RESUMEN
Manipulating magnetic skyrmions by means of a femtosecond (fs) laser pulse has attracted great interest due to their promising applications in efficient information-storage devices with ultralow energy consumption. However, the mechanism underlying the creation of skyrmions induced by an fs laser is still lacking. As a result, a key challenge is to reveal the pathway for the massive reorientation of magnetization from trivial to nontrivial topological states. Here, we studied a series of ferrimagnetic CoHo alloys and investigated the effect of a single laser pulse on the magnetic states. Thanks to the time-resolved magneto-optical Kerr effect and imaging techniques, we demonstrate that the laser-induced phase transitions from single domains into a topological skyrmion phase are mediated by the transient in-plane magnetization state, in real time and space domains, respectively. Combining experiments and micromagnetic simulations, we propose a two-step process for creating skyrmions through laser pulse irradiation: (i) the electron temperature enhancement induces a spin reorientation transition on a picosecond (ps) timescale due to the suppression of perpendicular magnetic anisotropy (PMA) and (ii) the PMA slowly restores, accompanied by out-of-plane magnetization recovery, leading to the generation of skyrmions with the help of spin fluctuations. This work provides a route to control skyrmion patterns using an fs laser, thereby establishing the foundation for further exploration of topological magnetism at ultrafast timescales.
RESUMEN
Spin-transfer torque magnetic memory (STT-MRAM) is currently under intense academic and industrial development, since it features non-volatility, high write and read speed and high endurance. In this work, we show that when used in a non-conventional regime, it can additionally act as a stochastic memristive device, appropriate to implement a "synaptic" function. We introduce basic concepts relating to spin-transfer torque magnetic tunnel junction (STT-MTJ, the STT-MRAM cell) behavior and its possible use to implement learning-capable synapses. Three programming regimes (low, intermediate and high current) are identified and compared. System-level simulations on a task of vehicle counting highlight the potential of the technology for learning systems. Monte Carlo simulations show its robustness to device variations. The simulations also allow comparing system operation when the different programming regimes of STT-MTJs are used. In comparison to the high and low current regimes, the intermediate current regime allows minimization of energy consumption, while retaining a high robustness to device variations. These results open the way for unexplored applications of STT-MTJs in robust, low power, cognitive-type systems.