Physics-based modeling of a bi-layer Al₂O₃/Nb₂O₅ analog memristive device.

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This paper proposes the derivation of a physics-based model of an analog memristive device realized as a bi-layer Al2O3/Nb2O5 stack. Memristive crossbar arrays implementing matrix-vector multiplications are a central building block of novel computing-in-memory architectures for artificial neural network and neuromorphic computing applications. The presented memristor shows analog, multi-level switching at high resistances without electroforming and is suitable for crossbar operations with low energy consumption. By including a graphical analysis method of the I-V curves obtained in a quasi-static approach, the dynamic behavior is analyzed with regard to ohmic and Poole-Frenkel behavior. Finally, a compact model, represented by an algebraic differential equation, is proposed and verified by fitting calculated solutions to experimental data.


Original languageEnglish
Title of host publication2022 IEEE International Symposium on Circuits and Systems (ISCAS)
PublisherIEEE Xplore
Number of pages5
ISBN (electronic)9781665484855
ISBN (print)978-1-6654-8486-2
Publication statusPublished - May 2022

Publication series

SeriesIEEE International Symposium on Circuits and Systems (ISCAS)


TitleIEEE International Symposium on Circuits and Systems 2022
Abbreviated titleISCAS 2022
Duration28 May - 1 June 2022
Degree of recognitionInternational event
LocationAustin Hilton
CountryUnited States of America

External IDs

ORCID /0000-0003-3259-4571/work/107904026
Scopus 85136109450
dblp conf/iscas/SchroedterMHASM22
unpaywall 10.1109/iscas48785.2022.9937966
Mendeley 3885887a-1096-3d22-b1b1-7761097f88c8
ORCID /0000-0001-7436-0103/work/142240362
ORCID /0000-0003-3814-0378/work/142256248


Sustainable Development Goals

ASJC Scopus subject areas


  • analog memristive device, bi-layer Al2O3/Nb2O5 stack, crossbar array for neuromorphic computing, Physics-based compact model, Poole-Frenkel emission