Optical loss analysis of silicon solar cells using spatial resolved quantum efficiency and reflectance measurements

Schneller, Eric; Davis, Kristopher O.; Ogutman, Kortan; Schoenfeld, Winston V.

doi:10.1109/pvsc.2016.7750099

Cited by 2 publications

(10 citation statements)

References 8 publications

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“…Since the IQE also relies on the diffusion length of the charge carriers in the sample, the diffusion length might be reduced due to recombination, leading to the lower IQE. [ 27 ]…”

Section: Behavior Of Nc‐sic:h(n) Layers In Solar Cellsmentioning

confidence: 99%

Material Properties of Nanocrystalline Silicon Carbide for Transparent Passivating Contact Solar Cells

et al. 2023

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“…Since the IQE also relies on the diffusion length of the charge carriers in the sample, the diffusion length might be reduced due to recombination, leading to the lower IQE. [ 27 ]…”

Section: Behavior Of Nc‐sic:h(n) Layers In Solar Cellsmentioning

confidence: 99%

Material Properties of Nanocrystalline Silicon Carbide for Transparent Passivating Contact Solar Cells

et al. 2023

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“…In this study, large training and testing datasets of IQE curves were generated using the formulas adapted from Fischer 7,8 :…”

Section: Datasetsmentioning

confidence: 99%

“…2,[4][5][6][7] However, this process requires testing different combinations of input parameters, which can be time-consuming and complex. Software packages such as LASSIE 8 have been developed to automate the fitting process.…”

Section: Introductionmentioning

confidence: 99%

“…16 However, they do not always represent the ARC properties when deposited on Si wafers, particularly after undergoing thermal processing steps. [14][15][16] Sun-Solve 10 obtains the parasitic absorption by using complex ray tracing algorithms, which is computationally expensive, while LASSIE 8…”

Section: Introductionmentioning

confidence: 99%

“…2023;31(8):790-802. doi:10. 1002/pip.3683 APPENDIX AComplete formulae for the IQE generation used in Equation (2):7,8…”

mentioning

confidence: 99%

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Advanced analysis of internal quantum efficiency measurements using machine learning

Abdullah-Vetter

Dwivedi

Buratti

et al. 2023

Progress in Photovoltaics

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The internal quantum efficiency (IQE) is given as the ratio between the externally collected electron current and the photon current absorbed by the device. Spectral analysis of IQE measurements is a powerful method to identify performance‐limiting mechanisms in solar cells. It also enables the extraction of key electrical and optical parameters. However, the potential of IQE measurements is only rarely fully utilized, presumably due to the significant complexity associated with the fitting process and its sensitivity to noise. In this study, machine learning is proposed as an efficient method to extract quantitative information from IQE measurements. The extraction method is automated and easy to use, providing an array of specific device parameters. By simplifying the analytical process, the developed machine learning algorithms also extract the parasitic absorption of the antireflection coating, a key parameter that is difficult to obtain by traditional methods. Although the method has been developed for and tested on silicon solar cells, it can be adapted and applied to other types of solar cells.

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Optical loss analysis of silicon solar cells using spatial resolved quantum efficiency and reflectance measurements

Cited by 2 publications

References 8 publications

Material Properties of Nanocrystalline Silicon Carbide for Transparent Passivating Contact Solar Cells

Material Properties of Nanocrystalline Silicon Carbide for Transparent Passivating Contact Solar Cells

Advanced analysis of internal quantum efficiency measurements using machine learning

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