材料科学
二次离子质谱法
仿形(计算机编程)
涂层
表征(材料科学)
发射率
薄膜
电介质
纳米技术
光电子学
质谱法
计算机科学
光学
量子力学
操作系统
物理
作者
Sarah E. Bamford,Robert Jones,Wil Gardner,Benjamin W. Muir,David A. Winkler,Paul J. Pigram
标识
DOI:10.1002/admi.202300645
摘要
Abstract 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 SnO 2 as a dielectric, ZnO seeding layers, TiO x blocking layers, a Zn base layer, and a TiO x 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.
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