Modeling the Variability of Au/Ti/h-BN/Au Memristive Devices

Juan B. Roldan, David Maldonadoep, C. Aguilera-Pedregosa, Francisco J. Alonso, Yiping Xiao, Yaqing Shen, Wenwen Zheng, Mario Lanza

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


The variability of memristive devices using multilayer hexagonal boron nitride (h-BN) coupled with Ti and Au electrodes (i.e., Au/Ti/h-BN/Au) is analyzed in depth using different numerical techniques. We extract the reset voltage using three different methods, quantify its cycle-to-cycle variability, calculate the charge and flux that allows to minimize the effects of electric noise and the inherent stochasticity of resistive switching, describe the device variability using time series analyses to assess the “memory” effect, and employ a circuit breaker simulator to understand the formation and rupture of the percolation paths that produce the switching. We conclude that the cycle-to-cycle variability of the Au/Ti/h-BN/Au devices presented here is higher than that previously observed in Au/h-BN/Au devices, and hence, they may be useful for data encryption.
Original languageEnglish (US)
Pages (from-to)1-0
JournalIEEE Transactions on Electron Devices
StatePublished - Aug 30 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-09-14
Acknowledgements: This work was supported in part by the Ministry of Science and Technology of China under Grant 2019YFE0124200 and Grant 2018YFE0100800; in part by the National Natural Science Foundation of China under Grant 61874075; in part by the Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía (Spain), and European Regional Development Fund (ERDF) under Project A-TIC-117-UGR18, Project A-FQM-66-UGR20, Project A-FQM345-UGR18, Project B-TIC-624-UGR20, and Project IE2017-5414; in part by the Ministerio de Ciencia Innovación y Universidades/Agencia Estatal de Investigación/Fondo Europeo de Desarrollo Regional (MCIU/AEI/FEDER) under Grant PGC2018-098860-B-I00; in part by the “Maria de Maeztu” Excellence Unit IMAG, under Reference CEX2020- 001105-M; and in part by MCIN/AEI/10.13039/501100011033/. The work of Mario Lanza was supported by the King Abdullah University of Science and Technology.


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