Machine learning-aided automatic recognition and precise localization of marker layers within multilayer Laue lenses (MLLs) for high-resolution X-ray nanofocusing

We present a new method for the automatic recognition and precise localization of marker layers within multilayer Laue lenses (MLLs) used for high-resolution X-ray nanofocusing. This approach integrates image processing techniques with machine learning algorithms, encompassing multiple stages including marker layer identification, coarse localization, differentiation, fine localization, and profile fitting. By directly obtaining marker layer profiles, this method eliminates errors induced by various factors such as manual measurements and image misalignment. It is robust and effective in the presence of various image defects. The proposed method enhances and streamlines the characterization of MLL optics, enabling accurate assessment of zone placement and detailed multilayer profile analysis. Consequently, it advances the fabrication of MLL optics and improves their application in high-resolution X-ray nanofocusing.