Polyoxometalate Accelerated Cationic Migration for Reservoir Computing
Guohua Zhang, Ziyu Xiong, Yue Gong, Zexi Zhu, Ziyu Lv, Yan Wang, Jia‐Qin Yang, Xuechao Xing, Zhanpeng Wang, Jingrun Qin, Ye Zhou, Su‐Ting Han
Abstract
Abstract Memristor‐based reservoir computing systems represent an attractive approach in processing the time‐series information with a low training cost, in a range of fields from finance to engineering. Previous investigations have identified the charming potential of organic devices for next‐generation memory devices. However, the structural inhomogeneity and wide energy bandgap of most organic polymers usually lead to low‐yield and high operation power microelectronic devices, that permit their further application in neuromorphic computing. Herein, an organic‐inorganic hybrid memristor that can be conveniently processed into crossbar devices with tolerable yield via spin‐coating is shown. The doped inorganic polyoxometalate (POM) clusters via supramolecular assembly strategy not only act as the charge trapping modules but also assist the formation of conductive filaments due to their delocalized electrostatic adsorption property. With the dynamic short‐term memory property, the designed memristor devices can be used as a reservoir framework to process temporal information directly. A smaller reservoir with 100 memristors can be used for the recognition of emotion patterns efficiently. This strategy demonstrates the unique role of POM in developing low‐power and repeated memristors, which provides a new material platform to design advanced function memristors for neuromorphic computing.