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

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.

References in corpus (11)

• Canonical sampling through velocity-rescaling
• Separable Dual Space Gaussian Pseudo-potentials
• CP2K: An Electronic Structure and Molecular Dynamics Software Package -- Quickstep: Efficient and Accurate Electronic Structure Calculations
• The PseudoDojo: Training and grading a 85 element optimized norm-conserving pseudopotential table
• QuantumATK: An integrated platform of electronic and atomic-scale modelling tools
• Moment Tensor Potentials: a class of systematically improvable interatomic potentials
• Distribution of phonon lifetime in Brillouin zone
• A Performance and Cost Assessment of Machine Learning Interatomic Potentials
• Active learning of linearly parametrized interatomic potentials
• ATK-ForceField: A New Generation Molecular Dynamics Software Package
• GHz Operation of Nanometer-Scale Metallic Memristors: Highly Transparent Conductance Channels in Ag${}_2$S Devices

Cited by in corpus (2)

• Non-zero crossing current-voltage characteristics of interface-type resistive switching devices
• VacHopPy: A Python package for vacancy hopping analysis based on molecular dynamics simulations