Modeling and performance optimization of
            <scp>
              two‐terminal Cu
              <sub>2</sub>
              ZnSnS
              <sub>4</sub>
              –silicon
            </scp>
            tandem solar cells

Vallisree, S.; Thangavel, R.; Lenka, Trupti Ranjan

doi:10.1002/er.6540

Cited by 8 publications

(3 citation statements)

References 39 publications

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“…While approximately 90% of solar cells still rely on crystalline Si as the absorber, related group IV semiconductors such as SiC, II–VI semiconductors such as CdTe, III–V semiconductors such as GaAs, and various derivative compounds are all viable as photovoltaic (PV) materials and are currently in use in single terminal as well as tandem solar cells. 5 , 13 , 14 , 15 , 16 , 17 Many of these compounds have also been used in transistors, photodiodes, lasers, and qubits or quantum sensors. The chemical space of binary group IV, III–V, and II–VI semiconductors contains compounds that exist in the cubic zinc blende (ZB) or wurtzite crystal structures and show systematic trends in lattice constants, electronic band gaps, optical absorption coefficients, and defect properties.…”

Section: Introductionmentioning

confidence: 99%

Universal machine learning framework for defect predictions in zinc blende semiconductors

et al. 2022

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Section: Introductionmentioning

confidence: 99%

Universal machine learning framework for defect predictions in zinc blende semiconductors

et al. 2022

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“…13 Similar effects are felt in applications such as transistors, photodiodes, lasers, sensors, and quantum information sciences. [14][15][16][17][18] Canonical group IV, III-V, and II-VI semiconductors are some of the most important materials used in these applications, either as binary compounds or as alloys, typically in the Zinc Blende (ZB) or wurtzite (WZ) phases. In addition to Si and CdTe, compounds such as GaAs, SiC, CdSe, and CdS have been used in photovoltaics (PVs).…”

Section: Introductionmentioning

confidence: 99%

Accelerating defect predictions in semiconductors using graph neural networks

Rahman,

Gollapalli,

Manganaris

et al. 2024

APL Machine Learning

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First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

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“…They obtained an efficiency increase from 13.5% to 28.5%. While V. Sivathanu [ 15 ] simulated the monolithic CZTS/Si tandem using heterojunction silicon with an efficiency of 14.46% from their previous work [ 16 ] using ATLAS 2D simulator, they improved both sub cells achieving a maximum efficiency of 20.17%. Mora-Herrera et al simulated kesterite solar cell enhancement without tandem integration.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Potential of Pure Germanium Kesterite for a 2T Kesterite/Silicon Tandem Solar Cell: A Simulation Study

Rudzikas,

Pakalka,

Donėlienė

et al. 2023

Materials

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Recently, the development of tandem devices has become one of the main strategies for further improving the efficiency of photovoltaic modules. In this regard, combining well-established Si technology with thin film technology is one of the most promising approaches. However, this imposes several limitations on such thin film technology, such as low prices, the absence of scarce or toxic elements, the possibility to tune optical properties and long lifetime stability. Therefore, to show the potential of kesterite/silicon tandems, in this work, a 2 terminal (2T) structure using pure germanium kesterite was simulated with combined SCAPS and transfer matrix methods. To explore the impact of individual modifications, a stepwise approach was adopted to improve the kesterite. For the bottom sub cell, a state-of-the-art silicon PERC cell was used with an efficiency of 24%. As a final result, 19.56% efficiency was obtained for the standalone top kesterite solar cell and 28.6% for the tandem device, exceeding standalone silicon efficiency by 4.6% and justifying a new method for improvement. The improvement observed could be attributed primarily to the enhanced effective lifetime, optimized base doping, and mitigated recombination at both the back and top layers of the CZGSSe absorber. Finally, colorimetric analysis showed that color purity for such tandem structure was low, and hues were limited to the predominant colors, which were reddish, yellowish, and purple in an anti-reflective coating (ARC) thickness range of 20–300 nm. The sensitivity of color variation for the whole ARC thickness range to electrical parameters was minimal: efficiency was obtained ranging from 28.05% to 28.63%.

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Modeling and performance optimization of two‐terminal Cu ₂ ZnSnS ₄ –silicon tandem solar cells

Cited by 8 publications

References 39 publications

Universal machine learning framework for defect predictions in zinc blende semiconductors

Universal machine learning framework for defect predictions in zinc blende semiconductors

Accelerating defect predictions in semiconductors using graph neural networks

Exploring the Potential of Pure Germanium Kesterite for a 2T Kesterite/Silicon Tandem Solar Cell: A Simulation Study

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Modeling and performance optimization of two‐terminal Cu 2 ZnSnS 4 –silicon tandem solar cells

Cited by 8 publications

References 39 publications

Universal machine learning framework for defect predictions in zinc blende semiconductors

Universal machine learning framework for defect predictions in zinc blende semiconductors

Accelerating defect predictions in semiconductors using graph neural networks

Exploring the Potential of Pure Germanium Kesterite for a 2T Kesterite/Silicon Tandem Solar Cell: A Simulation Study

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Product

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Modeling and performance optimization of two‐terminal Cu ₂ ZnSnS ₄ –silicon tandem solar cells