Combinatorial and machine learning approaches for the analysis of Cu<sub>2</sub>ZnGeSe<sub>4</sub>: influence of the off-stoichiometry on defect formation and solar cell performance

Grau-Luque, Enric; Anefnaf, Ikram; Benhaddou, Nada; Fonoll‐Rubio, Robert; Becerril‐Romero, Ignacio; Aazou, Safae; Saucedo, Edgardo; Sekkat, Zouheir; Pérez-Rodrı́guez, A.; Izquierdo-Roca, Víctor; Guc, Maxim

doi:10.1039/d1ta01299a

Cited by 14 publications

(31 citation statements)

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“…[ 125 ] Recently, a combinatorial Cu 2 ZnGeSe 4 sample made by sequential sputtering process with varying [Zn]/[Ge] composition was also studied by Raman spectroscopy, XRF, and J – V curves analysis. [ 69 ] The correlation approach in the data analysis performed in this work has allowed to established the off‐stoichiometric limits of formation of the pure CZGSe phase. In addition, V Cu point defects inside the CZGSe structure extracted from Raman spectra have a strong influence on the final device efficiency (see Figure 4 right).…”

Section: Combinatorial Analysis Of Chalcogenide‐based Thin‐film Pv Te...mentioning

confidence: 99%

Section: Combinatorial Analysis Of Chalcogenide‐based Thin‐film Pv Te...mentioning

confidence: 99%

“…[72,78,96,101] Combination of the analysis of different techniques with optoelectronic data provides valuable information about the impact of the variation of the selected layer property on the performance of the devices, determining the optimal range of the graded property. [16,69,72,77,94] In this context, the last hint in the requirements of selection of measurement techniques with a macrospot while measuring a gradient library becomes more evident. Indeed, to perform reliable optoelectronic characterization, the combinatorial gradient library sample should be divided into separated unit cells, which in the case of thin-film PV technologies is usually made by mechanical or laser scribing.…”

Section: Characterization Techniques For Combinatorial Analysismentioning

confidence: 99%

“…This was also proved by combining the results of the correlations analysis with the results obtained using ML methodology (Figure 5), showing the high potential in applying this computational approach to the analysis of combinatorial samples. [69] Finally, Zakytaev et al also studied combinatorial CZTSe and CZTS solar cells synthesized with a graded thickness of the buffer layer, using either conventional CdS buffer layers and alternative In 2 S 3 buffer layers, respectively. [78,84] The CdS layer was deposited by CBD leading to a discrete library, and the In 2 S 3 layer was deposited by sputtering leading to a gradient library.…”

Section: Kesterite Familymentioning

confidence: 99%

“…Reproduced with permission. [69] Copyright 2021, Royal Society of Chemistry. et al also studied the solid solution of (Cd,Zn)Te synthesized by combinatorial metalorganic CVD in order to evaluate the possibility to apply these layers as top cell absorbers in tandem devices.…”

Section: Cdte Familymentioning

confidence: 99%

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Combinatorial Analysis Methodologies for Accelerated Research: The Case of Chalcogenide Thin‐Film Photovoltaic Technologies

et al. 2022

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One of the fastest ways for the discovery, understanding, development, and further optimization of new complex materials is the application of combinatorial analysis methodologies, which have already shown impressive results for different organic and inorganic materials, leading to the fast development of different scientific fields and industrial applications. However, in the case of thinfilm materials for optoelectronic devices and, in particular, for second-generation photovoltaic (PV) devices, the application of combinatorial analysis is still quite uncommon with a desultory rather than systematic application. The present review discusses the main constraints for the application of combinatorial analysis to thin-film materials with a focus on chalcogenide compounds and different strategies to overcome them. Special attention is paid to the requirements for the preparation of graded thin films, characterization, and analysis of the results, providing different hints for the implementation of high-quality combinatorial analysis. Finally, an overview of the currently published results in the field of chalcogenide thin-film PV technologies is presented, showing the relevance of the combinatorial approach for boosting the development not only of this promising PV technology, but also of other optoelectronic devices based on complex materials and multilayered structures.

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Section: Combinatorial Analysis Of Chalcogenide‐based Thin‐film Pv Te...mentioning

confidence: 99%

Section: Combinatorial Analysis Of Chalcogenide‐based Thin‐film Pv Te...mentioning

confidence: 99%

Section: Characterization Techniques For Combinatorial Analysismentioning

confidence: 99%

Section: Kesterite Familymentioning

confidence: 99%

Section: Cdte Familymentioning

confidence: 99%

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Combinatorial Analysis Methodologies for Accelerated Research: The Case of Chalcogenide Thin‐Film Photovoltaic Technologies

et al. 2022

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Machine Learning Assisted Analysis, Prediction, and Fabrication of High‐Efficiency CZTSSe Thin Film Solar Cells

