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Scanning Thermal Microscopy Method for Self-Heating in Nonlinear Devices and Application to Filamentary Resistive

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Summary
This summary is machine-generated.

A new Scanning Thermal Microscopy (SThM) method measures self-heating in nonlinear devices for neuromorphic computing. This technique quantifies thermal properties without material-specific calibration, advancing device characterization.

Keywords:
calibrationlessnanoscale thermometrynonequilibriumquantitativeresistive RAMscanning thermal microscopyself-heating

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Computational Neuroscience

Background:

  • Neuromorphic computing relies on devices with nonlinear resistance-voltage characteristics, often driven by temperature-dependent processes like ion migration.
  • Scanning Thermal Microscopy (SThM) is a high-resolution technique for thermal property analysis, but its quantitative application is hindered by device nonlinearity.
  • Existing SThM methods struggle with accurate thermal measurements of nonlinear devices due to self-heating effects and contact variations.

Purpose of the Study:

  • To develop an extended nonequilibrium scheme for quantitative temperature measurement in nonlinear devices using SThM.
  • To investigate the self-heating phenomena in HfO2-based resistive random-access memory (RRAM) devices.
  • To enable material-independent thermal characterization of nonlinear electronic components.

Main Methods:

  • An extended nonequilibrium scheme employing both DC and AC voltage modulation was applied to SThM.
  • Simultaneous calculation of tip-sample thermal resistance and device temperature rise was achieved.
  • The method was applied to HfO2-based RRAM devices to study filamentary switching dynamics.

Main Results:

  • The proposed SThM scheme successfully measured self-heating in nonlinear HfO2-based RRAM devices without contact calibration.
  • Temperature imaging and thermal wave propagation were visualized, revealing insights into filamentary switching.
  • Key thermal properties such as filament number, thermal confinement, and crosstalk were extracted.

Conclusions:

  • The developed SThM method provides a robust, material-independent approach for characterizing thermal properties of nonlinear devices.
  • This technique is crucial for understanding and optimizing devices for neuromorphic computing applications.
  • The study demonstrates the capability to image thermal dynamics and extract critical parameters in RRAM devices.