@misc{10481/71825,
year = {2021},
month = {2},
url = {http://hdl.handle.net/10481/71825},
abstract = {A powerful time series analysis modeling technique is presented to describe cycle-to-cycle variability in memristors. These devices show variability linked to the inherent stochasticity of device operation and it needs to be accurately modeled to build compact models for circuit simulation and design purposes. A new multivariate approach is proposed for the reset and set voltages that accurately describes the statistical data structure of a resistive switching series. Experimental data were measured from advanced hafnium oxide based devices. The models reproduce the experiments correctly and a comparison of the multivariate and univariate approaches is shown for comparison.},
organization = {IMB-CNM (CSIC) in Barcelona},
organization = {Spanish  Ministry  of  Science,  Innovation  and Universities TEC2017-84321-C4-3-R,  MTM2017-88708-P,  IJCI-2017-34038  (also  supported  by  the  FEDER  program)},
organization = {PGC2018-098860-B-I00 supported by MCIU/AEI/FEDER},
organization = {Consejería  de  Conocimiento,  Investigación  y Universidad, Junta de Andalucía and European Regional Development Fund (ERDF) under projects A-TIC-117-UGR18 and A-FQM-345-UGR18},
organization = {Spanish ICTS Network MICRONANOFABS},
publisher = {Elsevier},
keywords = {Memristors},
keywords = {Variability},
keywords = {Resistive switching memory},
keywords = {Conductive filaments},
keywords = {Time series modeling},
keywords = {Compact modeling},
keywords = {Autocovariance},
title = {Memristor variability and stochastic physical properties modeling from a multivariate time series approach},
doi = {https://doi.org/10.1016/j.chaos.2020.110461},
author = {Alonso Morales, Francisco J. and Maldonado Correa, David and Aguilera Del Pino, Ana María and Roldán Aranda, Juan Bautista},
}