Novel spintronic devices can play a role in the quest for GreenIT if they are stable and can transport and manipulate spin with low power. Devices have been proposed, where switching by energy-efficient approaches is used to manipulate topological spin structures that are stable in multilayers [1] but also in 2D systems [2]. We combine ultimate stability of topological states due to chiral interactions [3,4] with ultra-efficient manipulation using novel spin torques [3-7].

We use stochastic MTJs and skyrmion dynamics for non-conventional stochastic computing applications [8,9]. Beyond spatially multiplexed quasi-static Reservoir Computing [9], we have recently realized time multiplexed Reservoir Computing [10]. Given the huge tunability of thermal spin dynamics of skyrmions, the reservoir response can be tuned to the input timescales allowing for a drastic simplification of the device architecture and dynamic detection of time series with variable input timescales [10, 11].

While stochastic dynamics is thus useful for unconventional computing, machine learning can also be useful for skyrmion research. We have developed a convolutional neural network [12] based on the U-Net framework [13]. Our network is capable of detecting skyrmions and using a three-class approach it outperforms other approaches.

References

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             R. Gruber et al., Nature Commun. vol. 13, pp. 3144, 2022.

9)          K. Raab et al., Nature Commun. vol. 13, pp. 6982, 2022.

10)        G. Beneke et al., Nature Commun. Vol. 15, pp. 8103, 2024.

11)        T. Dohi et al., Nature Commun. vol. 14, pp. 5424, 2023.

12)        I. Labrie-Boulay et al., Phys. Rev. Appl. vol. 21, pp. 014014, 2024.

13)        O. Ronneberger et al., U-Net: Convolutional Networks for Biomedical Image Segmentation, 2015..