Suwon16419, Republic of Korea
| | - Subin Jin
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon16419, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University (SKKU), Suwon16419, Republic of Korea
| | - Mikyung Shin
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon16419, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University (SKKU), Suwon16419, Republic of Korea
- Department of Biomedical Engineering, Sungkyunkwan University (SKKU), Institute for Convergence, Suwon16419, Republic of Korea
| | - Donghee Son
- Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon16419, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon16419, Republic of Korea
- Department of Superintelligence Engineering, Sungkyunkwan University (SKKU), Suwon16419, Republic of Korea
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29
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Park B, Hwang Y, Kwon O, Hwang S, Lee JA, Choi DH, Lee SK, Kim AR, Cho B, Kwon JD, Lee JI, Kim Y. Robust 2D MoS 2 Artificial Synapse Device Based on a Lithium Silicate Solid Electrolyte for High-Precision Analogue Neuromorphic Computing. ACS APPLIED MATERIALS & INTERFACES 2022; 14:53038-53047. [PMID: 36394301 DOI: 10.1021/acsami.2c14080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
High-precision artificial synaptic devices compatible with existing CMOS technology are essential for realizing robust neuromorphic hardware systems with reliable parallel analogue computation beyond the von Neumann serial digital computing architecture. However, critical issues related to reliability and variability, such as nonlinearity and asymmetric weight updates, have been great challenges in the implementation of artificial synaptic devices in practical neuromorphic hardware systems. Herein, a robust three-terminal two-dimensional (2D) MoS2 artificial synaptic device combined with a lithium silicate (LSO) solid-state electrolyte thin film is proposed. The rationally designed synaptic device exhibits excellent linearity and symmetry upon electrical potentiation and depression, benefiting from the reversible intercalation of Li ions into the MoS2 channel. In particular, extremely low cycle-to-cycle variations (3.01%) during long-term potentiation and depression processes over 500 pulses are achieved, causing statistical analogue discrete states. Thus, a high classification accuracy of 96.77% (close to the software baseline of 98%) is demonstrated in the Modified National Institute of Standards and Technology (MNIST) simulations. These results provide a future perspective for robust synaptic device architecture of lithium solid-state electrolytes stacked with 2D van der Waals layered channels for high-precision analogue neuromorphic computing systems.
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Affiliation(s)
- Byeongjin Park
- Department of Energy and Electronic Materials, Nanosurface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon51508, Gyeongnam, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan46241, Republic of Korea
| | - Yunjeong Hwang
- Department of Energy and Electronic Materials, Nanosurface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon51508, Gyeongnam, Republic of Korea
| | - Ojun Kwon
- Department of Advanced Material Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju28644, Chungbuk, Republic of Korea
| | - Seungkwon Hwang
- Department of Energy and Electronic Materials, Nanosurface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon51508, Gyeongnam, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan46241, Republic of Korea
| | - Ju Ah Lee
- Department of Energy and Electronic Materials, Nanosurface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon51508, Gyeongnam, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan46241, Republic of Korea
| | - Dong-Hyeong Choi
- Department of Energy and Electronic Materials, Nanosurface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon51508, Gyeongnam, Republic of Korea
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan46241, Republic of Korea
| | - Seoung-Ki Lee
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan46241, Republic of Korea
| | - Ah Ra Kim
- Department of Energy and Electronic Materials, Nanosurface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon51508, Gyeongnam, Republic of Korea
| | - Byungjin Cho
- Department of Advanced Material Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju28644, Chungbuk, Republic of Korea
| | - Jung-Dae Kwon
- Department of Energy and Electronic Materials, Nanosurface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon51508, Gyeongnam, Republic of Korea
| | - Je In Lee
- School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan46241, Republic of Korea
| | - Yonghun Kim
- Department of Energy and Electronic Materials, Nanosurface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon51508, Gyeongnam, Republic of Korea
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30
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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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31
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Baek S, Yoo HH, Ju JH, Sriboriboon P, Singh P, Niu J, Park J, Shin C, Kim Y, Lee S. Ferroelectric Field-Effect-Transistor Integrated with Ferroelectrics Heterostructure. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2200566. [PMID: 35570404 PMCID: PMC9313508 DOI: 10.1002/advs.202200566] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/21/2022] [Indexed: 05/28/2023]
Abstract
To address the demands of emerging data-centric computing applications, ferroelectric field-effect transistors (Fe-FETs) are considered the forefront of semiconductor electronics owing to their energy and area efficiency and merged logic-memory functionalities. Herein, the fabrication and application of an Fe-FET, which is integrated with a van der Waals ferroelectrics heterostructure (CuInP2 S6 /α-In2 Se3 ), is reported. Leveraging enhanced polarization originating from the dipole coupling of CIPS and α-In2 Se3 , the fabricated Fe-FET exhibits a large memory window of 14.5 V at VGS = ±10 V, reaching a memory window to sweep range of ≈72%. Piezoelectric force microscopy measurements confirm the enhanced polarization-induced wider hysteresis loop of the double-stacked ferroelectrics compared to single ferroelectric layers. The Landau-Khalatnikov theory is extended to analyze the ferroelectric characteristics of a ferroelectric heterostructure, providing detailed explanations of the hysteresis behaviors and enhanced memory window formation. The fabricated Fe-FET shows nonvolatile memory characteristics, with a high on/off current ratio of over 106 , long retention time (>104 s), and stable cyclic endurance (>104 cycles). Furthermore, the applicability of the ferroelectrics heterostructure is investigated for artificial synapses and for hardware neural networks through training and inference simulation. These results provide a promising pathway for exploring low-dimensional ferroelectronics.
