Relationship between the ideality factor and the iron concentration in silicon solar cells

Olikh, O. Ya.

doi:10.1016/j.spmi.2019.106309

Cited by 3 publications

(4 citation statements)

References 31 publications

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“…As a result, the analytically obtained expressions for N Fe ¼ fðnÞ are not universal and numerous grading curves have to be used to determine N Fe ; moreover, IV must be measured in a range of temperatures. 12 Over the last decade, various fields of theoretical and applied physics have successfully been solving different problems which do not involve rigid algorithmization by using deep learning methods. [18][19][20] Moreover, the authors claim 19 that materials informatics (combination of material property calculations/measurements and algorithms of informatics) has become the fourth (along with theory, simulations and experiments) paradigm of science.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous work, 12 we show that iron concentration (

N_{Fe}

) can be estimated by using SC ideality factor ( n ), which is used quite often to characterize semiconductor barrier structures of different types 13–17 . However, the defect signatures are convoluted in the ideality factor with the signatures from many other physical processes.…”

Section: Introductionmentioning

confidence: 99%

“…However, the defect signatures are convoluted in the ideality factor with the signatures from many other physical processes. As a result, the analytically obtained expressions for

N_{Fe} = f false(n false)

are not universal and numerous grading curves have to be used to determine

N_{Fe}

; moreover, IV must be measured in a range of temperatures 12 …”

Section: Introductionmentioning

confidence: 99%

“…In our work too, we apply deep learning for predicting iron concentration from the ideality factor (so to say “deep learning for deep levels”). Unlike in Olikh, 12 we applied it to n + – p – p + structure with back surface field (BSF) and took into account the influence of the base thickness on the ideality factor.…”

Section: Introductionmentioning

confidence: 99%

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Estimation for iron contamination in Si solar cell by ideality factor: Deep neural network approach

Olikh

Lozitsky

Zavhorodnii

2022

Progress in Photovoltaics

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Defect‐assisted recombination often restricts the performance of photovoltaic devices, and in order to mass‐produce reliable solar cells, low‐cost express methods are in demand, which could monitor contamination during the process of manufacture. In our work, we applied the deep learning‐based approach for estimating iron concentration in silicon solar cells by using ideality factor. The simulation of solar cells with the back surface field design for generating labeled training and test datasets was performed using SCAPS‐1D software. Our results demonstrate that deep neural networks can predict iron concentration using the ideality factor, temperature, base‐thickness, and doping level of solar cells. Our simulation showed smaller prediction errors at high doping level, low temperature, and the two values of ideality factor: the first one for structures containing only iron interstitial atoms and the second for structures where Fei and iron–boron pairs coexist. The proposed method was tested on real silicon structures.

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Section: Introductionmentioning

confidence: 99%

“…In our previous work, 12 we show that iron concentration (

N_{Fe}

Section: Introductionmentioning

confidence: 99%

“…However, the defect signatures are convoluted in the ideality factor with the signatures from many other physical processes. As a result, the analytically obtained expressions for

N_{Fe} = f false(n false)

are not universal and numerous grading curves have to be used to determine

N_{Fe}

; moreover, IV must be measured in a range of temperatures 12 …”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation for iron contamination in Si solar cell by ideality factor: Deep neural network approach

Olikh

Lozitsky

Zavhorodnii

2022

Progress in Photovoltaics

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The effect of vacancy-impurity complexes in silicon on the current–voltage characteristics of p–n junctions

Bulyarskiy

Lakalin

Saurov

et al. 2020

Journal of Applied Physics

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The methods for analyzing the current–voltage characteristics of p–n junctions at forward and reverse bias with the calculation of the parameters of recombination centers before and after irradiation with gamma quanta were developed in this work. These methods are simple, convenient, and allow one to determine the parameters of deep levels at one temperature, which makes it possible to use them as express diagnostic techniques, taking measurements immediately on the semiconductor plates at the completion of the main technological processes. It was shown that after irradiation the composition of the centers in the depletion region of the p–n junction changes, the recombination processes and the lifetime begin to determine the divacancy complexes of silicon with oxygen (V2O). It was found that the generation of charge carriers in a strong field of the depletion region of the p–n junction occurs under the influence of the electron–phonon interaction, the parameters of this interaction are determined, and the configuration-coordinate diagrams of recombination centers are constructed.

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Modeling of ideality factor value in n⁺--p--p⁺-Si structure

Olikh¹,

Zavhorodnii²

2020

J. Phys. Stud.

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Relationship between the ideality factor and the iron concentration in silicon solar cells

Cited by 3 publications

References 31 publications

Estimation for iron contamination in Si solar cell by ideality factor: Deep neural network approach

Estimation for iron contamination in Si solar cell by ideality factor: Deep neural network approach

The effect of vacancy-impurity complexes in silicon on the current–voltage characteristics of p–n junctions

Modeling of ideality factor value in n⁺--p--p⁺-Si structure

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Relationship between the ideality factor and the iron concentration in silicon solar cells

Cited by 3 publications

References 31 publications

Estimation for iron contamination in Si solar cell by ideality factor: Deep neural network approach

Estimation for iron contamination in Si solar cell by ideality factor: Deep neural network approach

The effect of vacancy-impurity complexes in silicon on the current–voltage characteristics of p–n junctions

Modeling of ideality factor value in n+--p--p+-Si structure

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Modeling of ideality factor value in n⁺--p--p⁺-Si structure