Memristors with analogue switching and high on/off ratios using a van der Waals metallic cathode

Abstract

Neuromorphic computing based on memristors could help meet the growing demand for data-intensive computing applications such as artificial intelligence. Analogue memristors with multiple conductance states are of particular use in high-efficiency neuromorphic computing, but their weight mapping capabilities are typically limited by small on/off ratios. Here we show that memristors with analogue resistive switching and large on/off ratios can be created using two-dimensional van der Waals metallic materials (graphene or platinum ditelluride) as the cathodes. The memristors use silver as the top anode and indium phosphorus sulfide as the switching medium. Previous approaches have focused on modulating ion motion using changes to the resistive switching layer or anode, which can lower the on/off ratios. In contrast, our approach relies on the van der Waals cathode, which allows silver ion intercalation/de-intercalation, creating a high diffusion barrier to modulate ion motion. The strategy can achieve analogue resistive switching with an on/off ratio up to 10⁸, over 8-bit conductance states and attojoule-level power consumption. We use the analogue properties to perform the chip-level simulation of a convolutional neural network that offers high recognition accuracy.