Aguirre, Fernando Leonel ; Piros, Eszter ; Kaiser, Nico ; Vogel, Tobias ; Petzold, Stephan ; Gehrunger, Jonas ; Oster, Timo ; Hochberger, Christian ; Suñé, Jordi ; Alff, Lambert ; Miranda, Enrique (2022)
Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks.
In: Micromachines, 2022, 13 (11)
doi: 10.26083/tuprints-00022979
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
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-Vs obtained from Y₂O₃-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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Aguirre, Fernando Leonel ; Piros, Eszter ; Kaiser, Nico ; Vogel, Tobias ; Petzold, Stephan ; Gehrunger, Jonas ; Oster, Timo ; Hochberger, Christian ; Suñé, Jordi ; Alff, Lambert ; Miranda, Enrique |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Micromachines |
| Jahrgang/Volume einer Zeitschrift: | 13 |
| (Heft-)Nummer: | 11 |
| Kollation: | 14 Seiten |
| DOI: | 10.26083/tuprints-00022979 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/22979 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (Abstract): | 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-Vs obtained from Y₂O₃-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. |
| Freie Schlagworte: | RRAM, neural networks, curve fitting, dynamic memdiode, memristor |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-229795 |
| Zusätzliche Informationen: | This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications, Volume II |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 600 Technik 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 11 Fachbereich Material- und Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft > Fachgebiet Dünne Schichten 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik > Integrierte Elektronische Systeme (IES) 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik > Rechnersysteme |
| Hinterlegungsdatum: | 19 Dez 2022 12:28 |
| Letzte Änderung: | 20 Dez 2022 13:48 |
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| Suche nach Titel in: | TUfind oder in Google |
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- Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks. (deposited 19 Dez 2022 12:28) [Gegenwärtig angezeigt]
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