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A Neural Network Approach towards Generalized Resistive Switching Modelling.

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Summary

This study introduces an artificial neural network for modeling resistive switching behavior in electronic devices. The approach offers a flexible, general solution for device design, achieving high accuracy on simulated and real amorphous IGZO devices.

Keywords:
a-IGZOartificial neural network (ANN)device modellingresistive switching

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computational Science

Background:

  • Resistive switching is a key characteristic in many electronic materials, leading to diverse device behaviors.
  • Current modeling approaches struggle to provide a universal solution for system design due to device variability.
  • There is a need for general modeling tools that can adapt to evolving device characteristics.

Purpose of the Study:

  • To develop a flexible and general artificial neural network (ANN) learning approach for resistive switching (RS) modeling.
  • To demonstrate the efficacy of the ANN approach on both simulated and real-world devices.
  • To investigate the impact of network architecture on modeling accuracy and complexity.

Main Methods:

  • An artificial neural network (ANN) model was trained to learn resistive switching characteristics.
  • The ANN model was validated using two simulated resistive switching devices.
  • The model's performance was further evaluated on a 4 μm² amorphous Indium Gallium Zinc Oxide (IGZO) device.

Main Results:

  • The ANN approach achieved a normalized root-mean-squared error (NRMSE) of 5.66 × 10⁻³ for the amorphous IGZO device using a [2, 50, 50, 1] network structure.
  • Further optimization of network architecture yielded a best NRMSE of 4.63 × 10⁻³.
  • The model demonstrated a good balance between complexity and accuracy, indicating flexibility across different device types.

Conclusions:

  • The proposed artificial neural network learning approach provides a viable and flexible method for modeling resistive switching behavior.
  • This generalized modeling technique can be readily updated as new devices emerge, addressing a key challenge in system design.
  • The low error rates achieved confirm the potential of ANNs for accurate and adaptable modeling of diverse resistive switching devices.