@misc{10481/93250,
year = {2024},
month = {5},
url = {https://hdl.handle.net/10481/93250},
abstract = {A new statistical analysis is presented to assess cycle-to-cycle variability in resistive memories. This method employs two-dimensional (2D) distributions of parameters to analyse both set and reset voltages and currents, coupled with a 2D coefficient of variation (CV). This 2D methodology significantly enhances the analysis, providing a more thorough and comprehensive understanding of the data compared to conventional one-dimensional methods. Resistive switching (RS) data from two different technologies based on hafnium oxide are used in the variability study. The 2D CV allows a more compact assessment of technology suitability for applications such as non-volatile memories, neuromorphic computing and random number generation circuits.},
organization = {Projects PID2022-139586NB-C44,
PID2022-139586NB-C42 and PID2020-113961GB-I00},
organization = {“María de Maeztu” Excellence Unit IMAG reference CEX2020-
001105-M},
organization = {CSIC funding through project 20225AT012},
organization = {Generalitat de Catalunya-AGAUR through project 2021 SGR
00497},
organization = {Project CR32023-
040125},
organization = {European Union through the NextGenerationEU/PRTR
program.},
organization = {acknowledges the grant RYC2020-030150-I funded by MCIN/AEI/ 10.13039/501100011033 and by “ESF
Investing in your future”},
publisher = {Royal Society of Chemistry},
title = {Variability in HfO2-based memristors described with a new bidimensional statistical technique},
doi = {10.1039/D4NR01237B},
author = {Acal González, Christian José and Maldonado Correa, David and Cantudo Gómez, Antonio and Bargallo González, Mireia and Jiménez Molinos, Francisco and Campabadal, F. and Roldán, J. B.},
}