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Highly reproducible and CMOS-compatible VO2-based oscillators for brain-inspired computing.

Olivier Maher1,2, Roy Bernini3, Nele Harnack3

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|May 21, 2024
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Summary
This summary is machine-generated.

Vanadium dioxide (VO2) shows promise for artificial intelligence hardware. This study optimizes VO2 film fabrication on silicon, enabling reproducible, low-variability oscillators for advanced computing applications.

Keywords:
Brain inspired computingNeuromorphic engineeringOscillating neural networksPhase-change materialsRelaxation oscillatorsVO2

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Computer Engineering

Background:

  • Vanadium dioxide (VO2) exhibits unique electrical and optical switching properties near room temperature.
  • VO2 is a promising material for unconventional computing architectures like oscillating neural networks.
  • Challenges remain in fabricating stable, low-variability VO2 devices on silicon for large-scale integration.

Purpose of the Study:

  • To investigate the impact of annealing parameters on VO2 grain formation.
  • To develop an optimal substrate stack for reproducible VO2 device fabrication.
  • To present a recipe for creating stable VO2-based oscillators with minimal variability.

Main Methods:

  • Atomic layer deposition of vanadium oxide followed by three annealing methods: slow thermal, flash, and rapid thermal annealing.
  • Material and electrical characterization of fabricated VO2 films.
  • Optimization of a substrate stack including a hafnium oxide (HfO2) interlayer.

Main Results:

  • Identified optimal annealing parameters and substrate configuration for VO2 formation.
  • Demonstrated a step-by-step recipe for reproducible VO2 oscillator fabrication.
  • Achieved low variability between multiple VO2-based devices.

Conclusions:

  • The HfO2 interlayer is crucial for stabilizing VO2 and minimizing device variability.
  • The developed fabrication process enables the creation of highly reproducible VO2 oscillators.
  • This work paves the way for large-scale VO2 oscillating neural networks for AI applications.