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UV hyperspectral imaging as process analytical tool for the characterization of oxide layers and copper states on direct bonded copper

  • Hyperspectral imaging and reflectance spectroscopy in the range from 200–380 nm were used to rapidly detect and characterize copper oxidation states and their layer thicknesses on direct bonded copper in a non-destructive way. Single-point UV reflectance spectroscopy, as a well-established method, was utilized to compare the quality of the hyperspectral imaging results. For the laterally resolved measurements of the copper surfaces an UV hyperspectral imaging setup based on a pushbroom imager was used. Six different types of direct bonded copper were studied. Each type had a different oxide layer thickness and was analyzed by depth profiling using X-ray photoelectron spectroscopy. In total, 28 samples were measured to develop multivariate models to characterize and predict the oxide layer thicknesses. The principal component analysis models (PCA) enabled a general differentiation between the sample types on the first two PCs with 100.0% and 96% explained variance for UV spectroscopy and hyperspectral imaging, respectively. Partial least squares regression (PLS-R) models showed reliable performance with R2c = 0.94 and 0.94 and RMSEC = 1.64 nm and 1.76 nm, respectively. The developed in-line prototype system combined with multivariate data modeling shows high potential for further development of this technique towards real large-scale processes.

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Metadaten
Author of HS ReutlingenBrecht, Marc; Al Ktash, Mohammad; Rebner, Karsten; Ostertag, Edwin; Stiedl, Jan; Drechsel, Maryam; Stefanakis, Mona; Boldrini, Barbara
URN:urn:nbn:de:bsz:rt2-opus4-32949
DOI:https://doi.org/10.3390/s21217332
Erschienen in:Sensors
Publisher:MDPI
Place of publication:Basel
Document Type:Journal article
Language:English
Publication year:2021
Tag:UV spectroscopy; copper oxide layer thickness; direct bonded copper; hyperspectral imaging; partial least squares regression; principal component analysis; pushbroom
Volume:21
Issue:21
Page Number:13
Article Number:7332
DDC classes:620 Ingenieurwissenschaften und Maschinenbau
Open access?:Ja
Licence (German):License Logo  Creative Commons - CC BY - Namensnennung 4.0 International