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Affiliation(s)
- Sungpyo Baek
- SKKU Advanced Institute of Nanotechnology (SAINT)Sungkyunkwan UniversitySuwon440‐746Korea
| | - Hyun Ho Yoo
- SKKU Advanced Institute of Nanotechnology (SAINT)Sungkyunkwan UniversitySuwon440‐746Korea
| | - Jae Hyeok Ju
- SKKU Advanced Institute of Nanotechnology (SAINT)Sungkyunkwan UniversitySuwon440‐746Korea
| | - Panithan Sriboriboon
- School of Advanced Materials Science and EngineeringSungkyunkwan University (SKKU)Suwon440‐746Korea
| | - Prashant Singh
- SKKU Advanced Institute of Nanotechnology (SAINT)Sungkyunkwan UniversitySuwon440‐746Korea
| | - Jingjie Niu
- SKKU Advanced Institute of Nanotechnology (SAINT)Sungkyunkwan UniversitySuwon440‐746Korea
| | - Jin‐Hong Park
- SKKU Advanced Institute of Nanotechnology (SAINT)Sungkyunkwan UniversitySuwon440‐746Korea
| | - Changhwan Shin
- School of Electrical EngineeringKorea UniversitySeoul02841Korea
| | - Yunseok Kim
- School of Advanced Materials Science and EngineeringSungkyunkwan University (SKKU)Suwon440‐746Korea
| | - Sungjoo Lee
- SKKU Advanced Institute of Nanotechnology (SAINT)Sungkyunkwan UniversitySuwon440‐746Korea
- Department of Nano EngineeringSungkyunkwan UniversitySuwon440‐746Korea
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32
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Yang J, Lee J, Jung Y, Kim S, Kim J, Kim S, Kim J, Seo S, Park D, Lee J, Walsh A, Park J, Park N. Mixed-Dimensional Formamidinium Bismuth Iodides Featuring In-Situ Formed Type-I Band Structure for Convolution Neural Networks. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2200168. [PMID: 35307991 PMCID: PMC9108665 DOI: 10.1002/advs.202200168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Indexed: 06/14/2023]
Abstract
For valence change memory (VCM)-type synapses, a large number of vacancies help to achieve very linearly changed dynamic range, and also, the low activation energy of vacancies enables low-voltage operation. However, a large number of vacancies increases the current of artificial synapses by acting like dopants, which aggravates low-energy operation and device scalability. Here, mixed-dimensional formamidinium bismuth iodides featuring in-situ formed type-I band structure are reported for the VCM-type synapse. As compared to the pure 2D and 0D phases, the mixed phase increases defect density, which induces a better dynamic range and higher linearity. In addition, the mixed phase decreases conductivity for non-paths despite a large number of defects providing lots of conducting paths. Thus, the mixed phase-based memristor devices exhibit excellent potentiation/depression characteristics with asymmetricity of 3.15, 500 conductance states, a dynamic range of 15, pico ampere-scale current level, and energy consumption per spike of 61.08 aJ. A convolutional neural network (CNN) simulation with the Canadian Institute for Advanced Research-10 (CIFAR-10) dataset is also performed, confirming a maximum recognition rate of approximately 87%. This study is expected to lay the groundwork for future research on organic bismuth halide-based memristor synapses usable for a neuromorphic computing system.
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Affiliation(s)
- June‐Mo Yang
- School of Chemical EngineeringEnergy Frontier LaboratorySungkyunkwan UniversitySuwon16419Korea
| | - Ju‐Hee Lee
- Department of Electrical and Computer EngineeringSungkyunkwan UniversitySuwon16419Korea
| | - Young‐Kwang Jung
- Department of Materials Science and EngineeringYonsei UniversitySeoul03722Korea
| | - So‐Yeon Kim
- School of Chemical EngineeringEnergy Frontier LaboratorySungkyunkwan UniversitySuwon16419Korea
| | - Jeong‐Hoon Kim
- Department of Electrical and Computer EngineeringSungkyunkwan UniversitySuwon16419Korea
| | - Seul‐Gi Kim
- School of Chemical EngineeringEnergy Frontier LaboratorySungkyunkwan UniversitySuwon16419Korea
| | - Jeong‐Hyeon Kim
- School of Chemical EngineeringEnergy Frontier LaboratorySungkyunkwan UniversitySuwon16419Korea
| | - Seunghwan Seo
- Department of Electrical and Computer EngineeringSungkyunkwan UniversitySuwon16419Korea
| | - Dong‐Am Park
- School of Chemical EngineeringEnergy Frontier LaboratorySungkyunkwan UniversitySuwon16419Korea
| | - Jin‐Wook Lee
- Sungkyunkwan Advanced Institute of Nanotechnology (SAINT)Sungkyunkwan UniversitySuwon16419Korea
| | - Aron Walsh
- Department of Materials Science and EngineeringYonsei UniversitySeoul03722Korea
- Department of MaterialsImperial College LondonLondonSW7 2AZUK
| | - Jin‐Hong Park
- Department of Electrical and Computer EngineeringSungkyunkwan UniversitySuwon16419Korea
- Sungkyunkwan Advanced Institute of Nanotechnology (SAINT)Sungkyunkwan UniversitySuwon16419Korea
| | - Nam‐Gyu Park
- School of Chemical EngineeringEnergy Frontier LaboratorySungkyunkwan UniversitySuwon16419Korea
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