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Open Access Publications from the University of California

Magnetic Memory with Antiferromagnets and Multilayers

  • Author(s): Barra, Anthony
  • Advisor(s): Carman, Gregory P
  • et al.

In the next 10 years, the demand for data storage will increase exponentially until current storage methods are economically untenable. The speed and energy efficiency of digital memory will need to be improved by at least a factor of 100-10,000 times. Magnetic memory offers a major energy efficiency improvement (> 100 times) because it can be integrated with voltage-controlled switching methods, like multiferroicity (i.e. strain-coupling), but it is also unfortunately speed limited by the material’s ferromagnetic resonance. To surpass the speed limit, ferromagnetic materials can be substituted by magnetic multilayers or antiferromagnets, since their resonances are 10-1000 times higher. However, further work is required to integrate these under-studied materials into the necessary highly energy efficient multiferroic control schemes. In this dissertation, three main problems are addressed regarding voltage control of multilayers and antiferromagnets. First, the level of exchange coupling and magnetic property averaging in multilayers is not well understood. In this dissertation, a novel micromagnetic simulation of multilayer is presented that includes a distinct multilayer exchange coupling term, and the model’s predictions are compared to experimental magnetic depth profiles obtained via neutron scattering. Second, a deficiency in the literature regarding strain control of antiferromagnets is corrected by presenting a new antiferromagnetic magneto-electro-mechanical model that predicts both near THz and aJ-level energy costs for switching. Finally, the first experimental test to measure strain-induced anisotropy in antiferromagnets is presented, showing that small strains (around 300 με) produces magnetoresistance changes similar to those observed when 3 Tesla of external magnetic field is applied. This work should provide new pathways to simulate and integrate next-generation materials choices into magnetic memory.

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