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

• Published in: Progress in photovoltaics 2022-03, Vol.30 (3), p.229-239
• Main Authors: Grau‐Luque, Enric, Guc, Maxim, Becerril‐Romero, Ignacio, Izquierdo‐Roca, Víctor, Pérez‐Rodríguez, Alejandro, Bolt, Pieter, Van den Bruele, Fieke, Ruhle, Ulfert
• Format: Article
• Language: English
• ISSN: 1062-7995; 1099-159X
• DOI: 10.1002/pip.3478

Flexible photovoltaic (PV) devices, such as those based on Cu (In,Ga)Se 2 (CIGS) and perovskites, use polymeric front sheets for encapsulation that do not provide sufficient protection against the environment. The addition of nanometric Al x O 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 enable 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)Se 2 multistack flexible modules, and polyethylene terephthalate (PET) flexible encapsulation foil. The proposed methodology demonstrates a sensitivity