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Numerical Modeling of Vortex-Based Superconducting Memory Cells: Dynamics and Geometrical Optimization.

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Area of Science:

  • Superconducting electronics
  • Quantum computing hardware
  • Nanoscale device physics

Background:

  • Dense random-access memory is a key limitation for digital superconducting computers.
  • Abrikosov vortex RAM (AVRAM) cells offer potential for nanoscale memory through quantized magnetic flux storage.

Purpose of the Study:

  • To numerically model AVRAM cells for digital superconducting computers.
  • To determine optimal parameters for zero magnetic field operation and controllable vortex manipulation.
  • To investigate the potential for miniaturization and ultrafast switching.

Main Methods:

  • Time-dependent Ginzburg-Landau equations for numerical modeling.
  • Design of a fluxonic quantum dot cell with specific components (island, notch, track, trap).
  • Analysis of geometrical parameters and current pulse manipulation.

Main Results:

  • Optimal geometrical parameters for zero magnetic field operation identified.
  • Controllable vortex manipulation achieved using short current pulses.
  • Ultrafast vortex motion observed, exceeding macroscopic superconductor velocities.
  • Cell scaling down to ~100 nm demonstrated, enabling picosecond switching and ~10-19 J energy per operation.

Conclusions:

  • AVRAM cells are a viable candidate for dense, high-speed superconducting memory.
  • Ultrafast vortex dynamics are driven by mesoscopic island interactions and nonlinear viscosity reduction.
  • The proposed cell design enables significant miniaturization and energy efficiency for future superconducting computing.