Numerical modelling of a vortex-based superconducting memory cell: dynamics and geometrical optimization of a fluxonic quantum dot

TL;DR

This paper presents numerical modeling of a vortex-based superconducting memory cell, demonstrating ultra-fast vortex dynamics, optimal geometrical design, and potential for nanoscale, energy-efficient, ultrafast superconducting memory devices.

Contribution

It introduces a detailed numerical model of a fluxonic quantum dot memory cell, optimizing its geometry for zero-field operation and controllable vortex manipulation.

Findings

Ultra-fast vortex velocities exceeding expectations for macroscopic superconductors.

Scalability of the cell to sizes around 100 nm, comparable to the London penetration depth.

Potential for picosecond-scale switching with ultra-low energy consumption of 0.1 aJ.

Abstract

The lack of dense random-access memory is one of the main obstacles to the development of digital superconducting computers. It has been suggested that AVRAM cells, based on the storage of a single Abrikosov vortex, the smallest quantized object in superconductors, can enable drastic miniaturization to the nanometer scale. In this work, we present numerical modeling of such cells using time-dependent Ginzburg-Landau equations. The cell represents a fluxonic quantum dot containing a small superconducting island, an asymmetric notch for vortex entrance, a guiding track, and a vortex trap. We determine the optimal geometrical parameters for operation at zero magnetic field and the conditions for controllable vortex manipulation by short current pulses. We report ultra-fast vortex motion with velocities more than an order of magnitude faster than those expected for macroscopic superconductors. This phenomenon is attributed to strong interactions with the edges of a mesoscopic island, combined with the nonlinear reduction of flux-flow viscosity due to nonequilibrium effects in the track. Our results show that such cells can be scaled down to sizes comparable to the London penetration depth, 100 nm, and can enable ultrafast switching on the picosecond scale with ultra-low energy per operation, 0.1 aJ.