Resistive switching (RS) is a promising emerging storage technology that has received much attention due to its many advantages, such as economy, fast operating speed, long retention, high density, and low energy consumption. [1] RS effects are widely applied in the fields of nonvolatile RS random access memory (RRAM), artificial neural computing, and reconfigurable logic operations and so on. [2] RRAM memories based on electrochemical metallization (ECM) and valance change mechanism (VCM) are commonly used for the memristor application. [3] Compared with conventional computing based on the von Neumann architecture, memristor (i.e., RS device) computing is proving to be superior for brain-inspired computing, such as image processing and speech recognition, [2] where the diffusion of the metal ions such as Ag + , Cu + , or oxygen vacancies are used to mimic the diffusion of Ca + in the neural cell. [4] However, the switching voltages in the memristor devices (MDs) showWith the advent of the era of big data, resistive random access memory (RRAM) has become one of the most promising nanoscale memristor devices (MDs) for storing huge amounts of information. However, the switching voltage of the RRAM MDs shows a very broad distribution due to the random formation of the conductive filaments. Here, selfassembled lead sulfide (PbS) quantum dots (QDs) are used to improve the uniformity of switching parameters of RRAM, which is very simple comparing with other methods. The resistive switching (RS) properties of the MD with the self-assembled PbS QDs exhibit better performance than those of MDs with pure-Ga 2 O 3 and randomly distributed PbS QDs, such as a reduced threshold voltage, uniformly distributed SET and RESET voltages, robust retention, fast response time, and low power consumption. This enhanced performance may be attributed to the ordered arrangement of the PbS QDs in the self-assembled PbS QDs which can efficiently guide the growth direction for the conducting filaments. Moreover, biosynaptic functions and plasticity, are implemented successfully in the MD with the self-assembled PbS QDs. This work offers a new method of improving memristor performance, which can significantly expand existing applications and facilitate the development of artificial neural systems.
Data Storage