Open Access

The influence of turbidity on the Raman signal strengths of condensed matter is theoretically analyzed and measured with laboratory - scale equipment for remote sensing. The results show the quantitative dependence of back- and forward-scattered signals on the thickness and elastic-scattering properties of matter. In the extreme situation of thin, highly turbid layers, the measured Raman signal strengths exceed their transparent analogs by more than a factor of ten. The opposite behavior is found for thick layers of low turbidity, where the presence of a small amount of scatterers leads to a decrease of the measured signal. The wide range of turbidities appearing in nature is experimentally realized with stacked polymer layers and solid/liquid dispersions, and theoretically modeled by the equation of radiative transfer using the analytical diffusion approximation or random walk simulations.

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.