Multiscale Modeling of Metal/Oxide/Metal Conductive Bridging Random Access Memory Cells: from Ab Initio to Finite Element Calculations

TL;DR

This paper introduces a multiscale simulation framework combining ab initio, machine learning, and finite element methods to accurately model and optimize the resistance switching behavior of metal/oxide/metal RRAM cells, including effects of Joule heating.

Contribution

The work develops a novel multiscale modeling approach that integrates quantum calculations and finite element analysis to predict RRAM device performance with fewer fitting parameters.

Findings

Successfully reproduces experimental I-V data for Ag/a-SiO2/Pt RRAM

Identifies Joule heating effects in thin filament devices

Enables optimization of memory cell design before fabrication

Abstract

We present a multiscale simulation framework to compute the current vs. voltage (I-V ) characteristics of metal/oxide/metal structures building the core of conductive bridging random access memory (CBRAM) cells and to shed light on their resistance switching properties. The approach relies on a finite element model whose input material parameters are extracted either from ab initio or from machine-learned empirical calculations. The applied techniques range from molecular dynamics and nudged elastic band to electronic and thermal quantum transport. Such an approach drastically reduces the number of fitting parameters needed and makes the resulting modeling environment more accurate than traditional ones. The developed computational framework is then applied to the investigation of an Ag/a-SiO2/Pt CBRAM, reproducing experimental data very well. Moreover, the relevance of Joule heating is assessed by considering various cell geometries. It is found that self-heating manifests itself in devices with thin conductive filaments with few-nanometer diameters and at current concentrations in the 10s-microampere range. With the proposed methodology it is now possible to explore the potential of not-yet fabricated memory cells and to reliably optimize their design.