Stochastic modeling of Random Access Memories reset transitions.
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| Title: | Stochastic modeling of Random Access Memories reset transitions. |
|---|---|
| Authors: | Aguilera-Morillo, M. Carmen1, Aguilera, Ana M.1,2 aaguiler@ugr.es, Jiménez-Molinos, Francisco3, Roldán, Juan B.3 |
| Source: | Mathematics & Computers in Simulation. May2019, Vol. 159, p197-209. 13p. |
| Subjects: | Random variables, Mathematical models, Algebra, Algorithms, Stochastic analysis |
| Abstract: | 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. Highlights • Functional data analysis is applied for modeling Resistive Random Access Memories transitions. • Curve registration, P-spline smoothing and functional principal component analysis is used. • An orthogonal representation of the reset curves in terms of only one random parameter is obtained. • The probability distribution of this random parameter is estimated. [ABSTRACT FROM AUTHOR] |
| Copyright of Mathematics & Computers in Simulation is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: Stochastic modeling of Random Access Memories reset transitions. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Aguilera-Morillo%2C+M%2E+Carmen%22">Aguilera-Morillo, M. Carmen</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Aguilera%2C+Ana+M%2E%22">Aguilera, Ana M.</searchLink><relatesTo>1,2</relatesTo><i> aaguiler@ugr.es</i><br /><searchLink fieldCode="AR" term="%22Jiménez-Molinos%2C+Francisco%22">Jiménez-Molinos, Francisco</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Roldán%2C+Juan+B%2E%22">Roldán, Juan B.</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Mathematics+%26+Computers+in+Simulation%22">Mathematics & Computers in Simulation</searchLink>. May2019, Vol. 159, p197-209. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Random+variables%22">Random variables</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Algebra%22">Algebra</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+analysis%22">Stochastic analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. Highlights • Functional data analysis is applied for modeling Resistive Random Access Memories transitions. • Curve registration, P-spline smoothing and functional principal component analysis is used. • An orthogonal representation of the reset curves in terms of only one random parameter is obtained. • The probability distribution of this random parameter is estimated. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Mathematics & Computers in Simulation is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.matcom.2018.11.016 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 197 Subjects: – SubjectFull: Random variables Type: general – SubjectFull: Mathematical models Type: general – SubjectFull: Algebra Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Stochastic analysis Type: general Titles: – TitleFull: Stochastic modeling of Random Access Memories reset transitions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Aguilera-Morillo, M. Carmen – PersonEntity: Name: NameFull: Aguilera, Ana M. – PersonEntity: Name: NameFull: Jiménez-Molinos, Francisco – PersonEntity: Name: NameFull: Roldán, Juan B. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 03784754 Numbering: – Type: volume Value: 159 Titles: – TitleFull: Mathematics & Computers in Simulation Type: main |
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