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Numerous researchers have dedicated efforts toward enhancing the efficiency of solar cells, particularly through the utilization of multi-junction or tandem solar cell configurations. However, a common approach employed by many researchers involves the use of the standard absorption formula (SAF) to determine the transmitted spectrum from the top cell to illuminate the bottom cells. The SAF method relies on conventional absorption calculations, neglecting reflection, refraction, and parasitic absorption losses. In this study, the transfer matrix (TRM) method, which accounts for reflection and refraction losses and an interference effect, is reported for the accurate calculation of a filtered spectrum. A comparative analysis between the SAF and TRM approaches reveal that the TRM technique provides a more accurate representation of the transmitted spectrum, particularly when considering reflection, refraction, and parasitic absorption losses. The primary aim of this research is to precisely predict the efficiency of tandem configurations by integrating multiple low-bandgap semiconductor bottom cells (BCs) with a top cell (TC) based on high-bandgap perovskite (PVK). Furthermore, the optimization of current matching is achieved by adjusting the thicknesses of the top absorber layer (TAL) and bottom absorber layer (BAL) based on the obtained filtered spectra. Tandem devices optimized using the TRM approach exhibit superior performance, achieving efficiencies of 28.72% (PVK/c-Si), 27.88% (PVK/CIGS), and 29.99% (PVK/PVK). This comparative investigation underscores the importance of considering reflection and refraction losses in tandem solar cell design and highlights the effectiveness of the TRM technique in enhancing device performance. [ABSTRACT FROM AUTHOR]

Conventional perovskite-based solar cells (PSCs) have emerged as promising candidates for next-generation solar energy due to their remarkable features, including a high absorption coefficient, tunable bandgaps, high mobility, low maintenance cost, and high power conversion efficiency (PCE). However, the major bottleneck in commercialization of conventional PSCs is their poor stability (of few days), and toxicity concerns (due to lead content). To address these challenges cesium-based perovskites are widely adopted by researchers. However, detailed understanding of these devices considering several device parameters and their connection with overall PCE is not comprehensively disclosed in previous findings. Therefore, in this study, the PV performance of six (6) different PSCs with Cs-based absorber layer (CAL) viz. CsPbI 3 , CsPbBr 3 , CsSnCl 3 , CsSnI 3 , Cs 2 AgBiBr 6 and CsSn 0.5 Ge 0.5 I 3 has been investigated through SCAPS simulator, followed by developing few machine learning models to forecast the efficiency. Total 2160 dataset has been obtained by varying the absorber layer, thickness, and doping and defect density for training and testing the five different machine learning algorithms such as linear regression (LR), support vector regression (SVR), neural network (NN), random forest (RF), and XGBoost (XGB). The XGB algorithm outperforms other approaches, achieving an impressive R2 of 99.99 % and low MSE of 0.0006. Impact of each input variable on the efficiency is also obtained by generating SHAP plot for each model which revealed that absorber layer and it thickness variation greatly affected the PCE and least impact of doping is observed on PCE. Among all the absorbers, CsPbI 3 shows promising performance by delivering a maximum PCE of 14.00 %. Results reported in this work along with developed ML models may pave the way in the development of Cs based PSCs without the need of complex device simulations. • Investigated six different PSCs with Cs-based materials CsPbI 3 , CsPbBr 3 , CsSnCl 3 , CsSnI 3 , Cs 2 AgBiBr 6 and CsSn 0.5 Ge 0.5 I 3. • Five different supervised machine learning models are employed on 2160 datasets using Python. • SHAP plots are generated to visualize the impact of each feature on the conversion efficiency. • XGB outperformed other algorithms, achieving an impressive R2 value of 99.99 % and a low MSE of 0.0006. [ABSTRACT FROM AUTHOR]

The wide photon absorption range of tandem solar cells (TSCs) allows them to deliver higher efficiencies than single-junction solar cells. The top cell with a wide bandgap absorbs the higher energy photons, while the bottom cell with a relatively low bandgap absorbs the filtered lower energy photons. However, for cheap, efficient, and long-lasting solar cells, the design must incorporate the top and bottom cell absorber layers with an appropriate band gap. In this context, this article proposes all-perovskite tandem solar cells with suitable perovskite partners as active materials in both the upper (band gap 1.68 eV) and the lower (band gap 1.16 eV) sub-cell. This research has retrieved the calibrated top cell from a previous publication, and the bottom cell has been designed, calibrated, and optimised. The filtered spectrum of the upper cell is evaluated at different perovskite thicknesses and fed to the lower cell to set up a tandem configuration. Subsequently, the current- matching technique has been used to study the tandem short-circuit current density (J SC) at different thicknesses of both sub-cells, followed by current density-voltage (J-V) curve. A total of ten tandem J-V curves are constructed and corresponding PV parameters are obtained to obtain the optimized efficiency of 33.8% with an open circuit voltage (V OC) of 2.15 V, a fill factor (FF) of 78.87% and a J SC of 20 mA.cm−2. The reported results will open the door for future development of highly efficient and low-cost monolithic two-terminal tandem devices. [ABSTRACT FROM AUTHOR]