Quantifying charge–to–spin conversion efficiency in magnetically–doped topological insulator heterostructures

ORAL

Abstract

We deployed a magneto–optical mangetometer and an electrical loop shift method to directly quantify the charge–to–spin conversion efficiency in a magnetically–doped topological insulator heterostructure. While these two approaches are essentially different in their experimental principles, quantitative agreements are found in values obtained by the two approaches. This consistency strongly suggests both methods can accurately estimate the charge–to–spin conversion efficiency without some ambiguity reported previously with other approaches. The charge–to–spin conversion efficiency, which is parameterized by the spin Hall angle tangent, is estimated to be 0.46 and 0.38 at 12K by the magneto–optical mangetometer and the electrical loop shift method, respectively. This value is at least one order larger than those of conventional heavy metals. Our results also reveal that magneto–optical mangetometer and loop shift methods are both reliable and easily accessible for investigation of magnetization dynamics in TI–based magnetic structures.

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Presenters

  • Quanjun Pan

    Electrical and Computer Engineering Department, University of California, Los Angeles

Authors

  • Quanjun Pan

    Electrical and Computer Engineering Department, University of California, Los Angeles

  • Xiaoyu Che

    Electrical Engineering, University of California, Los Angeles, ECE, UCLA, Electrical and Computer Engineering Department, University of California, Los Angeles

  • Qiming Shao

    Electrical Engineering, University of California, Los Angeles, Electrical and Computer Engineering, University of California, Los Angeles, ECE, UCLA, University of California, Los Angeles, Electrical and Computer Engineering Department, University of California, Los Angeles, Department of Electrical Engineering, University of California, Los Angeles

  • Yabin Fan

    Microsystems Technology Laboratories, MIT, Microsystems Technology Laboratories, Massachusetts Institute of Technology

  • Lei Pan

    Electrical Engineering, University of California, Los Angeles, University of California, Los Angeles, University of California Los Angeles, Department of Electrical Engineering, University of California, Los Angeles, Electrical and Computer Engineering Department, University of California, Los Angeles

  • Hao Wu

    Electrical and Computer Engineering, University of California, Los Angeles, University of California, Los Angeles, Electrical and Computer Engineering Department, University of California, Los Angeles

  • Peng Zhang

    Department of Electrical Engineering, University of California, Los Angeles, Electrical and Computer Engineering Department, University of California, Los Angeles

  • Mohammad Montazeri

    Electrical and Computer Engineering Department, University of California, Los Angeles

  • Kang L. Wang

    University of California, Los Angeles, University of California Los Angeles, ECE, UCLA, Electrical and Computer Engineering Department, University of California, Los Angeles