Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks
Aguirre, Fernando Leonel (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)
Piros, Eszter (Technische Universität Darmstadt)
Kaiser, Nico (Technische Universität Darmstadt)
Vogel, Tobias (Technische Universität Darmstadt)
Petzold, Stephan (Technische Universität Darmstadt)
Gehrunger, Jonas (Technische Universität Darmstadt)
Oster, Timo (Technische Universität Darmstadt)
Hochberger, Christian (Technische Universität Darmstadt)
Suñé, Jordi 1963- (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)
Alff, Lambert (Technische Universität Darmstadt)
Miranda, Enrique (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)

Fecha:	2022
Resumen:	In this paper, the use of Artificial Neural Networks (ANNs) in the form of Convolutional Neural Networks (AlexNET) for the fast and energy-efficient fitting of the Dynamic Memdiode Model (DMM) to the conduction characteristics of bipolar-type resistive switching (RS) devices is investigated. Despite an initial computationally intensive training phase the ANNs allow obtaining a mapping between the experimental Current-Voltage (I-V) curve and the corresponding DMM parameters without incurring a costly iterative process as typically considered in error minimization-based optimization algorithms. In order to demonstrate the fitting capabilities of the proposed approach, a complete set of I-V s obtained from YO-based RRAM devices, fabricated with different oxidation conditions and measured with different current compliances, is considered. In this way, in addition to the intrinsic RS variability, extrinsic variation is achieved by means of external factors (oxygen content and damage control during the set process). We show that the reported method provides a significant reduction of the fitting time (one order of magnitude), especially in the case of large data sets. This issue is crucial when the extraction of the model parameters and their statistical characterization are required.
Ayudas:	European Commission 101007321 European Commission 783176
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	RRAM ; Neural networks ; Curve fitting ; Dynamic memdiode ; Memristor
Publicado en:	Micromachines, Vol. 13, Issue 11 (November 2022) , art 2002, ISSN 2072-666X

DOI: 10.3390/mi13112002
PMID: 36422434

14 p, 7.1 MB

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