The development of memristors operating at low switching voltages <50 mV could be very useful to avoid signal amplification in many types of circuits, such as those used in bioelectronic applications to interact with neurons and nerves. However, so far, in most of the reported studies, the switching voltages (0.2~2 V) are much larger than the spike bias in biological neurons (<50 mV). Therefore, it is very significant to explore candidate structures for memristors that exhibit ultra-low switching voltages and low power consumption.

Professor Zhijun Zhang’s group of Henan University and Professor Mario Lanza’s group of King Abdullah University of Science and Technology (KAUST) jointly built a surface modified copper nanoparticles based nano-memristors, which achieve the lowest switching voltage of 4 mV ever reported. This study was published on 23th March, 2022 in Advanced Materials entitled with " Nano-memristors with 4 mV switching voltage based on surface-modified copper nanoparticles" (doi: 10.1002/adma.202201197) as research article.

In this work, we have designed nanostructures consisting of dalkyl-dithiophosphoric (DDP) modified copper nanoparticles (CuNPs), and reported that 400-nm-thick films made of DDP-CuNPs exhibit volatile threshold-type resistive switching (RS) at ultra-low switching voltage of ~4 mV. The RS has been observed in small nanocells with a lateral size of <50 nm², during hundreds of cycles, and with an ultra-low variability. Atomistic calculations reveal that the switching mechanism is related to the modification of the Schottky barriers and insulator-to-metal transition when ionic movement is induced via external bias. We also use the devices to model integrate-and-fire neurons for spiking neural networks, and conclude that circuits employing DDP-CuNPs consume around 10 times less power than similar neurons implemented with a memristor that switches at 40 mV.

This work was supported by the Ministry of Science and Technology of China (grants no. 2019YFE0124200 and 2018YFE0100800), and the National Natural Science Foundation of China (grants no. 61874075), the Science and Technology Planning Project of Henan Province (212102210466), the first class subject cultivation project of Henan University (2019YLZDJL12), as well as the generous Baseline funding program of the King Abdullah University of Science and Technology.

Paper link: https://onlinelibrary.wiley.com/doi/10.1002/adma.202201197