Machine learning-based spin structure detection

• URL: http://arxiv.org/abs/2303.16905v1
• Date: Fri, 24 Mar 2023 17:19:31 GMT
• Title: Machine learning-based spin structure detection
• Authors: Isaac Labrie-Boulay, Thomas Brian Winkler, Daniel Franzen, Alena Romanova, Hans Fangohr, Mathias Kl\"aui
• Abstract summary: We report a convolutional neural network specifically designed for segmentation problems to detect the position and shape of skyrmions in our measurements. The results of this study show that a well-trained network is a viable method of automating data pre-processing in magnetic microscopy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One of the most important magnetic spin structure is the topologically stabilised skyrmion quasi-particle. Its interesting physical properties make them candidates for memory and efficient neuromorphic computation schemes. For the device operation, detection of the position, shape, and size of skyrmions is required and magnetic imaging is typically employed. A frequently used technique is magneto-optical Kerr microscopy where depending on the samples material composition, temperature, material growing procedures, etc., the measurements suffer from noise, low-contrast, intensity gradients, or other optical artifacts. Conventional image analysis packages require manual treatment, and a more automatic solution is required. We report a convolutional neural network specifically designed for segmentation problems to detect the position and shape of skyrmions in our measurements. The network is tuned using selected techniques to optimize predictions and in particular the number of detected classes is found to govern the performance. The results of this study shows that a well-trained network is a viable method of automating data pre-processing in magnetic microscopy. The approach is easily extendable to other spin structures and other magnetic imaging methods.

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