Finding Relevant Parameters for the Thin‐film Photovoltaic Cells Production Process with the Application of Data Mining Methods

Ulaczyk, J.; Morawiec, Krzysztof; Zabierowski, P.; Drobiazg, Tomasz; Barreau, Nicolas

doi:10.1002/minf.201600161

Cited by 6 publications

(2 citation statements)

References 14 publications

(13 reference statements)

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“…ML-based methods are already used at the PV system level, for example for fault detection 23,24 or to identify cracks in modules using luminescence imaging techniques [25][26][27][28] . ML has also been used in non-Si applications to find relevant material parameters for fabrication of CIGS solar cells 29 , multijunction solar cells 30 , organic solar cells 31 , or perovskite solar cells 32 . More recently, Kurchin et al 33 proposed a Bayesian-based model to predict the probability distribution of the defect parameters from temperature dependent current-voltage measurements.…”

Section: Introductionmentioning

confidence: 99%

Extracting bulk defect parameters in silicon wafers using machine learning models

et al. 2020

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The performance of high-efficiency silicon solar cells is limited by the presence of bulk defects. Identification of these defects has the potential to improve cell performance and reliability. The impact of bulk defects on minority carrier lifetime is commonly measured using temperature- and injection-dependent lifetime spectroscopy and the defect parameters, such as its energy level and capture cross-section ratio, are usually extracted by fitting the Shockley-Read-Hall equation. We propose an alternative extraction approach by using machine learning trained on more than a million simulated lifetime curves, achieving coefficient of determinations between the true and predicted values of the defect parameters above 99%. In particular, random forest regressors, show that defect energy levels can be predicted with a high precision of ±0.02 eV, 87% of the time. The traditional approach of fitting to the Shockley-Read-Hall equation usually yields two sets of defect parameters, one in each half bandgap. The machine learning model is trained to predict the half bandgap location of the energy level, and successfully overcome the traditional approach’s limitation. The proposed approach is validated using experimental measurements, where the machine learning predicts defect energy level and capture cross-section ratio within the uncertainty range of the traditional fitting method. The successful application of machine learning in the context of bulk defect parameter extraction paves the way to more complex data-driven physical models which have the potential to overcome the limitation of traditional approaches and can be applied to other materials such as perovskite and thin film.

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Section: Introductionmentioning

confidence: 99%

Extracting bulk defect parameters in silicon wafers using machine learning models

et al. 2020

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“…However, PCA identified a subpopulation of cells that were indifferent to the addition of MoO 3 . Finally, Ulaczyk et al 18 used PCA to cluster thin-film photovoltaic (PV) cells.…”

Section: ■ Introductionmentioning

confidence: 99%

Visualization of Solar Cell Library Space by Dimensionality Reduction Methods

Kaspi

Yosipof

Senderowitz

2018

J. Chem. Inf. Model.

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Visualizing high-dimensional data by projecting them into a two-or three-dimensional space is a popular approach in many scientific fields, including computer-aided drug design and cheminformatics. In contrast, dimensionality reduction techniques have been far less explored for materials informatics. Nevertheless, similar to their usefulness in analyzing the space of, e.g., drug-like molecules, such techniques could provide useful insights on materials space, including an intuitive grasp of the overall distribution of samples, the identification of interesting trends, including the formation of materials clusters and the presence of activity cliffs and outliers, and rational navigation through this space in the search for new materials. Here we present the first application of four dimensionality reduction techniques, namely, principal component analysis (PCA), kernel PCA, Isomap, and diffusion map, to visualize and analyze a part of the materials space populated by solar cells made of metal oxides. Solar cells in general and metal-oxide-based solar cells in particular hold the promise of contributing to the world's search for clean and affordable energy resources. With the exception of PCA, these methods have seldom been used to visualize chemistry space and almost never been used to visualize materials space. For this purpose, we integrated five metal-oxide-based solar cell libraries into a uniform database and subjected it to dimensionality reduction by all four methods, comparing their performances using various criteria such as maintaining the local environment of samples and the clustering structure in the lowdimensional space. We also looked at the number of outliers produced by each method and analyzed common outliers. We found that PCA performs best in terms of the ability to correctly maintain the local environment of samples, whereas Isomap does the best job of assigning class membership on the basis of the identities of nearest neighbors (i.e., it is the best classifier). We also found that many of the outliers identified by all of the methods could be rationalized. We suggest that the methods used in this work could be extended to study other types of solar cells, thereby setting the ground for further analysis of the photovoltaic (PV) space as well as other regions of materials space.

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Machine Learning (ML)‐Assisted Design and Fabrication for Solar Cells

Fan

Peng

Zuo

et al. 2019

Energy & Environ Materials

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Photovoltaic (PV) technologies have attracted great interest due to their capability of generating electricity directly from sunlight. Machine learning (ML) is a technique for computer to learn how to perform a specific task using known data. It can be used in many areas and has become a hot research topic recently due to the rapid accumulation of data and advancement of computer hardware. The application of ML techniques in the design and fabrication of solar cells started slowly but has recently gained tremendous momentum. An exhaustive compilation of the literatures indicates that all the major aspects in the research and development of solar cells can be effectively assisted by ML techniques. If combined with other tools and fed with additional theoretical and experimental data, more accurate and robust results can be achieved from ML techniques. The aspects can be grouped into four categories: prediction of material properties, optimization of device structures, optimization of fabrication processes, and reconstruction of measurement data. A statistical analysis of the literatures shows that artificial neural network (ANN) and genetic algorithm (GA) are the two most applied ML techniques and the topics in the optimization of device structures and optimization of fabrication processes are more popular. This article can be used as a reference by all PV researchers who are interested in ML techniques.

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Finding Relevant Parameters for the Thin‐film Photovoltaic Cells Production Process with the Application of Data Mining Methods

Cited by 6 publications

References 14 publications

Extracting bulk defect parameters in silicon wafers using machine learning models

Extracting bulk defect parameters in silicon wafers using machine learning models

Visualization of Solar Cell Library Space by Dimensionality Reduction Methods

Machine Learning (ML)‐Assisted Design and Fabrication for Solar Cells

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