Optimal control of laser-induced spin-orbit mediated ultrafast demagnetization

被引:33
作者
Elliott, P. [1 ]
Krieger, K. [1 ]
Dewhurst, J. K. [1 ]
Sharma, S. [1 ,2 ]
Gross, E. K. U. [1 ]
机构
[1] Max Planck Inst Mikrostrukturphys, Weinberg 2, D-06120 Halle, Germany
[2] Indian Inst Technol Roorkee, Dept Phys, Roorkee 247667, Uttarkhand, India
关键词
TDDFT; QOCT; laser induced spin dynamics; demagnetization; coherent control; DENSITY-FUNCTIONAL THEORY; QUANTUM-OPTIMAL-CONTROL; MAGNETIZATION DYNAMICS; LATTICE-RELAXATION; FILMS;
D O I
10.1088/1367-2630/18/1/013014
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Laser induced ultrafast demagnetization is the process whereby the magnetic moment of a ferromagnetic material is seen to drop significantly on a timescale of 10-100 s of femtoseconds due to the application of a strong laser pulse. If this phenomenon can be harnessed for future technology, it offers the possibility for devices operating at speeds several orders of magnitude faster than at present. A key component to successful transfer of such a process to technology is the controllability of the process, i.e. that it can be tuned in order to overcome the practical and physical limitations imposed on the system. In this paper, we demonstrate that the spin-orbit mediated form of ultrafast demagnetization recently investigated (Krieger et al 2015 J. Chem. Theory Comput. 11 4870) by ab initio time-dependent density functional theory (TDDFT) can be controlled. To do so we use quantum optimal control theory (OCT) to couple our TDDFT simulations to the optimization machinery of OCT. We show that a laser pulse can be found which maximizes the loss of moment within a given time interval while subject to several practical and physical constraints. Furthermore we also include a constraint on the fluence of the laser pulses and find the optimal pulse that combines significant demagnetization with a desire for less powerful pulses. These calculations demonstrate optimal control is possible for spin-orbit mediated ultrafast demagnetization and lays the foundation for future optimizations/simulations which can incorporate even more constraints.
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页数:8
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