Ion-Gated Transistor: An Enabler for Sensing and Computing Integration
Xianbao Bu, Han Xu, Dashan Shang, Yue Li, Hangbing Lv, and Qi Liu
Advanced Intelligent Systems, p.2000156.
DOI: 10.1002/aisy.202000156
Abstract: With the rapid development of the Internet of Things, the amount of data we involved in our daily life is growing exponentially, which poses significant challenges for data processing and transmission to the conventional terminal sensors that passively acquire external data. Inspired by biological sensory nervous systems, building artificial intelligent sensory systems with both sensing and computing capability is regarded as a promising way to address these challenges, by which the acquired data can be preprocessed locally and timely before transmitting them to the remote server for further processing. Ion-gated transistors (IGTs), which have been widely used in sensors and have been recently investigated for neuromorphic computing, exhibit great potential in this domain. Herein, the essential operation principles, device structures, and electrical characteristics of IGT are introduced, and the recent developments in biosensors, neuromorphic computing, and intelligent sensors with near-sensor computing and in-sensor computing modes are summarized. To conclude, the current challenges and future development of IGT for intelligent sensory systems are presented.
Fig: (a) Optical micrograph displaying the top view of an individual IGT (top right) and IGT array conforming to the surface of a human hand (bottom left). (b) Sample traces of in vivo signals acquired by IGTs, reflecting the wide span of frequency and amplitude characteristics.
Acknowledgements: X.B. and H.X. contributed equally to this work. This work was supported by the National Key R&D Program of China under grant no. 2018YFA0701500; the National Natural Science Foundation of China under grant nos. 61874138, 61821091, 61825404, 61732020, and 61851402; the Strategic Priority Research Program of the Chinese Academy of Sciences under grant no. XDB44000000; Major Scientific Research Project of Zhejiang Lab (grant no. 2019KC0AD02); and Beijing Academy of Artificial Intelligence (BAAI).
