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

High-speed, low-bias operated, broadband (Vis-NIR) photodetector based on sputtered Cu2ZnSn(S, Se)(4) (CZTSSe) thin films

Photodetectors have large applications in the current ongoing pandemic. These can be used to study the growth of viruses where depending upon the concentration the light intensity will be reduced. Since the viruses grow very fast therefore a device with very low response time as well as quick recovery will be useful for this study. If the device can be made from the non-toxic materials and sizes are quite small, they can be used for in vitro studies as well. Kesterite Cu2ZnSn(5, Se)(4) (CZTSSe) thin film can be deposited over flexible substrates. The detectivity of even very small area device is very high with ultra-small response and recovery time. The CZTSSe PD exhibited excellent broadband (Vis-NIR) photoresponse, high responsivity of 18.0 mA.W-1, a fast rise time of 82 ms, and a decay time of 97 ms, as well as high detectivity (similar to 10(9) Jones) with favorable self-powered characteristics. This work suggests significant scientific insights for photoconductivity properties of emerging kesterite CZTSSe thin-film materials for broadband, low-cost, high-efficiency next-generation thin-film photodetectors for various optoelectronic applications including diagnostic

Nanoscale Rear-Interface Passivation in Cu2ZnSn(S,Se)4 Solar Cells through the CuAlO2 Intermediate Layer

The present work demonstrates that the addition of p-type CuAlO2 (CAO) as an intermediate layer between molybdenum (Mo) and the absorber rear interface efficiently improves the Cu2ZnSn(S,Se)4 (CZTSSe) device performance. The efficacy of the intermediate layer is analyzed through sputtering the CAO nanolayer at different deposition times on top of the Mo layer. The addition of an ultrathin CAO nanolayer improved the absorber bulk quality with the formation of compact and larger crystalline grains. Furthermore, the CZTSSe device with an optimum deposition time (154 s) of the CAO nanolayer successfully reduced the Mo(S,Se)2 layer thickness from ∼50 to ∼25 nm. This reduced Mo(S,Se)2 layer thickness results in the reduced series resistance (Rs) in the device providing improvement in the overall device performance. The short-circuit current density (JSC) and the power conversion efficiency of the device with the CAO nanolayer increased from 33.48 to 35.40 mA/cm2 and from 9.61 to 10.54%, respectively, compared to a reference device. © 2021 American Chemical Society.1

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

Machine Learning Aided Optimization of P1 Laser Scribing Process on Indium Tin Oxide Substrates

Present study employes a picosecond laser (532 nm) for selective P1 laser scribing on the indium tin oxide (ITO) layer and subsequent fine‐tuning of P1 scribing conditions with machine learning (ML) techniques. Initially, the scribing is performed by varying different laser parameters and further evaluate them via an optical microscope and two probe resistivity measurements. The corresponding scribing width and sheet resistance data are used as input databases for ML analysis. The classification and regression tree (CART)‐based ML analysis revealed that median pulse energy 5.7 μJ, APL > 35%, LSO > 46%, and processing speed ≥1250 mm s−1 gives ≥16 μm of scribing width. Further, the decision tree (DT) analysis showed that pulse energy of ≥8.1 μJ, and LSO ≥ 37% are required for electrically isolated lines. The feature importance score suggests that laser fluence and pulse energy determined the scribing width, whereas electrical isolation strongly depends on LSO and processing speed. Finally, the ML achieved conditions experimentally validated and reassessed via scanning electron microscope, and atomic force microscopy aligns well with optical microscope measurements