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 distri...
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| 格式: | 文件 |
| 语言: | English |
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IM Publications Open
2019-06-01
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| 丛编: | Journal of Spectral Imaging |
| 主题: | |
| 在线阅读: | https://www.impopen.com/download.php?code=I08_a10 |
| _version_ | 1828399413567946752 |
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| author | Jan Stiedl Georgette Azemtsop M. Barbara Boldrini Simon Green Thomas Chassé Karsten Rebner |
| author_facet | Jan Stiedl Georgette Azemtsop M. Barbara Boldrini Simon Green Thomas Chassé Karsten Rebner |
| author_sort | Jan Stiedl |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T09:16:55Z |
| format | Article |
| id | doaj.art-1fec9bb5d0ea4d8db7469edd8845754f |
| institution | Directory Open Access Journal |
| issn | 2040-4565 2040-4565 |
| language | English |
| last_indexed | 2024-12-10T09:16:55Z |
| publishDate | 2019-06-01 |
| publisher | IM Publications Open |
| record_format | Article |
| series | Journal of Spectral Imaging |
| spelling | doaj.art-1fec9bb5d0ea4d8db7469edd8845754f2022-12-22T01:54:50ZengIM Publications OpenJournal of Spectral Imaging2040-45652040-45652019-06-0181a1010.1255/jsi.2019.a10Characterisation of oxide layers on technical copper based on visible hyperspectral imagingJan Stiedl0Georgette Azemtsop M.1Barbara Boldrini2Simon Green3Thomas Chassé4Karsten Rebner5University of Tuebingen, Institute of Physical and Theoretical Chemistry, Auf der Morgenstelle 18, 72076 Tuebingen, Germany; Reutlingen University, Process Analysis & Technology, Alteburgstrasse 150, 72762 Reutlingen, Germany; Robert Bosch GmbH, Automotive Electronics, Postfach 1342, 72703 Reutlingen, GermanyReutlingen University, Process Analysis & Technology, Alteburgstrasse 150, 72762 Reutlingen, GermanyReutlingen University, Process Analysis & Technology, Alteburgstrasse 150, 72762 Reutlingen, GermanyRobert Bosch GmbH, Automotive Electronics, Postfach 1342, 72703 Reutlingen, GermanyCenter for Light-Matter Interaction, Sensors & Analytics (LISA+), Auf der Morgenstelle 15, 72076 Tuebingen, GermanyReutlingen University, Process Analysis & Technology, Alteburgstrasse 150, 72762 Reutlingen, GermanyThe 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.https://www.impopen.com/download.php?code=I08_a10hyperspectral imagingpushbroom imagingcopper oxideoxide layer thicknessmultivariate analysispartial least square regressionpredictionreflectance |
| spellingShingle | Jan Stiedl Georgette Azemtsop M. Barbara Boldrini Simon Green Thomas Chassé Karsten Rebner Characterisation of oxide layers on technical copper based on visible hyperspectral imaging Journal of Spectral Imaging hyperspectral imaging pushbroom imaging copper oxide oxide layer thickness multivariate analysis partial least square regression prediction reflectance |
| title | Characterisation of oxide layers on technical copper based on visible hyperspectral imaging |
| title_full | Characterisation of oxide layers on technical copper based on visible hyperspectral imaging |
| title_fullStr | Characterisation of oxide layers on technical copper based on visible hyperspectral imaging |
| title_full_unstemmed | Characterisation of oxide layers on technical copper based on visible hyperspectral imaging |
| title_short | Characterisation of oxide layers on technical copper based on visible hyperspectral imaging |
| title_sort | characterisation of oxide layers on technical copper based on visible hyperspectral imaging |
| topic | hyperspectral imaging pushbroom imaging copper oxide oxide layer thickness multivariate analysis partial least square regression prediction reflectance |
| url | https://www.impopen.com/download.php?code=I08_a10 |
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