Knoblich Mona, Al Ktash Mohammad, Wackenhut Frank, Englert Tim, Stiedl Jan, Wittel Hilmar, Green Simon, Jacob Timo, Boldrini Barbara, Ostertag Edwin, Rebner Karsten, Brecht Marc
Center of Process Analysis and Technology (PA&T), School of Life Sciences, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany.
Institute of Physical and Theoretical Chemistry, Eberhard Karls University Tübingen, Auf der Morgenstelle 18, 72076 Tübingen, Germany.
Sensors (Basel). 2024 Jul 19;24(14):4680. doi: 10.3390/s24144680.
In the manufacturing process of electrical devices, ensuring the cleanliness of technical surfaces, such as direct bonded copper substrates, is crucial. An in-line monitoring system for quality checking must provide sufficiently resolved lateral data in a short time. UV hyperspectral imaging is a promising in-line method for rapid, contactless, and large-scale detection of contamination; thus, UV hyperspectral imaging (225-400 nm) was utilized to characterize the cleanliness of direct bonded copper in a non-destructive way. In total, 11 levels of cleanliness were prepared, and a total of 44 samples were measured to develop multivariate models for characterizing and predicting the cleanliness levels. The setup included a pushbroom imager, a deuterium lamp, and a conveyor belt for laterally resolved measurements of copper surfaces. A principal component analysis (PCA) model effectively differentiated among the sample types based on the first two principal components with approximately 100.0% explained variance. A partial least squares regression (PLS-R) model to determine the optimal sonication time showed reliable performance, with = 0.928 and RMSECV = 0.849. This model was able to predict the cleanliness of each pixel in a testing sample set, exemplifying a step in the manufacturing process of direct bonded copper substrates. Combined with multivariate data modeling, the in-line UV prototype system demonstrates a significant potential for further advancement towards its application in real-world, large-scale processes.
在电气设备的制造过程中，确保技术表面（如直接键合铜基板）的清洁至关重要。用于质量检查的在线监测系统必须在短时间内提供足够分辨率的横向数据。紫外高光谱成像技术是一种很有前景的在线方法，可用于快速、非接触式和大规模的污染检测；因此，利用紫外高光谱成像（225 - 400 nm）以无损方式表征直接键合铜的清洁度。总共准备了11个清洁度等级，并对44个样品进行了测量，以建立用于表征和预测清洁度等级的多变量模型。该装置包括一个推扫式成像仪、一个氘灯和一个用于对铜表面进行横向分辨率测量的传送带。主成分分析（PCA）模型基于前两个主成分有效地区分了样品类型，解释方差约为100.0%。用于确定最佳超声处理时间的偏最小二乘回归（PLS - R）模型表现出可靠的性能，R² = 0.928，RMSECV = 0.849。该模型能够预测测试样品集中每个像素的清洁度，这是直接键合铜基板制造过程中的一个实例步骤。结合多变量数据建模，在线紫外原型系统在实际大规模生产过程中的应用方面显示出进一步发展的巨大潜力。
