Thickness evaluation of AlOx barrier layers for encapsulation of flexible PV modules in industrial environments by normal reflectance and machine learning. (18th October 2021)

• Record Type: Journal Article
• Authors: Grau‐Luque, Enric; Guc, Maxim; Becerril‐Romero, Ignacio; Izquierdo‐Roca, Víctor; Pérez‐Rodríguez, Alejandro; Bolt, Pieter; Van den Bruele, Fieke; Ruhle, Ulfert
• Is Part Of: Progress in photovoltaics. Volume 30:Number 3(2022)
• Page Start: 229
• Page End: 239
• Publication Date: 2021-10-18
• Subjects: AlOx -- CIGS -- encapsulation -- flexible PV -- machine learning -- normal reflectance -- process monitoring -- thickness assessment; Solar cells -- Periodicals; Photovoltaic cells -- Periodicals; Solar power plants -- Periodicals; 621.31245
• DOI: 10.1002/pip.3478
• Languages: English
• ISSNs: 1062-7995

Abstract

Abstract: Flexible photovoltaic (PV) devices, such as those based on Cu (In, Ga)Se2 (CIGS) and perovskites, use polymeric front sheets for encapsulation that do not provide sufficient protection against the environment. The addition of nanometric Alx 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 AlOx 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 AlOx nanometric layers. In particular, the proposed methodology is applied for precisely determining the thickness AlOx nanolayers deposited on three different substrates relevant for the PV industry: monocrystalline Si, Cu (In, Ga)Se2 multistack 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. Abstract : 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 AlOx nanometric layers for photovoltaic (PV) encapsulation. The proposed methodology demonstrates a sensitivity <10 nm and acquisition times ≤100 ms which makes it compatible with industrial monitoring applications.