Self-powered high-sensitivity sensory memory actuated by triboelectric sensory receptor for real-time neuromorphic computing

Liu, Yaqian; Yang, Wenyu; Yan, Yujie; Wu, Xiaomin; Wang, Xiumei; Zhou, Yilun; Hu, Yuanyuan; Chen, Huipeng; Guo, Tailiang

doi:10.1016/j.nanoen.2020.104930

Cited by 69 publications

(38 citation statements)

References 58 publications

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“…As a good substitute for mechanical switches, flexible touchpads with multiple arrays/units have been investigated frequently by using TENG technology to achieve selfpowered HMIs to interact with household appliances [244][245][246][247]. For these multi-array touchpads, there are two common ways for wire connection.…”

Section: Hmis For Smart Home Applicationsmentioning

confidence: 99%

Progress in the Triboelectric Human–Machine Interfaces (HMIs)-Moving from Smart Gloves to AI/Haptic Enabled HMI in the 5G/IoT Era

Sun

Zhu

Lee

2021

Nanoenergy Advances

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Entering the 5G and internet of things (IoT) era, human–machine interfaces (HMIs) capable of providing humans with more intuitive interaction with the digitalized world have experienced a flourishing development in the past few years. Although the advanced sensing techniques based on complementary metal-oxide-semiconductor (CMOS) or microelectromechanical system (MEMS) solutions, e.g., camera, microphone, inertial measurement unit (IMU), etc., and flexible solutions, e.g., stretchable conductor, optical fiber, etc., have been widely utilized as sensing components for wearable/non-wearable HMIs development, the relatively high-power consumption of these sensors remains a concern, especially for wearable/portable scenarios. Recent progress on triboelectric nanogenerator (TENG) self-powered sensors provides a new possibility for realizing low-power/self-sustainable HMIs by directly converting biomechanical energies into valuable sensory information. Leveraging the advantages of wide material choices and diversified structural design, TENGs have been successfully developed into various forms of HMIs, including glove, glasses, touchpad, exoskeleton, electronic skin, etc., for sundry applications, e.g., collaborative operation, personal healthcare, robot perception, smart home, etc. With the evolving artificial intelligence (AI) and haptic feedback technologies, more advanced HMIs could be realized towards intelligent and immersive human–machine interactions. Hence, in this review, we systematically introduce the current TENG HMIs in the aspects of different application scenarios, i.e., wearable, robot-related and smart home, and prospective future development enabled by the AI/haptic-feedback technology. Discussion on implementing self-sustainable/zero-power/passive HMIs in this 5G/IoT era and our perspectives are also provided.

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Section: Hmis For Smart Home Applicationsmentioning

confidence: 99%

Progress in the Triboelectric Human–Machine Interfaces (HMIs)-Moving from Smart Gloves to AI/Haptic Enabled HMI in the 5G/IoT Era

Sun

Zhu

Lee

2021

Nanoenergy Advances

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“…The results indicated that the NKN memristor with the NKN NG has exhibited an excellent synaptic plasticity behavior, which, in an artificial synapse, implements advanced and bio-realistic functions. Liu et al (2020b) demonstrated a self-powered artificial sensory memory system by integrating a triboelectric nanogenerator (TENG) and a field effect synaptic transistor (FEST). The voltage spike signal was obtained when an external touch was applied to the triboelectric nanogenerator, which was subsequently transmitted to the gate electrode of the transistor.…”

Section: Self-powered Memristive Systems For Neuromorphic Computingmentioning

confidence: 99%

“… Liu et al (2020b) demonstrated a self-powered artificial sensory memory system by integrating a triboelectric nanogenerator (TENG) and a field effect synaptic transistor (FEST). The voltage spike signal was obtained when an external touch was applied to the triboelectric nanogenerator, which was subsequently transmitted to the gate electrode of the transistor.…”

Section: Self-powered Memristive Systems For Neuromorphic Computingmentioning

confidence: 99%

Self-Powered Memristive Systems for Storage and Neuromorphic Computing

et al. 2021

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A neuromorphic computing chip that can imitate the human brain’s ability to process multiple types of data simultaneously could fundamentally innovate and improve the von-neumann computer architecture, which has been criticized. Memristive devices are among the best hardware units for building neuromorphic intelligence systems due to the fact that they operate at an inherent low voltage, use multi-bit storage, and are cost-effective to manufacture. However, as a passive device, the memristor cell needs external energy to operate, resulting in high power consumption and complicated circuit structure. Recently, an emerging self-powered memristive system, which mainly consists of a memristor and an electric nanogenerator, had the potential to perfectly solve the above problems. It has attracted great interest due to the advantages of its power-free operations. In this review, we give a systematic description of self-powered memristive systems from storage to neuromorphic computing. The review also proves a perspective on the application of artificial intelligence with the self-powered memristive system.

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“…Hence, it's urgent to fabricate synapse-like devices, which is able to imitate the functions of biological synapse to overcome the limitation of von Neumann-based computing systems [13]. In recent years, two-terminal memristors and three-terminal transistors have been investigated to emulate the artificial synapse functions [2], [14], [15]. Compared with two-terminal memristors, three-terminal transistors can be a more promising candidate for artificial synapse, which have lower energy consumption and higher scalability and the synaptic weight can be modulated by additional gate terminals [16].…”

Section: Introductionmentioning

confidence: 99%

Noise Detection System Based on Noise Triboelectric Nanogenerator and Synaptic Transistors

Lian

Liu

Zhang

et al. 2021

IEEE Electron Device Lett.

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Self-powered high-sensitivity sensory memory actuated by triboelectric sensory receptor for real-time neuromorphic computing

Cited by 69 publications

References 58 publications

Progress in the Triboelectric Human–Machine Interfaces (HMIs)-Moving from Smart Gloves to AI/Haptic Enabled HMI in the 5G/IoT Era

Progress in the Triboelectric Human–Machine Interfaces (HMIs)-Moving from Smart Gloves to AI/Haptic Enabled HMI in the 5G/IoT Era

Self-Powered Memristive Systems for Storage and Neuromorphic Computing

Noise Detection System Based on Noise Triboelectric Nanogenerator and Synaptic Transistors

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