Profiling a Low Emissivity Glass Coating with ToF-SIMS and Machine Learning

Characterization of multilayer coatings in 3D presents many challenges, as composition can change by area and by depth. Compositional characteristics of the interior of multilayer coatings emerge during analysis, so are frequently discovered only through exacting retrospective investigations. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) can be used to elucidate such complex systems; however, data analysis is a challenge. In this work a detail presentation is done of 3D chemical characterization of a low emissivity (low-E) double silver coating on glass using ToF-SIMS and machine learning. An unsupervised machine learning technique, the self-organizing map with relational perspective mapping, is used to visualize the chemical similarity between different layers of the low-E film. Repeating layers are easily identified at the single-voxel level, based on their entire mass spectra, and are classified as chemically indistinguishable. All major film components are identified, including the use of SnO2 as a dielectric, ZnO seeding layers, TiOx blocking layers, a Zn base layer, and a TiOx topcoat. The thin optically active silver layers are examined in detail, demonstrating subtle chemical changes with depth. This technique provides excellent insight into manufacturing processes and production challenges and has excellent potential in forensic applications. A 3D chemical characterisation of a low emissivity (low-E) double silver coating on glass using time-of-flight secondary ion mass spectrometry (ToF-SIMS) and machine learning is presented. All coating layers are identified. The dataset is classified at the single-voxel level, based on the entire mass spectrum. This technique provides excellent insight into manufacturing processes and production challenges and has strong potential in forensic applications.image