Variability in Resistive Memories

• Published in: Advanced intelligent systems Vol. 5; no. 6
• Main Authors: Roldán, Juan B., Miranda, Enrique, Maldonado, David, Mikhaylov, Alexey N., Agudov, Nikolay V., Dubkov, Alexander A., Koryazhkina, Maria N., González, Mireia B., Villena, Marco A., Poblador, Samuel, Saludes-Tapia, Mercedes, Picos, Rodrigo, Jiménez-Molinos, Francisco, Stavrinides, Stavros G., Salvador, Emili, Alonso, Francisco J., Campabadal, Francesca, Spagnolo, Bernardo, Lanza, Mario, Chua, Leon O.
• Format: Journal Article
• Language: English
• Published: Weinheim John Wiley & Sons, Inc 01.06.2023; Wiley
• Subjects: 2D materials; Circuit design; Cryptography; Data storage; experimental characterization; Ion transport; memristor; Metal oxides; modeling; Modelling; Neural networks; R&D; Reproducibility; Research & development; resistive memory; resistive switching; Roads & highways; Switching; variability
• ISSN: 2640-4567; 2640-4567
• DOI: 10.1002/aisy.202200338

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. A comprehensive review of variability in resistive memories is presented. Experimental evidence of variability for resistive memories is described. Later on, different approaches to model this variability from the physical, behavioral, and stochastic viewpoints are presented. The models help to understand the physics behind resistive switching and provide the infrastructure for simulation tools needed in circuit design.