Variability in Resistive Memories

TL;DR

This review discusses the recent advancements in understanding and modeling the cycle-to-cycle variability of resistive memories, emphasizing the importance of accurate modeling for circuit design and industrial application.

Contribution

It provides a comprehensive overview of the latest modeling approaches addressing variability in resistive memories, highlighting their significance for device reliability and circuit integration.

Findings

Various modeling techniques capture different aspects of variability.

Recent models improve understanding of resistive switching mechanisms.

Advances support better circuit design and reliability.

Abstract

Resistive memories are outstanding electron devices that have displayed a large potential in a plethora of applications such as nonvolatile data storage, neuromorphic computing, hardware cryptography, etc. Their fabrication control and performance have been notably improved in the last few years to cope with the requirements of massive industrial production. However, the most important hurdle to progress in their development is the so-called cycle-to-cycle variability, which is inherently rooted in the resistive switching mechanism behind the operational principle of these devices. In order to achieve the whole picture, variability must be assessed from different viewpoints going from the experimental characterization to the adequation of modeling and simulation techniques. Herein, special emphasis is put on the modeling part because the accurate representation of the phenomenon is critical for circuit designers. In this respect, a number of approaches are used to the date: stochastic, behavioral, mesoscopic..., each of them covering particular aspects of the electron and ion transport mechanisms occurring within the switching material. These subjects are dealt with in this review, with the aim of presenting the most recent advancements in the treatment of variability in resistive memories.