Abstract-Memristors are novel devices, which can be used in applications such as memory, logic, and neuromorphic systems. A memristor offers several advantages to existing applications: nonvolatility, good scalability, effectively no leakage current, and compatibility with CMOS technology, both electrically and in terms of manufacturing. Several models for memristors have been developed and are discussed in this paper. Digital applications such as memory and logic require a model that is highly nonlinear, simple for calculations, and sufficiently accurate. In this paper, a new memristor model is presented -TEAM, ThrEshold Adaptive Memristor model. Previously published models are compared in this paper to the proposed TEAM model. It is shown that the proposed model is reasonably accurate and computationally efficient, and is more appropriate for circuit simulation than previously published models.
Memristors are novel electrical devices used for a variety of applications including memory, logic circuits, and neuromorphic systems. Memristive technologies are attractive due to the nonvolatility, scalability, and compatibility with CMOS. Numerous physical experiments have shown the existence of a threshold voltage in some physical memristors. Additionally, as shown in this paper, some applications require voltage controlled memristors to operate properly. In this paper, the Voltage ThrEshold Adaptive Memristor (VTEAM) model is proposed to describe the behavior of voltage controlled memristors. The VTEAM model extends the previously proposed TEAM model, which describes current-controlled memristors. The VTEAM model has similar advantages to the TEAM model: it is simple, general, and flexible and can characterize different voltage controlled memristors. The VTEAM model is accurate (below 1.5% in terms of relative root mean squared error) and computationally efficient as compared to existing memristor models and experimental results describing different memristive technologies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.