Comprehensive numerical modeling of filamentary RRAM devices including voltage ramp-rate and cycle-to-cycle variations

TL;DR

This paper presents a comprehensive numerical model for filamentary RRAM devices that accurately simulates their I-V characteristics, including effects of voltage ramp-rate and cycle-to-cycle variations, by incorporating thermodynamic principles and atomic-scale interactions.

Contribution

The model uniquely accounts for ramp-rate dependence and cycle-to-cycle variability in RRAM, advancing understanding of filament formation and destruction mechanisms.

Findings

Reproduces I-V characteristics with varying voltage ramp-rates.

Captures cycle-to-cycle variations due to atomic potential distributions.

Integrates thermodynamic and atomic-scale effects in RRAM modeling.

Abstract

The equilibrium ON and OFF states of resistive random access memory (RRAM) are due to formation and destruction of a conducting filament. The laws of thermodynamics dictate that these states correspond to the minimum of free energy. Here, we develop a numerical model that, through the minimization of free energy at a given voltage, determines the filament parameters and thus the electric current. Overall, it simulates the current-voltage (I-V) characteristics of RRAM. The model describes mutual transformations of RRAM states through SET (ON to OFF) and RESET (OFF to ON) processes. From the modeling perspectives, these states and processes constitute four programming modules constructed here in COMSOL Multiphysics software tackling the electrodynamic and heat transfer equations and yielding RRAM energy and I-V. Our modeling uniquely reproduces the observed I-V varying with voltage ramp-rates. That is achieved by accounting for the ramp-rate dependent activation energy of conduction. The underlying mechanism is due to the deformation interaction caused by the double well atomic potentials universally present in amorphous materials and having exponentially broad distribution of relaxation times. As another unique feature, our modeling reproduces the observed cycle-to-cycle variations of RRAM parameters attributed to the lack of self-averaging in small ensembles of double well potentials and electronic states in geometrically small (nano-size) RRAM structures.