TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Englert, Tim
A1  - Gruber, Florian
A1  - Stiedl, Jan
A1  - Green, Simon
A1  - Jacob, Timo
A1  - Rebner, Karsten
A1  - Grählert, Wulf
T1  - Use of hyperspectral imaging for the quantification of organic contaminants on copper surfaces for electronic applications
JF  - Sensors
N2  - To correctly assess the cleanliness of technical surfaces in a production process, corresponding online monitoring systems must provide sufficient data. A promising method for fast, large-area, and non-contact monitoring is hyperspectral imaging (HSI), which was used in this paper for the detection and quantification of organic surface contaminations. Depending on the cleaning parameter constellation, different levels of organic residues remained on the surface. Afterwards, the cleanliness was determined by the carbon content in the atom percent on the sample surfaces, characterized by XPS and AES. The HSI data and the XPS measurements were correlated, using machine learning methods, to generate a predictive model for the carbon content of the surface. The regression algorithms elastic net, random forest regression, and support vector machine regression were used. Overall, the developed method was able to quantify organic contaminations on technical surfaces. The best regression model found was a random forest model, which achieved an R2 of 0.7 and an RMSE of 7.65 At.-% C. Due to the easy-to-use measurement and the fast evaluation by machine learning, the method seems suitable for an online monitoring system. However, the results also show that further experiments are necessary to improve the quality of the prediction models.
KW  - spectral imaging
KW  - HSI
KW  - XPS
KW  - AES
KW  - multivariate analysis
KW  - machine learning
KW  - elastic net
KW  - cleanliness
KW  - cleaning after soldering
KW  - organic residues
KW  - RF
KW  - SVM
Y1  - 2021
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-32201
SN  - 1424-8220
SS  - 1424-8220
U6  - https://doi.org/10.3390/s21165595
DO  - https://doi.org/10.3390/s21165595
VL  - 21
IS  - 16
SP  - 14
S1  - 14
PB  - MDPI
CY  - Basel
ER  -