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, 13 (11)
doi: 10.3390/mi13112002
Article, Bibliographie
This is the latest version of this item.
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.
| Item Type: | Article |
|---|---|
| Erschienen: | 2022 |
| Creators: | Aguirre, Fernando Leonel ; Piros, Eszter ; Kaiser, Nico ; Vogel, Tobias ; Petzold, Stephan ; Gehrunger, Jonas ; Oster, Timo ; Hochberger, Christian ; Suñé, Jordi ; Alff, Lambert ; Miranda, Enrique |
| Type of entry: | Bibliographie |
| Title: | Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks |
| Language: | English |
| Date: | 2022 |
| Place of Publication: | Darmstadt |
| Publisher: | MDPI |
| Journal or Publication Title: | Micromachines |
| Volume of the journal: | 13 |
| Issue Number: | 11 |
| Collation: | 14 Seiten |
| DOI: | 10.3390/mi13112002 |
| Corresponding Links: | |
| 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. |
| Uncontrolled Keywords: | RRAM, neural networks, curve fitting, dynamic memdiode, memristor |
| Additional Information: | This article belongs to the Special Issue New Advances in Ionic-Drift Resistive Switching Memory and Neuromorphic Applications, Volume II |
| Classification DDC: | 600 Technology, medicine, applied sciences > 600 Technology 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering |
| Divisions: | 11 Department of Materials and Earth Sciences 11 Department of Materials and Earth Sciences > Material Science 11 Department of Materials and Earth Sciences > Material Science > Advanced Thin Film Technology 18 Department of Electrical Engineering and Information Technology 18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering 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: | 02 Aug 2024 12:46 |
| Last Modified: | 02 Aug 2024 12:46 |
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Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks. (deposited 19 Dec 2022 12:28)
- Fast Fitting of the Dynamic Memdiode Model to the Conduction Characteristics of RRAM Devices Using Convolutional Neural Networks. (deposited 02 Aug 2024 12:46) [Currently Displayed]
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