@misc{10481/81638,
year = {2023},
month = {4},
url = {https://hdl.handle.net/10481/81638},
abstract = {We present a new methodology to quantify the
variability of resistive switching memories. Instead of statistically
analyzing few data points extracted from current versus voltage (I−
V) plots, such as switching voltages or state resistances, we take
into account the whole I−V curve measured in each RS cycle. This
means going from a one-dimensional data set to a two-dimensional
data set, in which every point of each I−V curve measured is
included in the variability calculation. We introduce a new
coefficient (named two-dimensional variability coefficient,
2DVC) that reveals additional variability information to which traditional one-dimensional analytical methods (such as the
coefficient of variation) are blind. This novel approach provides a holistic variability metric for a better understanding of the
functioning of resistive switching memories},
organization = {Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía (Spain)},
organization = {FEDER:  B-TIC-624-UGR20, PID2020-113961GB-I00, A-FQM-66-UGR20, FQM-307},
organization = {IMAG María de Maeztu CEX2020-001105-M/AEI/10.13039/501100011033},
organization = {King Abdullah University of Science and Technology},
publisher = {American Chemical Society},
keywords = {Resistive memories},
keywords = {Variability},
keywords = {Variability coefficient},
keywords = {Functional data analysis},
keywords = {Holistic methodology},
title = {Holistic Variability Analysis in Resistive Switching Memories Using a Two-Dimensional Variability Coefficient},
doi = {10.1021/acsami.2c22617},
author = {Acal González, Christian José and Maldonado Correa, David and Aguilera Del Pino, Ana María and Roldán Aranda, Juan Bautista},
}