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Sol-Gel-Processed Y2O3 Multilevel Resistive Random-Access Memory Cells for Neural Networks.

Taehun Lee1, Hae-In Kim1, Yoonjin Cho1

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Nanomaterials (Basel, Switzerland)
|September 9, 2023
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Yttrium oxide resistive random-access memory (RRAM) devices achieve multilevel cell switching by controlling conductive filament formation. These devices show promise for high-accuracy neural network applications.

Keywords:
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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Nanotechnology

Background:

  • Resistive random-access memory (RRAM) offers promising non-volatile memory solutions.
  • Yttrium oxide (Y2O3) is explored as a potential dielectric material for RRAM devices.
  • Achieving multilevel cell (MLC) functionality is key for increasing memory density.

Purpose of the Study:

  • To fabricate and characterize Y2O3-based RRAM devices.
  • To investigate the impact of current compliance on device performance and filament formation.
  • To evaluate the potential of these RRAM devices for MLC switching and neuromorphic computing.

Main Methods:

  • Sol-gel fabrication of Y2O3 thin films on ITO/glass substrates.
  • Bipolar resistive switching measurements with varying current compliance.
  • Analysis of conductive filament composition and evolution.
  • Numerical simulations for neural network applications.

Main Results:

  • Y2O3 RRAM devices exhibited bipolar switching without a forming process.
  • Adjusting current compliance controlled Ag filament formation and enabled MLC switching.
  • Devices achieved three low-resistance states and one high-resistance state for 2-bit/cell capacity.
  • Simulated neural networks using these RRAM devices achieved ~88% digit image classification accuracy.

Conclusions:

  • Yttrium oxide RRAM devices are suitable for multilevel cell operation.
  • Controlled filament engineering is crucial for MLC functionality.
  • These RRAM devices show significant potential for practical neuromorphic systems.
  • The fabricated devices offer a viable memory component for future AI hardware.