Ignacio Becerril-Romero

This work showcases the importance of developing suitable inspection and analysis methodologies with high statistical relevance data coupled with machine learning algorithms, for the detection, control, and understanding of small fluctuations in the scale‐up of thin film photovoltaics to industrial sizes. To exhibit this methodology, this work investigates the effect of subtle inhomogeneities on the efficiency of thin film solar cells based on the Cu₂ZnSnSe₄/CdS interface using two large area samples subdivided in ≈400 individual solar cells. A large dataset obtained from Raman and photoluminescence spectroscopic techniques together with J–V optoelectronic data is generated to elucidate the impact of these inhomogeneities on the efficiency of the devices. Using a combination of statistical (spectral difference) and over 440 000 multivariate polynomial regressions through machine learning algorithms, it is revealed how the main limiting factor for device performance are subtle fluctuations in the nanostructure and surface defects of the CdS layer, rather than compositional fluctuations or defects in the kesterite absorber. It is estimated that the avoidance of these issues could result in an absolute increase in device efficiency of 2%. This could provide a potential avenue for further technology advancement within the kesterite community.

Thin film photovoltaic (TFPV) materials and devices present a high complexity with multiscale, multilayer, and multielement structures and with complex fabrication procedures. To deal with this complexity, the evaluation of their physicochemical properties is critical for generating a model that proposes strategies for their development and optimization. However, this process is time‐consuming and requires high expertise. In this context, the adoption of combinatorial analysis (CA) and artificial intelligence (AI) strategies represents a powerful asset for accelerating the development of these complex materials and devices. This work introduces a methodology to facilitate the adoption of AI and CA for the development of TFPV technologies. The methodology covers all the necessary steps from the synthesis of samples for CA to data acquisition, AI‐assisted data analysis, and the extraction of relevant information for research acceleration. Each step provides details on the necessary concepts, requirements, and procedures and are illustrated with examples from the literature. Then, the application of the methodology to a complex set of samples from a TFPV production line highlights its ability to rapidly glean significant insights even in intricate scenarios. The proposed methodology can be applied to other types of materials and devices beyond PV and using different characterization techniques.

Accurate anionic control during the formation of chalcogenide solid solutions is fundamental for tuning the physicochemical properties of this class of materials. Compositional grading is the key aspect of band gap engineering and is especially valuable at the device interfaces for an optimum band alignment, for controlling interface defects and recombination and for optimizing the formation of carrier-selective contacts. However, a simple and reliable technique that allows standardizing anionic compositional profiles is currently missing for kesterites and the feasibility of achieving a compositional gradient remains a challenging task. This work aims at addressing these issues by a simple and innovative technique. It basically consists of first preparing a pure sulfide absorber with a specific thickness followed by the synthesis of a pure selenide part of complementary thickness on top of it. Specifically, the technique is applied to the synthesis of Cu₂ZnSn(S,Se)₄ and Cu₂ZnGe(S,Se)₄ kesterite absorbers, and a series of characterizations are performed to understand the anionic redistribution within the absorbers. For identical processing conditions, different Se incorporation dynamics is identified for Sn- and Ge-based kesterites, leading to a homogeneous or graded composition in depth. It is first demonstrated that for Sn-based kesterite the anionic composition can be perfectly controlled through the thicknesses ratio of the sulfide and selenide absorber parts. Then, it is demonstrated that for Ge-based kesterite an anionic (Se–S) gradient is obtained and that by adjusting the processing conditions the composition at the back side can be finely tuned. This technique represents an innovative approach that will help to improve the compositional reproducibility and determine a band gap grading strategy pathway for kesterites. Furthermore, due to its simplicity and reliability, the proposed methodology could be extended to other chalcogenide materials.

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 Al_xO 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)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.

This work explores the thermal stability of antimony‐based photovoltaic (PV) technologies investigating the effect of low‐temperature (50—350 °C) postdeposition annealings (PDAs) on bare Sb₂Se₃ absorbers and complete SLG/Mo/Sb₂Se₃/CdS/i‐ZnO/ITO devices (5.7% power conversion efficiency with no anti‐reflecting coating or metallic grid). A comprehensive structural analysis by means of X‐ray diffraction and Raman spectroscopy, coupled with optoelectronic characterization, reveals clear evidences of a degradation process dominated by selenium diffusion. The degradation process is observed to start at low PDA temperatures as a shrinkage of the Sb₂Se₃ unit cell. Further increasing the PDA temperature above 200 °C leads to the formation of Sb oxides and Se secondary phases in bare absorbers, and of CdS_1—x Se _x in complete devices at the Sb₂Se₃ front interface which completely degrade the heterojunction and kill device performance. Furthermore, a clear correlation is found between PV performance decrease and Sb₂Se₃ layer lattice shrinkage with the increasing PDA temperature (T > 50 °C). This is the first time that thermal instability is reported for the Sb₂Se₃ compound at temperatures commonly used during PV module fabrication processes such as emitter/transparent conducting oxide deposition or encapsulation.

Fabrication on transparent soda-lime glass/FTO substrates opens the way to advanced applications for kesterite solar cells such as semitransparent, bifacial and tandem devices which are key to the future of the PV market. However, the complex behavior of the p-kesterite/n-FTO back interface potentially limits the power conversion efficiency of such devices. Overcoming this issue requires careful interface engineering. This work empirically explores the use of transition metal oxides (TMOs) and Mo-based nanolayers to improve the back interface of Cu₂ZnSnSe₄, Cu₂ZnSnS₄ and Cu₂ZnSn(S,Se)₄ solar cells fabricated on transparent glass/FTO substrates. Although the use of TMOs alone is found to be highly detrimental for the devices inducing complex current blocking behaviors, the use of Mo:Na nanolayers and their combination with the n-type TMOs TiO₂ and V₂O₅ are shown to be a very promising strategy to improve the limited performance of kesterite devices fabricated on transparent substrates. The optoelectronic, morphological, structural and in-depth compositional characterization performed on the devices suggests that the improvements observed are related to a combination of shunt insulation and recombination reduction. This way, record efficiencies of 6.1%, 6.2% and 7.9% are obtained for Cu₂ZnSnSe₄, Cu₂ZnSnS₄ and Cu₂ZnSn(S,Se)₄ devices, respectively, giving proof of the potential of TMOs for the development of kesterite solar cells on transparent substrates.