Time series statistical analysis: A powerful tool to evaluate the variability of resistive switching memories
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AuthorRoldán Aranda, Juan Bautista; Alonso Morales, Francisco J.; Aguilera Del Pino, Ana María; Maldonado Correa, David; Lanza, M
Resistive switching memoryRRAMConductive filamentsVariabilityTime series modellingAutocovarianceStationary time series
Roldan, Juan & Alonso, F. & Aguilera, Ana & Maldonado, David & Lanza, Mario. (2019). Time series statistical analysis: A powerful tool to evaluate the variability of resistive switching memories. Journal of Applied Physics. 125. 174504. 10.1063/1.5079409
SponsorshipIMB-CNM (CSIC) in Barcelona; Spanish Ministry of Science, Innovation and Universities TEC2017-84321-C4-3-R, MTM2017-88708-P (also supported by the FEDER program); Spanish ICTS Network MICRONANOFABS
Time series statistical analyses (TSSA) have been employed to evaluate the variability of resistive switching memories and to model the set and reset voltages for modeling purposes. The conventional procedures behind time series theory have been used to obtain autocorrelation and partial autocorrelation functions and determine the simplest analytical models to forecast the set and reset voltages in a long series of resistive switching processes. To do so, and for the sake of generality in our study, a wide range of devices have been fabricated and measured. Different oxides and electrodes have been employed, including bilayer dielectrics in devices such as Ni/HfO2/Si-n⁺, Cu/HfO2/Si-n⁺, and Au/Ti/TiO2/SiOx/Si-n⁺. The TSSA models obtained allowed one to forecast the reset and set voltages in a series if previous values were known. The study of autocorrelation data between different cycles in the series allows estimating the inertia between cycles in long resistive switching series. Overall, TSSA seems to be a very promising method to evaluate the intrinsic variability of resistive switching memories.