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Field-effect at electrical contacts to two-dimensional materials.

Yao Guo1, Yan Sun1, Alvin Tang2

  • 1School of Physics, Beijing Institute of Technology, Beijing, 100081 China.

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|August 2, 2021
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Summary
This summary is machine-generated.

This study reveals a capacitive field-effect at electrical contacts to 2D materials, impacting integrated circuits. Engineering these contacts can improve device performance and enhance artificial neural network capabilities.

Keywords:
electrical contactfield-effectin-memory-computingnonlinearitytwo-dimensional materials

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Nanotechnology

Background:

  • Inferior electrical contact to two-dimensional (2D) materials presents a significant hurdle for post-silicon very large-scale integrated circuits.
  • Contact resistance is a key metric, but a deeper understanding of contact behavior is needed.

Purpose of the Study:

  • To investigate the electrical contact phenomena in 2D materials beyond simple resistance.
  • To demonstrate and analyze the field-effect at these contacts.
  • To explore the implications of this field-effect for electronic devices and circuits.

Main Methods:

  • Systematic investigation of electrical contacts to 2D materials.
  • Demonstration of a capacitive metal-insulator-semiconductor (MIS) field-effect.
  • Analysis of charge carrier depletion/accumulation and voltage potential redistribution.
  • Application of findings in monolithic analog artificial neural network circuits.

Main Results:

  • A capacitive MIS field-effect at 2D material contacts was demonstrated.
  • This field-effect causes abnormal current saturation and nonlinearity.
  • Current saturation can be mitigated through engineered contact configurations.
  • Nonlinearity enhanced the perception ability of artificial neural network circuits, shown in a COVID-19 prediction model.

Conclusions:

  • The field-effect at 2D material electrical contacts is fundamental to device behavior.
  • Understanding this effect is crucial for designing, simulating, and fabricating 2D material-based electronics.
  • This work offers insights for optimizing 2D material devices and advancing neuromorphic computing.