The project:

WP1 aims to assess the actual discrepancies of measurements of the DMI and to establish guidelines for reducing the measurement uncertainty. The WP builds on the recent results of the EMPIR project TOPS, which showed that the two most popular measurement methods (a) asymmetric bubble domain expansion by MOKE and b) spin wave dispersion non reciprocity by BLS) are potential candidates for measurement services for industry. One of the results of TOPS was to give guidelines for a reliable measurement employing these methods. While the measurement uncertainty of a single measurement is excellent for high quality samples, there are remaining open questions, such as the large discrepancy between MOKE and BLS measurements of identical samples or why one method works better for certain types of samples. WP1 will tackle these problems by a) preparing well characterised samples with different DMI values and properties, b) determining and comparing the actual measurement uncertainty of a single measurement performed by the two methods with the aim to propose practices for improving it and c) studying repeatability and reproducibility of the two techniques correlated to spatial inhomogeneities of the samples. WP1 will be complemented by WP2, which will interpret the results of WP1 on a theoretical basis. Furthermore, WP1 will build the physical basis for a successful ML approach applied in WP3, since the validation of the ML approach requires the availability of accurate and reliable measurements.

WP2 aims to correlate the measurement discrepancies and uncertainties observed in WP1 to physical parameters and to build a solid theoretical basis for the ML approach in WP3. Task 1 of WP2 is dedicated to a review of the different analytical models for the evaluation of the DMI from the measurement and proposes innovative approaches taking into account e.g. the magnetic history of the sample and/or defects and inhomogeneities. Task 2 is for micromagnetic simulations, which represent an indispensable tool for testing magnetic systems. Simulations will be performed to reproduce the asymmetric bubble expansion technique, and they will complement the statistical approach in WP1 Task3 by considering defects and inhomogeneities. A parameter sensitivity analysis will be carried out for the validation of the analytical model and the benchmark with experimental achievements, which represent Task 3. WP2 is essential for a successful ML approach applied in WP3, since ML requires a clear and unique connection between the training dataset and the material parameters provided by accurate analytical or numerical models.

WP3 aims to evaluate from WP1 and WP2 the actual measurement reliability and to employ an innovative approach to the measurement analysis of the DMI. This approach will help to overcome the difficulties due to the statistical spread of measurement data and has therefore the ability to revolutionise metrology in fields where these play an important role (nanotechnology, spintronics, etc.). In particular, four steps are needed: i) standardise the procedures and the data analysis to be able to determine the uncertainty budget (Task1), ii) apply ML methods to trial and test simulation and experimental data to determine the DMI (Task2); iii) evaluate various sources of noise and errors to be able to perform a Uncertainty Quantification and a parameter sensitivity analysis (Task3); iv) validate the overall ML approach, and set a few guidelines for estimation of spintronics parameters.

The project encompasses various tasks including knowledge transfer, training, data management, open access. Under WP4 Task1, initiatives such as creating a project website, sending newsletters, forming a stakeholder committee, publishing papers, participating in conferences, and developing webinars or online courses are planned. In WP4 Task2, activities involve preparing a good practice guide and assessing measurement reproducibility. WP4 Task3 focuses on research data management, open access publishing, and developing a data management plan. In WP4 Task4, project coordination and management entail regular interaction among leaders, monthly reporting, holding meetings, and conducting risk assessments. These tasks aim to ensure effective knowledge dissemination, stakeholder engagement, data integrity, and project progress.