/Au Memristor Devices

The past decade has seen the meteoric rise of machine learning and its integration into a wide variety of applications. However, Si-based computational systems are not ideally suited for machine learning applications, due to their power requirements and memory-related bottlenecks, which hinder the use of machine learning on portable, lower-power devices such as cell phones. Resistive switching memory (RSM) is one promising technology for overcoming these issues, as it is expected to enable low power, non-volatile memory cells which also show potential for in-memory computing. Transition metal dichalcogenides (TMDs) with sub-nanometer thickness have previously been used as the active layer in RSM devices. However, it is yet unknown to what degree such TMD-based RSM devices can be scaled down laterally. In this talk, we will discuss our efforts to probe the local switching characteristics of a model RSM device comprising a graphene/MoS₂/Au heterostructure. Our investigations utilize a scanning tunneling microscope probe tip to perform switching operations over sub-100nm regions of the device, as well as assess the morphological and electronic properties of the device’s surface. Using these data, we will discuss the atomic-scale resistive switching behavior of our model RSM device.