Amylum Resistive Random Access Memory Based on the Quantization Effect of Gold Nanoparticles

Resistive random access memory (RRAM) is integrated computing and storage, but most devices have problems, such as high power consumption and poor consistency, so it is critical to optimize the performance of the device. In this paper, amylum RRAM is prepared by using the quantization effect of gold nanoparticles (Au NPs), which not only improves the consistency of the threshold voltage distribution of the device but also reduces the power consumption of the device and significantly increases the ON/OFF current ratio of the device. This work not only realized multilevel data storage (4 levels, 2 bits) on the same unit, improving the storage density of starch RRAM, but also simulated the synaptic potentiation and depression behavior, providing a way of thinking for the next generation of artificial intelligence and brain-like neural computing.