Artificial Synaptic Performance with Learning Behavior for Memristor Fabricated with Stacked Solution-Processed Switching Layers

This is the authors' abstract. We don't add key points for this paper.

As one of the promising next-generation electronics, brain-inspired synaptic resistive random access memory (RRAM) devices with stacked solution-processed (SP) spin-coated resistive switching (RS) layers were fabricated in this work. Compared with the RRAM device with a single SP-RS layer (Ag/SP-AlO x /ITO), the device with stacked SP-RS layers (Ag/SP-GaO x /SP-AlO x /ITO) is induced by the metal conductive filament performed with lower power consumption (∼±0.6 V operation voltage), larger read and write capability (∼2 × 10 4 ON/OFF ratio), and enhanced stability (>2 × 10 4 s retention time and >1000 endurance cycles). Multiple conductance states with long-term potentiation and depression (200 pulses) were obtained on Ag/SP-GaO x /SP-AlO x /ITO RRAM devices, which resulted in the human brain-like behavior (learning–forgetting–relearning) of a matrix comprising of RRAM devices with SP-GaO x /SP-AlO x layers. Based on the synaptic performance of Ag/SP-GaO x /SP-AlO x /ITO RRAM devices, an image recognition process based on a neuron network was conducted and the average recognition accuracy was close to 90%.