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

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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.
Funding Information
  • ECSEL Joint Undertaking (JU) (101007321, 783176, 16MEE0154, 16ESE0298, AL 560/13-2, 384682067, AL 560/21-1)
  • ECSEL Joint Undertaking project WAKeMeUP in collaboration with the European Union’s H2020 Framework Program (H2020/2014-2020) and National Authorities (101007321, 783176, 16MEE0154, 16ESE0298, AL 560/13-2, 384682067, AL 560/21-1)
  • Bundesministerium für Forschung, Technologie und Raumfahrt (101007321, 783176, 16MEE0154, 16ESE0298, AL 560/13-2, 384682067, AL 560/21-1)
  • Deutsche Forschungsgemeinschaft (101007321, 783176, 16MEE0154, 16ESE0298, AL 560/13-2, 384682067, AL 560/21-1)
  • Merck KGaA, Darmstadt, Germany (101007321, 783176, 16MEE0154, 16ESE0298, AL 560/13-2, 384682067, AL 560/21-1)
  • EMPIR Programme (101007321, 783176, 16MEE0154, 16ESE0298, AL 560/13-2, 384682067, AL 560/21-1)

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