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Compact Two-State-Variable Second-Order Memristor Model.

Sungho Kim1, Hee-Dong Kim1, Sung-Jin Choi2

  • 1Department of Electrical Engineering, Sejong University, Seoul, 05006, South Korea.

Small (Weinheim an Der Bergstrasse, Germany)
|May 7, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new two-state-variable memristor model for accurate circuit simulation. The advanced model captures complex resistive switching dynamics for both DC and AC signals.

Keywords:
analytical modelsdevice dynamicsmemristorsresistive switchingsecond-order

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computational Physics

Background:

  • Accurate memristor modeling is crucial for developing functional circuits.
  • Existing first-order models with one state-variable are insufficient for capturing complex resistive switching behaviors.
  • Resistive switching involves multiple physical mechanisms that necessitate more sophisticated models.

Purpose of the Study:

  • To present a novel second-order memristor model with two state-variables.
  • To accurately predict memristor resistive switching characteristics using a compact physical model.
  • To enable the implementation of memristor models in circuit simulators.

Main Methods:

  • Developed a second-order memristor model incorporating two state-variables.
  • Modeled axial drift of charged vacancies under an electric field.
  • Incorporated radial vacancy motion due to thermophoresis and diffusion.

Main Results:

  • The two-state-variable model accurately emulates short-term dynamics like decay and temporal heat summation.
  • The model successfully predicts resistive switching characteristics for both DC and AC input signals.
  • The proposed model offers improved accuracy over first-order memristor models.

Conclusions:

  • The presented second-order memristor model provides a more accurate and predictive tool for circuit design.
  • This model enhances the understanding and application of memristive devices.
  • The model's ability to capture complex dynamics makes it suitable for advanced memristor-based circuit simulations.