VTEAM: A General Model for Voltage-Controlled Memristors

Abstract

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.

Modeling and simulation of large memristive networks

“…Figure 3 (a) compares the left-hand side of (18) as a function of v M for the original and the modified Bayat (MB) model. Note that, according to [10], there are two different sets of fitting parameters α, λ, χ, ζ, δ, and θ for v M ≥ 0 and for v M < 0 in (18). In order to avoid singularity for v M = 0, the original port equation was reformulated as follows:…”

SkyNet: Memristor-based 3D IC for artificial neural networks

“…Verilog-A VTEAM [20] memristor model compatible with HSPICE was chosen to model them. It is a general model for voltage controlled memristors and is used to fit the experimental results of titanium dioxide memristors demonstrated [21].…”

SPICE Compact Modeling of Bipolar/Unipolar Memristor Switching Governed by Electrical Thresholds

“…Several works propose models whose behavior depends on voltage or current thresholds that trigger different mechanisms unleashing a state change in the device [1], [11], [13].…”

“…Phenomenological approaches obtain a broad flexibility regarding the range of described devices [1]. More recent solutions update and improve earlier versions such as [11]- [13], standing as a powerful solution for general modeling, and an effective application to neuromorphic computing [14].…”