Short-Term to Long-Term Plasticity Transition Behavior of Memristive Devices with Low Power Consumption via Facilitating Ionic Drift of Implanted Lithium

Recent innovations in information technology have encouraged extensive research into the development of future generation memory and computing technologies. Memristive devices based on resistance switching are not only attractive because of their multi-level information storage, but they also display fascinating neuromorphic behaviors. We investigated the basic human brain's learning and memory algorithm for memorizing as a feature for memristive devices based on Li-implanted structures with low power consumption. A topographical and surface chemical functionality analysis of an Li:ITO substrate was conducted to observe its characterization. In addition, a switching mechanism of a memristive device was theoretically studied and associated with ion migrations into a polymeric insulating layer. Biological short-term and long-term memory properties were imitated with the memristive device using low power consumption.

Automated summary

Researchers have explored memristive devices based on resistance switching to simulate the learning and memory algorithm of the human brain, finding their multi-level information storage and neuromorphic behaviors attractive. The study also conducted a detailed analysis of an Li:ITO substrate and theoretically studied the switching mechanism of memristive devices.