Engineering of Interface and Bulk Properties in Cu2ZnSn(S,Se)4 Thin-Film Solar Cells with Ultrathin CuAlO2 Intermediate Layer and Ge Doping

Recently, kesterite-based absorbers and related compounds have been considered as promising eco-friendly light absorber materials for thin-film solar cells (TFSCs). However, the device performances of kesterite-based TFSCs are limited because of the formation of defects and poor interfacial properties. In this study, we developed a strategic approach to improve the device performances of Cu2ZnSn­(S,Se)4 (CZTSSe) solar cells using back-interface passivation of the absorber layer and further reduced the formation of defects through Ge doping. The application of CuAlO2 (CAO) as an intermediate layer near the back interface efficiently improves the grain growth and minimizes the detrimental Mo­(S,Se)2 thickness. In addition, the Ge nanolayer deposited over the CAO layer improves the absorber bulk quality, effectively suppresses the defect density, and reduces the nonradiative carrier recombination losses. As a result, the short-circuit current density, fill factor, and power conversion efficiency of the champion device with the CAO and Ge nanolayer improved from 31.91 to 36.26 mA/cm2, 0.55 to 0.61, and 8.58 to 11.01%, respectively. This study demonstrates a potential approach to improve the performances of CZTSSe TFSCs using a combination of back-interface passivation and doping

In Search of Disorder Transitions and Defects Within Cu2ZnSn(S,Se)4‐Based Absorber Layers via Temperature‐Dependent Raman Spectroscopy Technique

The temperature-dependent (25-300 degrees C) disorder transitions analyzed via Raman spectroscopic technique for the different non-stoichiometric Cu2ZnSn(S,Se)(4) (CZTSSe) thin films are demonstrated. In the thin films prepared with different Zn conditions, i.e., in Zn-1 (Zn-poor), the density of the A-type defect cluster [Zn-Cu + V-Cu] increases with temperature; however, it slightly decreases and remains constant for Zn-rich samples, i.e., Zn-2 and Zn-3. At the same time, the density of the B-type defect cluster [2Zn(Cu) + Zn-Sn] increases with increasing temperature and Zn content. The observations further reveal that Zn concentration has less impact on V-Cu formation; therefore, above the optimum Cu-poor and Zn-rich conditions, Zn-Cu shallow donors negatively influence the kesterite device performances. Finally, solar cells based on all the CZTSSe thin-film samples (Zn-1, Zn-2, and Zn-3) are fabricated in which a device based on Zn-2 exhibits excellent power conversion efficiency of approximate to 11.0% with open-circuit voltage of 478 mV, short-circuit current of 35.51 mA cm(-2), and fill factor of 64%, respectively

Unraveling the Effect of Compositional Ratios on the Kesterite Thin-Film Solar Cells Using Machine Learning Techniques

In the Kesterite family, the Cu2ZnSn(S,Se)4 (CZTSSe) thin-film solar cells (TFSCs) have demonstrated the highest device efficiency with non-stoichiometric cation composition ratios. These composition ratios have a strong influence on the structural, optical, and electrical properties of the CZTSSe absorber layer. So, in this work, a machine learning (ML) approach is employed to evaluate effect composition ratio on the device parameters of CZTSSe TFSCs. In particular, the bi-metallic ratios like Cu/Sn, Zn/Sn, Cu/Zn, and overall Cu/(Zn+Sn) cation composition ratio are investigated. To achieve this, different machine learning algorithms, such as decision trees (DTs) and classification and regression trees (CARTs), are used. In addition, the output performance parameters of CZTSSe TFSCs are predicted by both continuous and categorical approaches. Artificial neural networks (ANN) and XGBoost (XGB) algorithms are employed for the continuous approach. On the other hand, support vector machine and k-nearest neighbor’s algorithms are also used for the categorical approach. Through the analysis, it is observed that the DT and CART algorithms provided a critical composition range well suited for the fabrication of highly efficient CZTSSe TFSCs, while the XGB and ANN showed better prediction accuracy among the tested algorithms. The present work offers valuable guidance towards the integration of the ML approach with experimental studies in the field of TFSCs