Memristor variability and stochastic physical properties modeling from a multivariate time series approach

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.IMB-CNM (CSIC) in BarcelonaSpanish Ministry of Science, Innovation and Universities TEC2017-84321-C4-3-R, MTM2017-88708-P, IJCI-2017-34038 (also supported by the FEDER program)PGC2018-098860-B-I00 supported by MCIU/AEI/FEDERConsejería de Conocimiento, Investigación y Universidad, Junta de Andalucía and European Regional Development Fund (ERDF) under projects A-TIC-117-UGR18 and A-FQM-345-UGR18Spanish ICTS Network MICRONANOFAB