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Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks

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. (Publisher's Version)
In: Micromachines, 13 (11), MDPI, e-ISSN 2072-666X,
DOI: 10.26083/tuprints-00022979,
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Item Type: Article
Origin: Secondary publication DeepGreen
Status: Publisher's Version
Title: Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks
Language: English
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.

Journal or Publication Title: Micromachines
Volume of the journal: 13
Issue Number: 11
Place of Publication: Darmstadt
Publisher: MDPI
Collation: 14 Seiten
Uncontrolled Keywords: RRAM, neural networks, curve fitting, dynamic memdiode, memristor
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 600 Technik
600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 11 Department of Materials and Earth Sciences > Material Science > Advanced Thin Film Technology
18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering > Integrated Electronic Systems (IES)
18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering > Computer Systems Group
Date Deposited: 19 Dec 2022 12:28
Last Modified: 04 Jan 2023 07:36
DOI: 10.26083/tuprints-00022979
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-229795
Additional Information:

This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications, Volume II

SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/22979
PPN: 503237817
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