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Progress in self-powered sensors—Moving toward artificial intelligent and neuromorphic system

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Abstract

Wearable and flexible electronics are shaping our life with their unique advantages of light weight, good compliance, and desirable comfortability. With marching into the era of Internet of Things (IoT), numerous sensor nodes are distributed throughout networks to capture, process, and transmit diverse sensory information, which gives rise to the demand on self-powered sensors to reduce the power consumption. Meanwhile, the rapid development of artificial intelligence (AI) and fifth-generation (5G) technologies provides an opportunity to enable smart-decision making and instantaneous data transmission in IoT systems. Due to continuously increased sensor and dataset number, conventional computing based on von Neumann architecture cannot meet the needs of brain-like high-efficient sensing and computing applications anymore. Neuromorphic electronics, drawing inspiration from the human brain, provide an alternative approach for efficient and low-power-consumption information processing. Hence, this review presents the general technology roadmap of self-powered sensors with detail discussion on their diversified applications in healthcare, human machine interactions, smart homes, etc. Via leveraging AI and virtual reality/augmented reality (VR/AR) techniques, the development of single sensors to intelligent integrated systems is reviewed in terms of step-by-step system integration and algorithm improvement. In order to realize efficient sensing and computing, brain-inspired neuromorphic electronics are next briefly discussed. Last, it concludes and highlights both challenges and opportunities from the aspects of materials, minimization, integration, multimodal information fusion, and artificial sensory system.

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Acknowledgement

This work was supported by the Reimagine Research Scheme (RRSC) grant (“Scalable AI Phenome Platform towards Fast-Forward Plant Breeding (Sensor)”, Nos. A-0009037-02-00 and A-0009037-03-00) at NUS, Singapore; the Reimagine Research Scheme (RRSC) grant (“Under-utilised Potential of Micro-biomes (soil) in Sustainable Urban Agriculture”, No. A-0009454-01-00) at NUS, Singapore; and the RIE advanced manufacturing and engineering (AME) programmatic grant (“Nanosystems at the Edge”, No. A18A4b0055) at NUS, Singapore.

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  1. Feng Wen and Chan Wang contributed equally to this work.

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore

    Feng Wen, Chan Wang & Chengkuo Lee

  2. Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, 117608, Singapore

    Feng Wen, Chan Wang & Chengkuo Lee

  3. National University of Singapore Suzhou Research Institute (NUSRI), Suzhou, 215123, China

    Feng Wen, Chan Wang & Chengkuo Lee

  4. NUS Graduate School—Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, 119077, Singapore

    Chengkuo Lee

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Wen, F., Wang, C. & Lee, C. Progress in self-powered sensors—Moving toward artificial intelligent and neuromorphic system. Nano Res. 16, 11801–11821 (2023). https://doi.org/10.1007/s12274-023-5879-4

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