In‐Sensor Computing: Materials, Devices, and Integration Technologies

The number of sensor nodes in the Internet of Things is growing rapidly, leading to a large volume of data generated at sensory terminals. Frequent data transfer between the sensors and computing units causes severe limitations on the system performance in terms of energy efficiency, speed, and secu...

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Published in:	Advanced materials (Weinheim) Vol. 35; no. 37; p. e2203830
Main Authors:	Wan, Tianqing, Shao, Bangjie, Ma, Sijie, Zhou, Yue, Li, Qiao, Chai, Yang
Format:	Journal Article
Language:	English
Published:	Weinheim Wiley Subscription Services, Inc 01.09.2023
Subjects:	Data processing Data transfer (computers) Internet of Things Materials science Sensors
ISSN:	0935-9648, 1521-4095, 1521-4095
Online Access:	Get full text
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Abstract	The number of sensor nodes in the Internet of Things is growing rapidly, leading to a large volume of data generated at sensory terminals. Frequent data transfer between the sensors and computing units causes severe limitations on the system performance in terms of energy efficiency, speed, and security. To efficiently process a substantial amount of sensory data, a novel computation paradigm that can integrate computing functions into sensor networks should be developed. The in‐sensor computing paradigm reduces data transfer and also decreases the high computing complexity by processing data locally. Here, the hardware implementation of the in‐sensor computing paradigm at the device and array levels is discussed. The physical mechanisms that lead to unique sensory response characteristics and their corresponding computing functions are illustrated. In particular, bioinspired device characteristics enable the implementation of the functionalities of neuromorphic computation. The integration technology is also discussed and the perspective on the future development of in‐sensor computing is provided.
AbstractList	The number of sensor nodes in the Internet of Things is growing rapidly, leading to a large volume of data generated at sensory terminals. Frequent data transfer between the sensors and computing units causes severe limitations on the system performance in terms of energy efficiency, speed, and security. To efficiently process a substantial amount of sensory data, a novel computation paradigm that can integrate computing functions into sensor networks should be developed. The in‐sensor computing paradigm reduces data transfer and also decreases the high computing complexity by processing data locally. Here, the hardware implementation of the in‐sensor computing paradigm at the device and array levels is discussed. The physical mechanisms that lead to unique sensory response characteristics and their corresponding computing functions are illustrated. In particular, bioinspired device characteristics enable the implementation of the functionalities of neuromorphic computation. The integration technology is also discussed and the perspective on the future development of in‐sensor computing is provided. The number of sensor nodes in the Internet of Things is growing rapidly, leading to a large volume of data generated at sensory terminals. Frequent data transfer between the sensors and computing units causes severe limitations on the system performance in terms of energy efficiency, speed, and security. To efficiently process a substantial amount of sensory data, a novel computation paradigm that can integrate computing functions into sensor networks should be developed. The in-sensor computing paradigm reduces data transfer and also decreases the high computing complexity by processing data locally. Here, the hardware implementation of the in-sensor computing paradigm at the device and array levels is discussed. The physical mechanisms that lead to unique sensory response characteristics and their corresponding computing functions are illustrated. In particular, bioinspired device characteristics enable the implementation of the functionalities of neuromorphic computation. The integration technology is also discussed and the perspective on the future development of in-sensor computing is provided.The number of sensor nodes in the Internet of Things is growing rapidly, leading to a large volume of data generated at sensory terminals. Frequent data transfer between the sensors and computing units causes severe limitations on the system performance in terms of energy efficiency, speed, and security. To efficiently process a substantial amount of sensory data, a novel computation paradigm that can integrate computing functions into sensor networks should be developed. The in-sensor computing paradigm reduces data transfer and also decreases the high computing complexity by processing data locally. Here, the hardware implementation of the in-sensor computing paradigm at the device and array levels is discussed. The physical mechanisms that lead to unique sensory response characteristics and their corresponding computing functions are illustrated. In particular, bioinspired device characteristics enable the implementation of the functionalities of neuromorphic computation. The integration technology is also discussed and the perspective on the future development of in-sensor computing is provided.
Author	Wan, Tianqing Li, Qiao Shao, Bangjie Chai, Yang Zhou, Yue Ma, Sijie
Author_xml	– sequence: 1 givenname: Tianqing surname: Wan fullname: Wan, Tianqing organization: Department of Applied Physics The Hong Kong Polytechnic University Hong Kong China – sequence: 2 givenname: Bangjie surname: Shao fullname: Shao, Bangjie organization: Department of Applied Physics The Hong Kong Polytechnic University Hong Kong China – sequence: 3 givenname: Sijie surname: Ma fullname: Ma, Sijie organization: Department of Applied Physics The Hong Kong Polytechnic University Hong Kong China – sequence: 4 givenname: Yue surname: Zhou fullname: Zhou, Yue organization: Department of Applied Physics The Hong Kong Polytechnic University Hong Kong China – sequence: 5 givenname: Qiao surname: Li fullname: Li, Qiao organization: Department of Applied Physics The Hong Kong Polytechnic University Hong Kong China – sequence: 6 givenname: Yang orcidid: 0000-0002-8943-0861 surname: Chai fullname: Chai, Yang organization: Department of Applied Physics The Hong Kong Polytechnic University Hong Kong China, Shenzhen Research Institute The Hong Kong Polytechnic University Shenzhen China
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Snippet	The number of sensor nodes in the Internet of Things is growing rapidly, leading to a large volume of data generated at sensory terminals. Frequent data...
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