# An Enhanced Lumped Element Electrical Model of a Double Barrier Memristive Device

## TL;DR

This paper presents an improved lumped element electrical model for a double barrier memristive device, enabling fast and physically meaningful simulations suitable for neuromorphic circuit applications.

## Contribution

The paper introduces a novel enhanced electrical model for a VCM-based double barrier memristive device that balances simulation speed and physical accuracy.

## Key findings

- Model enables rapid circuit simulations
- Verification against kinetic Monte-Carlo simulations
- Maintains physically meaningful parameters

## Abstract

The massive parallel approach of neuromorphic circuits leads to effective methods for solving complex problems. It has turned out that resistive switching devices with a continuous resistance range are potential candidates for such applications. These devices are memristive systems - nonlinear resistors with memory. They are fabricated in nanotechnology and hence parameter spread during fabrication may aggravate reproducible analyses. This issue makes simulation models of memristive devices worthwhile. Kinetic Monte-Carlo simulations based on a distributed model of the device can be used to understand the underlying physical and chemical phenomena. However, such simulations are very time-consuming and neither convenient for investigations of whole circuits nor for real-time applications, e.g. emulation purposes. Instead, a concentrated model of the device can be used for both fast simulations and real-time applications, respectively. We introduce an enhanced electrical model of a valence change mechanism (VCM) based double barrier memristive device (DBMD) with a continuous resistance range. This device consists of an ultra-thin memristive layer sandwiched between a tunnel barrier and a Schottky-contact. The introduced model leads to very fast simulations by using usual circuit simulation tools while maintaining physically meaningful parameters. Kinetic Monte-Carlo simulations based on a distributed model and experimental data have been utilized as references to verify the concentrated model.