Karade

Sutar

Shin

et al. 2023

Adv Funct Materials

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The Earth‐abundant element‐based Cu2ZnSn(S,Se)4 (CZTSSe) absorber is considered as a promising material for thin‐film solar cells (TFSCs). The current record power conversion efficiency (PCE) of CZTSSe TFSCs is ≈13%, and it's still lower than CdTe and CIGS‐based TFSCs. A further breakthrough in its PCE mainly relies on deep insights into the various device fabrication conditions; accordingly, the experimental–oriented machine learning (ML) approach can be an effective way to discover key governing factors in improving PCE. The present work aims to identify the key governing factors throughout the device fabrication processes and apply them to break the saturated PCE for CZTSSe TFSCs. For realization, over 25,000 data points were broadly collected by fabricating more than 1300 CZTSSe TFSC devices and analyzed them using various ML techniques. Through extensive ML analysis, the i‐ZnO thickness is found to be the first, while Zn/Sn compositional ratio and sulfo‐selenization temperature are other key governing factors under thin or thick i‐ZnO thickness to achieve over 11% PCE. Based on these key governing factors, the applied random forest ML prediction model for PCE showed Adj. R2 = >0.96. Finally, the best‐predicted ML conditions considered for experimental validation showed well‐matched experimental outcomes with different ML models.

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Passivating Sn_Zn Defect and Optimizing Energy Level Alignment via Organic Silicon Salt Incorporation toward Efficient Solution‐Processed CZTSSe Solar Cells

Qi,

Wei,

et al. 2023

Adv Funct Materials

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The SnZn defect passivation and heterojunction interface energetics‐modification are crucial for further improvement of open‐circuit voltage (Voc) and efficiency of Cu2ZnSn(S,Se)4 (CZTSSe) solar cells. Herein, a simple but effective cation substitution strategy is reported to promote the performance of CZTSSe devices by simultaneously modifying the absorber and heterojunction interface. This is achieved by introducing the organic silicon salt C8H20O4Si into CZTSSe precursor solution as a source of silicon to partially substitute Sn with Si. Systematic studies reveal that there are two mechanisms synergistically contributing to the increase of efficiency: 1) This strategy can passivate the undesired SnZn defect in CZTSSe absorber more efficiently, leading to at least one order of magnitude lower SnZn defect density and beneficial carrier transport properties; 2) After Si incorporation, the conduction band minimum of the CZTSSe is upshifted, thus enlarging the bandgap, which ultimately optimizes the energy level structure at the CdS/CZTSSe interface and reduces interfacial energy loss by decreasing the carrier transport barrier. Consequently, the CZTSSe device with 7% Si substitution delivers a satisfactory efficiency of 13.02% owing to an increase in Voc. It is hoped the Si substitution strategy reported here can provide inspiration for developing advanced CZTSSe devices with broad application prospects.

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Combinatorial and machine learning approaches for the analysis of Cu₂ZnGeSe₄: influence of the off-stoichiometry on defect formation and solar cell performance

Abstract: This work provides insights for understanding and further developing the Cu2ZnGeSe4 photovoltaic technology, and gives an example of the potential of combinatorial analysis and machine learning for the study of complex systems in materials research.

Cited by 14 publications

References 59 publications

Combinatorial Analysis Methodologies for Accelerated Research: The Case of Chalcogenide Thin‐Film Photovoltaic Technologies

Combinatorial Analysis Methodologies for Accelerated Research: The Case of Chalcogenide Thin‐Film Photovoltaic Technologies

Machine Learning Assisted Analysis, Prediction, and Fabrication of High‐Efficiency CZTSSe Thin Film Solar Cells

Passivating Sn_Zn Defect and Optimizing Energy Level Alignment via Organic Silicon Salt Incorporation toward Efficient Solution‐Processed CZTSSe Solar Cells

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Combinatorial and machine learning approaches for the analysis of Cu2ZnGeSe4: influence of the off-stoichiometry on defect formation and solar cell performance

Abstract: This work provides insights for understanding and further developing the Cu2ZnGeSe4 photovoltaic technology, and gives an example of the potential of combinatorial analysis and machine learning for the study of complex systems in materials research.

Cited by 14 publications

References 59 publications

Combinatorial Analysis Methodologies for Accelerated Research: The Case of Chalcogenide Thin‐Film Photovoltaic Technologies

Combinatorial Analysis Methodologies for Accelerated Research: The Case of Chalcogenide Thin‐Film Photovoltaic Technologies

Machine Learning Assisted Analysis, Prediction, and Fabrication of High‐Efficiency CZTSSe Thin Film Solar Cells

Passivating SnZn Defect and Optimizing Energy Level Alignment via Organic Silicon Salt Incorporation toward Efficient Solution‐Processed CZTSSe Solar Cells

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Product

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Combinatorial and machine learning approaches for the analysis of Cu₂ZnGeSe₄: influence of the off-stoichiometry on defect formation and solar cell performance

Passivating Sn_Zn Defect and Optimizing Energy Level Alignment via Organic Silicon Salt Incorporation toward Efficient Solution‐Processed CZTSSe Solar Cells