An Analytic Equation for Assessing the Thickness of Titanium Nitride Coatings by Energy Dispersive X-ray Spectroscopy in the Scanning Electron Microscope

© 2023 The Author(s). Published by Oxford University Press on behalf of the Microscopy Society of America. All rights reserved.In this study, a methodology for assessing the thickness of titanium nitride (TiN) coatings by energy dispersive X-ray spectroscopy (EDS) in the scanning electron microscope...

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Detalles Bibliográficos
Autores: Cruz J.P.N., Garzon C.M., Recco, Abel Andre Candido
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:Brasil
Institución:Universidade do Estado de Santa Catarina (UDESC)
Repositorio:Repositório Institucional da Udesc
Idioma:inglés
OAI Identifier:oai:repositorio.udesc.br:UDESC/2286
Acceso en línea:https://repositorio.udesc.br/handle/UDESC/2286
Access Level:acceso abierto
Descripción
Sumario:© 2023 The Author(s). Published by Oxford University Press on behalf of the Microscopy Society of America. All rights reserved.In this study, a methodology for assessing the thickness of titanium nitride (TiN) coatings by energy dispersive X-ray spectroscopy (EDS) in the scanning electron microscope is explored. A standardless method is applied, where the film thickness (th) is related to the microscope accelerating voltage (V0), the type of substrate and the ratio between the more intense peaks in the EDS spectrum, arising from both the substrate and the coating (afterwards called the I-ratio, IR). Three different substrates covered with TiN were studied, namely, silicon, glass, and stainless steel. Monte Carlo simulations enabled to state an analytic equation, which allows assessing the coating thickness as follows:th=thcr⋅exp[-βIR1/n] where IR = Iksubstrate/Ikcoating, thcr (critical thickness) is the largest coating thickness, which is assessable at a fixed V0, β is a multiplication factor, and n is an exponent, where thcr, β and n are assessable from V0 and substrate type. Interpolation via the equation presented, using reference thicknesses, allowed thickness predictions with around 80% of datapoints differing less than around 2% from the reference value. A procedure for detecting variations as low as 1.0% in coating thickness regarding the nominal thickness is presented.