Machine-learning-based detection of spin structures

One of the most important magnetic spin structures is the topologically stabilized skyrmion quasiparticle. Its interesting physical properties make it a candidate for memory and efficient neuromorphic computation schemes. For device operation, the 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, in which, depending on the sample's 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 show that a well-trained network is a viable method of automating data preprocessing in magnetic microscopy. The approach is easily extendable to other spin structures and other magnetic imaging methods.