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Stochastic Modelling of Random Access Memories Reset Transitions

[PDF] Pre-print Aguilera-Morillo et al (2.866Mb)
Identificadores
URI: http://hdl.handle.net/10481/71546
DOI: https://doi.org/10.1016/j.matcom.2018.11.016
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Autor
Aguilera Morillo, María del Carmen; Aguilera Del Pino, Ana María; Jiménez Molinos, Francisco; Roldán Aranda, Juan Bautista
Editorial
Elsevier
Materia
Functional data
 
Karhunen–Loève expansion
 
Penalized splines
 
Resistive switching
 
Resistive memories
 
Device variability
 
Fecha
2019-05
Referencia bibliográfica
M. Carmen Aguilera-Morillo, Ana M. Aguilera, Francisco Jiménez-Molinos, Juan B. Roldán, Stochastic modeling of Random Access Memories reset transitions, Mathematics and Computers in Simulation, Volume 159, 2019, Pages 197-209, ISSN 0378-4754, https://doi.org/10.1016/j.matcom.2018.11.016.
Patrocinador
Spanish Ministry of Economy and Competitiveness MTM2017-88708-P, TEC2017-84321-C4-3-R (also supported by the FEDER program); IMB-CNM (CSIC) in Barcelona; Spanish ICTS Network MICRONANOFABS
Resumen
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.
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