Thickness evaluation of AlOx barrier layers for encapsulation of flexible PV modules in industrial environments by normal reflectance and machine learning

Flexible photovoltaic (PV) devices, such as those based on CIGS and perovskites, use polymeric front sheets for encapsulation that do not provide sufficient protection against the environment. The addition of nanometric AlO_x layers by spatial atomic layer deposition (S-ALD) to these polymeric materials can highly improve environmental protection due to their low water vapor transmission rate and is a suitable solution to be applied in roll-to-roll industrial production lines. A precise control of the thickness of the AlO_x layers is crucial to ensure an effective water barrier performance. However, current thickness evaluation methods of such nanometric layers are costly and complex to incorporate in industrial environments. In this context, the present work describes and demonstrates a novel characterization methodology based on normal reflectance measurements and either on control parameter-based calibration curves or machine learning algorithms that enables a precise, low-cost and scalable assessment of the thickness of AlO_x nanometric layers. In particular, the proposed methodology is applied for precisely determining the thickness AlO_x nanolayers deposited on three different substrates relevant for the PV industry: monocrystalline Si, Cu(In,Ga)Se₂ multi-stack flexible modules and polyethylene terephthalate (PET) flexible encapsulation foil. The proposed methodology demonstrates a sensitivity < 10 nm and acquisition times ≤ 100 ms which makes it compatible with industrial monitoring applications. Additionally, a specific design for in-line integration of a normal reflectance system into a roll-to-roll production line for thickness control of nanometric layers is defined and proposed.

Final version of this work have been published in Progress in Photovoltaics 30 (2022) 229-239. https://doi.org/10.1002/pip.3478