摘要: Manipulation of light-induced magnetization has become a fundamentally hot
topic with a potentially high impact for atom trapping, confocal and magnetic
resonance microscopy, and data storage. The control of the magnetization
orientation mainly relies on the direct methods composed of amplitude, phase
and polarization modulations of the incident light under the tight focusing
condition, leaving the achievement of arbitrary desirable three-dimensional
(3D) magnetization orientation complicated, inflexible and inefficient. Here,
we propose a facile approach called machine learning inverse design to achieve
expected vectorial opto-magnetization orientation. This pathway is
time-efficient and accurate to produce the demanded incident beam for arbitrary
prescribed 3D magnetization orientation. It is highlighted that the machine
learning method is not only applied for magnetization orientations, but also
widely used in the control of magnetization structures.