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Approaches for Memristive Structures Using Scratching Probe Nanolithography: Towards Neuromorphic Applications.

Roman V Tominov1,2, Zakhar E Vakulov1, Vadim I Avilov1

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Summary
This summary is machine-generated.

This study uses atomic force microscopy (AFM) nanolithography to optimize memristor fabrication. The research demonstrates control over memristor size and contact thickness, crucial for neuromorphic computing applications.

Keywords:
ReRAMTaguchi methodZnO thin filmsartificial intelligencememristorneuromorphic systemspulsed laser depositionresistive switchingscratching probe nanolithography

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Electrical Engineering

Background:

  • Resistive switching in oxide thin films is key for memristor devices.
  • Controlling memristor dimensions is essential for reliable performance.
  • Atomic Force Microscopy (AFM) offers high-resolution patterning capabilities.

Purpose of the Study:

  • To investigate resistive switching in oxide thin films using AFM scratching probe nanolithography.
  • To assess the impact of memristor size and top-contact thickness on resistive switching characteristics.
  • To optimize AFM parameters for reliable nanostructure fabrication.

Main Methods:

  • Utilized scratching probe nanolithography with Atomic Force Microscopy (AFM).
  • Employed the Taguchi method to optimize AFM parameters (normal load, scratch distance, probe speed, direction).
  • Fabricated and characterized Si/TiN/ZnO/photoresist memristor structures with varying dimensions.

Main Results:

  • Achieved controlled fabrication of pinholes (25.4–85.1 nm) and grooves (40.5 nm depth) with low roughness.
  • Demonstrated control over memristor size (27–83 nm), yielding HRS/LRS ratios from ~8 to ~128.
  • Showed control over TiN top contact thickness (8.3–32.4 nm), resulting in HRS/LRS ratios from ~22 to ~188.

Conclusions:

  • AFM nanolithography provides precise control over memristor dimensions.
  • Optimized fabrication parameters enable tunable resistive switching properties.
  • The findings support the development of memristive devices for neuromorphic AI.