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The contact of metal and semiconductor can lead to the formation of a junction with either Schottky or Ohmic behavior.
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Interfacial electrochemical methods focus on the phenomena occurring at the boundary between an electrode and a solution, as opposed to bulk methods that concentrate on the solution's overall properties. These interfacial methods are classified as either static or dynamic based on the presence of a nonzero current in the electrochemical cell and the consistency of analyte concentrations. Static methods, such as potentiometry, measure the cell's potential without any significant current...
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Using Laser Scanning Microscopy to Determine Electromigration in Molybdenum Disilicide
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Using Machine Learning Potentials to Explore Interdiffusion at Metal-Chalcogenide Interfaces.

Siddarth K Achar1,2,3, Julian Schneider4, Derek A Stewart3

  • 1Computational Modeling & Simulation Program, University of Pittsburgh, Pittsburgh, Pennsylvania15260, United States.

ACS Applied Materials & Interfaces
|December 14, 2022
PubMed
Summary
This summary is machine-generated.

Moment tensor potentials reveal significant interdiffusion at Ti|Ge-Se interfaces in memory devices. This interaction degrades performance, highlighting the need for improved interface engineering in chalcogenide alloy memory elements.

Keywords:
Ovonic threshold switchamorphous chalcogenide alloyscross-point memoryinterface interdiffusionmachine learning potentialsselector

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Computational Materials Science

Background:

  • Chalcogenide alloys are crucial for next-generation nonvolatile memory.
  • Device degradation is linked to interdiffusion at interfaces under operational stress.
  • Understanding atomic-scale interactions is key to improving device endurance.

Purpose of the Study:

  • To develop and apply machine learning (ML) potentials for simulating chalcogenide alloy-electrode interactions.
  • To investigate the atomic-scale mechanisms of interdiffusion at Ti|Ge-Se interfaces.
  • To assess the impact of these interactions on memory element performance.

Main Methods:

  • Development of moment tensor potentials (MTPs) using an active learning framework.
  • Utilizing a large dataset of first-principles calculations for Ti, Ge, and Se.
  • Performing long-term molecular dynamics simulations (>1 ns) at various temperatures.

Main Results:

  • Significant interdiffusion observed at the Ti|Ge-Se interface, with both Ti and Se migrating.
  • Formation of a distinct Ti-Se region and a Se-depleted Ge-Se region.
  • Self-limited growth model describes Ti-Se layer evolution; low activation energy (0.1 eV) for interdiffusion found.

Conclusions:

  • ML-based MTPs are effective for studying interface behavior in nanoscale devices.
  • Interdiffusion at Ti|Ge-Se interfaces negatively impacts selector characteristics.
  • Findings provide insights for designing more durable chalcogenide-based memory devices.