A simulation study has been performed to analyze resistive switching (RS) phenomena in valence change memories (VCM) based on a HfO ₂ dielectric. The kernel of the simulation tool consists of a 3D kinetic Monte Carlo (kMC) algorithm implemented self-consistently with the 3D Poisson and heat equations. These VCM devices show filamentary conduction, their RS operation is based on the destruction and regeneration of an ohmic conductive filament (CF) composed of oxygen vacancies. The physics underlying the RS operation is described by means of processes linked to generation of oxygen vacancies, oxygen ion migration and recombination between vacancies and oxygen ions that can be accurately described by using the electric field and temperature distributions in the dielectric. The studied devices consist of TiN/Ti/HfO ₂ /W stacks where the Ti capping layer plays the role of oxygen ion getter material. The simulation tool is useful for obtaining information of internal physical variables, explaining RS dynamics and the CFs evolution from the microscopic viewpoint in terms of their size and shape under different electrical input signals; particularly, the pulsed operation regime has been studied in depth. Furthermore, interesting phenomena, such as partial SETs within overall RESET processes can be accurately reproduced.

Resistive Random Access Memories (RRAMs) are based on resistive switching (RS) operation and exhibit a set of technological features that make them ideal candidates for applications related to non-volatile memories, neuromorphic computing and hardware cryptography. For the full industrial development of these devices different simulation tools and compact models are needed in order to allow computer-aided design, both at the device and circuit levels. Most of the different RRAM models presented so far in the literature deal with temperature effects since the physical mechanisms behind RS are thermally activated; therefore, an exhaustive description of these effects is essential. As far as we know, no revision papers on thermal models have been published yet; and that is why we deal with this issue here. Using the heat equation as the starting point, we describe the details of its numerical solution for a conventional RRAM structure and, later on, present models of different complexity to integrate thermal effects in complete compact models that account for the kinetics of the chemical reactions behind resistive switching and the current calculation. In particular, we have accounted for different conductive filament geometries, operation regimes, filament lateral heat losses, the use of several temperatures to characterize each conductive filament, among other issues. A 3D numerical solution of the heat equation within a complete RRAM simulator was also taken into account. A general memristor model is also formulated accounting for temperature as one of the state variables to describe electron device operation. In addition, to widen the view from different perspectives, we deal with a thermal model contextualized within the quantum point contact formalism. In this manner, the temperature can be accounted for the description of quantum effects in the RRAM charge transport mechanisms. Finally, the thermometry of conducting filaments and the corresponding models considering different dielectric materials are tackled in depth.

In this work, three different RRAM compact models implemented in Verilog-A are analyzed and evaluated in order to reproduce the multilevel approach based on the switching capability of experimental devices. These models are integrated in 1T-1R cells to control their analog behavior by means of the compliance current imposed by the NMOS select transistor. Four different resistance levels are simulated and assessed with experimental verification to account for their multilevel capability. Further, an Artificial Neural Network study is carried out to evaluate in a real scenario the viability of the multilevel approach under study.

A physically based circuit model is proposed for SPICE simulation of thermally assisted reset transitions in resistive switching devices. The model allows the simulation of conductive filaments with complex structures, such as a main filament with several subfilaments attached forming a tree structure, or several filaments interlaced between them. The model has been validated by comparing with experimental data and the simulations obtained with a previously published simulation tool. A study of the influence of different subfilaments configurations on the variability of resistive random access memory I-V reset curves is also presented.

The relevance of the intrinsic series resistance effect in the context of resistive random access memory (RRAM) compact modeling is investigated. This resistance notably affects the conduction characteristic of resistive switching memories so that it becomes an essential factor to consider when fitting experimental data, especially those coming from devices exhibiting the so-called snapback and snapforward effects. A thorough description of the resistance value extraction procedure and an analysis of the connection of this value with the set and reset transition voltages in HfO₂-based valence change memories are presented. Furthermore, in order to illustrate the importance of this feature in the shape of the I–V curve, the Stanford model for RRAM devices is enhanced by incorporating the series resistance as an additional parameter in the Verilog-A model script.

Kinetic Monte Carlo resistive random access memory simulations are used to understand different retention experiments performed at several temperatures. The physics behind resistive switching allows to explain experimental results, in particular the degradation of the conductive filaments with temperature. It is observed that competing mechanisms control resistive switching in this type of experiments and the thermal dependencies involved are key to explain the measurements. Besides, the simulation approach allows to analyze the existence of percolation paths in the device dielectric and the conductive filament density and compactness. Finally, the key physical mechanisms are detected and some clues related to the retention performance and possible technology improvements are unveiled.