Polyoxometalate Accelerated Cationic Migration for Reservoir Computing

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.

Automated summary

Memristor-based reservoir computing systems offer a promising approach for processing time-series information with low training cost. This study presents an organic-inorganic hybrid memristor with high yield and low power consumption. By using supramolecular assembly strategy and doped inorganic polyoxometalate clusters as charge trapping modules, the memristor demonstrates dynamic short-term memory and efficient recognition of emotion patterns.