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Rapid Detection of Cleanliness on Direct Bonded Copper Substrate by Using UV Hyperspectral Imaging
Mona Knoblich1,2, Mohammad Al Ktash1,2, Frank Wackenhut1
1Center of Process Analysis and Technology (PA&T), School of Life Sciences, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany.
Sensors (Basel, Switzerland)
|July 27, 2024
Summary
UV hyperspectral imaging rapidly assesses direct bonded copper cleanliness for electrical device manufacturing. This non-destructive method accurately predicts surface contamination levels, optimizing production quality control.
Area of Science:
- Materials Science
- Analytical Chemistry
- Manufacturing Engineering
Background:
- Ensuring technical surface cleanliness, like direct bonded copper (DBC) substrates, is vital in electrical device manufacturing.
- In-line quality monitoring requires rapid, laterally resolved data for effective contamination detection.
- UV hyperspectral imaging offers a promising contactless and large-scale solution for real-time surface analysis.
Purpose of the Study:
- To develop and validate an in-line UV hyperspectral imaging system for non-destructive characterization of DBC substrate cleanliness.
- To establish multivariate models for differentiating and predicting various cleanliness levels on DBC surfaces.
- To demonstrate the system's capability in optimizing manufacturing processes, such as determining sonication times.
Main Methods:
- Utilized UV hyperspectral imaging (225-400 nm) with a pushbroom imager, deuterium lamp, and conveyor belt for in-line measurements.
- Prepared 11 distinct cleanliness levels across 44 DBC samples for model development.
- Applied Principal Component Analysis (PCA) for sample differentiation and Partial Least Squares Regression (PLS-R) for quantitative prediction.
Main Results:
- PCA models achieved 100.0% explained variance, effectively distinguishing between different cleanliness levels.
- A PLS-R model demonstrated high reliability for predicting optimal sonication time (R^2_cv = 0.928, RMSECV = 0.849).
- The system successfully predicted pixel-level cleanliness on test samples, showcasing its potential for process control.
Conclusions:
- In-line UV hyperspectral imaging, coupled with multivariate data analysis, is a powerful tool for assessing DBC substrate cleanliness.
- The developed prototype system shows significant potential for real-world, large-scale application in electrical device manufacturing.
- This technology enables rapid, non-destructive quality control, contributing to improved manufacturing efficiency and product reliability.
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