Stochastic modeling of Random Access Memories reset transitions

M. Carmen Aguilera-Morillo, Ana M. Aguilera, Francisco Jiménez-Molinos and Juan B. Roldán

Mathematics and Computers in Simulation (MATCOM), 2019, vol. 159, issue C, 197-209

Abstract: Resistive Random Access Memories (RRAMs) are being studied by the industry and academia because it is widely accepted that they are promising candidates for the next generation of high density nonvolatile memories. Taking into account the stochastic nature of mechanisms behind resistive switching, a new technique based on the use of functional data analysis has been developed to accurately model resistive memory device characteristics. Functional principal component analysis (FPCA) based on Karhunen–Loève expansion is applied to obtain an orthogonal decomposition of the reset process in terms of uncorrelated scalar random variables. Then, the device current has been accurately described making use of just one variable presenting a modeling approach that can be very attractive from the circuit simulation viewpoint. The new method allows a comprehensive description of the stochastic variability of these devices by introducing a probability distribution that allows the simulation of the main parameter that is employed for the model implementation. A rigorous description of the mathematical theory behind the technique is given and its application for a broad set of experimental measurements is explained.

Keywords: Functional data; Karhunen–Loève expansion; Penalized splines; Resistive switching; Resistive memories; Device variability (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:159:y:2019:i:c:p:197-209

DOI: 10.1016/j.matcom.2018.11.016

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