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Characterisation of oxide layers on technical copper based on visible hyperspectral imaging

  • The detection and characterisation of oxide layers on metallic copper samples plays an important role for power electronic modules in the automotive industry. However, since precise identification of oxide layers by visual inspection is difficult and time consuming due to inhomogeneous colour distribution, a reliable and efficient method for estimating their thickness is needed. In this study, hyperspectral imaging in the visible wavelength range (425–725 nm) is proposed as an in-line inspection method for analysing oxide layers in real-time during processing of copper components such as printed circuit boards in the automotive industry. For implementation in the production line a partial least square regression (PLSR) model was developed with a calibration set of n = 12 with about 13,000 spectra per sample to determine the oxide layer thickness on top of the technical copper surfaces. The model shows a good prediction performance in the range of 0–30 nm compared to Auger electron spectroscopy depth profiles as a reference method. The root mean square error (RMSE) is 1.75 nm for calibration and 2.70 nm for full cross validation. Applied to an external dataset of four new samples with about 13,000 spectra per sample the model provides an RMSE of 1.84 nm for prediction and demonstrates the robustness of the model during real-time processing. The results of this study prove the ability and usefulness of the proposed method to estimate the thickness of oxide layers on technical copper. Hence, the application of hyperspectral imaging for the industrial process control of electronic devices is very promising.

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Author of HS ReutlingenStiedl, Jan; Boldrini, Barbara; Rebner, Karsten; Azemtsop, Georgette
Erschienen in:Journal of spectral imaging : JSI
Publisher:IM Publications Open LLP
Place of publication:Chichester
Document Type:Article
Year of Publication:2019
Tag:copper oxide; hyperspectral imaging; multivariate analysis; oxide layer thickness; partial least square regression; prediction; pushbroom imaging; reflectance
Issue:Aufsatz a10
Page Number:12
First Page:1
Last Page:12
DDC classes:570 Biowissenschaften, Biologie
Open Access?:Ja
Licence (German):License Logo  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International