TY - JOUR
T1 - Variability in Resistive Memories
AU - Roldán, Juan B.
AU - Miranda, Enrique
AU - Maldonado, David
AU - Mikhaylov, Alexey N.
AU - Agudov, Nikolay V.
AU - Dubkov, Alexander A.
AU - Koryazhkina, Maria N.
AU - González, Mireia B.
AU - Villena, Marco A.
AU - Poblador, Samuel
AU - Saludes-Tapia, Mercedes
AU - Picos, Rodrigo
AU - Jiménez-Molinos, Francisco
AU - Stavrinides, Stavros G.
AU - Salvador, Emili
AU - Alonso, Francisco J.
AU - Campabadal, Francesca
AU - Spagnolo, Bernardo
AU - Lanza, Mario
AU - Chua, Leon O.
N1 - Publisher Copyright:
© 2022 The Authors. Advanced Intelligent Systems published by Wiley-VCH GmbH.
PY - 2023/6
Y1 - 2023/6
N2 - 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.
AB - 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.
KW - 2D materials
KW - experimental characterization
KW - memristor
KW - modeling
KW - resistive memory
KW - resistive switching
KW - variability
UR - http://www.scopus.com/inward/record.url?scp=85164343347&partnerID=8YFLogxK
U2 - 10.1002/aisy.202200338
DO - 10.1002/aisy.202200338
M3 - Review article
AN - SCOPUS:85164343347
SN - 2640-4567
VL - 5
JO - Advanced Intelligent Systems
JF - Advanced Intelligent Systems
IS - 6
M1 - 2200338
ER -