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Area of Science:

  • Metamaterials Science
  • Nanotechnology
  • Computational Physics

Background:

  • Artificial Spin Ice (ASI) are metamaterials with interacting nanomagnets, promising for neuromorphic computing.
  • Current ASI excel at data transformation but lack efficient information transmission and storage capabilities.

Purpose of the Study:

  • To discover a mechanism for information transmission and storage in ASI, inspired by Cellular Automata gliders.
  • To develop a controllable magnetic structure for enhanced ASI functionality.

Main Methods:

  • Utilized an evolutionary algorithm to discover the 'snake' glider in pinwheel ASI.
  • Employed global field protocols for precise manipulation of magnetic textures (100 nm scale).
  • Conducted both simulations and experimental validation of the snake's behavior.

Main Results:

  • Discovered and characterized the 'snake', a novel magnetic glider in pinwheel ASI.
  • Demonstrated precise control and manipulation of the snake using global fields.
  • Investigated the snake's movement mechanism and robustness to disorder.

Conclusions:

  • The 'snake' enables integrated information transmission, storage, and transformation within a single magnetic substrate.
  • This finding unlocks the potential for developing ultra-low power neuromorphic computing devices.
  • ASI can be engineered for comprehensive data handling, advancing computing paradigms.