Application of non-contact quantum efficiency measurement for solar cell fabrication process insights

Chan, Keng Siew; Heng, Min Xuan; Ananthanarayanan, Divya; Choi, Kwan Bum; Ho, J.C.

doi:10.1016/j.solener.2021.12.054

Cited by 4 publications

(2 citation statements)

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“…More information on ncEQE for solar cells measurements could be found in our previous work here. [ 21 ]…”

Section: Resultsmentioning

confidence: 99%

“…More information on ncEQE for solar cells measurements could be found in our previous work here. [21] Rather than using CNN for dealing with 2D data, random forest (RF) regression models were selected to train this 1D data. RF is an ensemble machine-learning algorithm of tree predictors, where each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest.…”

Section: Predictions Of I-v Parametersmentioning

confidence: 99%

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Predicting Loss Analysis from Luminescence Images in Si Solar Cells with Convolutional Neural Networks

Huang,

Ho,

Choi

et al. 2023

Solar RRL

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In this work, deep convolutional neural networks (CNNs) are used to speed up the calculation of spatial nonuniform device parameters, voltage and fill factor (FF) losses in crystalline Si solar cells. Luminescence images on finished solar cells, specifically as‐measured electroluminescence (EL) and photoluminescence (PL) images, are used as input features to train the CNN models and losses analysis as output is from a finite‐element modeling program called Griddler. From the analysis of a small dataset of 250 commercial grade solar cells, the CNN models are able to predict the spatially nonuniform distribution of contact resistance and defect parameters of the devices under test and exhibit good performance in the inference of voltage and FF losses. As EL and PL images are widely collected in‐line production data, in the result, the possibility of deploying 2D spatial power loss analysis is implied as a fast, in‐line, and nondestructive analytic tool for solar cells manufacturing.

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“…More information on ncEQE for solar cells measurements could be found in our previous work here. [ 21 ]…”

Section: Resultsmentioning

confidence: 99%

Section: Predictions Of I-v Parametersmentioning

confidence: 99%