ArticleSensors (Basel, Switzerland)2021
UV Hyperspectral Imaging as Process Analytical Tool for the Characterization of Oxide Layers and Copper States on Direct Bonded Copper.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A Low-Measurement-Cost-Based Multi-Strategy Hyperspectral Image Classification Scheme.Sensors (Basel, Switzerland) · 2024Article
- Rapid Detection of Cleanliness on Direct Bonded Copper Substrate by Using UV Hyperspectral Imaging.Sensors (Basel, Switzerland) · 2024Article
- Advanced Hyperspectral Image Analysis: Superpixelwise Multiscale Adaptive T-HOSVD for 3D Feature Extraction.Sensors (Basel, Switzerland) · 2024Article
- Prediction of Honeydew Contaminations on Cotton Samples by In-Line UV Hyperspectral Imaging.Sensors (Basel, Switzerland) · 2022Article
- Quantitative detection of corrosion minerals in carbon steel using shortwave infrared hyperspectral imaging.RSC advances · 2022Article
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Authors and funding
11 authors.
Funding
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Abstract
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
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