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

Olikh, O. Ya.; Zavhorodnii, O. V.

doi:10.30970/jps.24.4701

Cited by 1 publication

(4 citation statements)

References 36 publications

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“…As seen from Figure 4C, at

T = 340

K, the maximum SRE is about 20, and SRE below 0.01 is observed for 55% of the samples whereas these values are equal to 0.02 and 83% at

T = 290

K (Figure 4B). It has been shown previously 52 that temperature rise causes the increase in the intrinsic recombination contributions to the ideality factor. As a result, the signature of Shockley‐Read‐Hall (SRH) recombination in n value becomes less prominent and DNN predictive ability decreases.…”

Section: Resultsmentioning

confidence: 92%

“…For instance, our simulation reveals the n dependence on base thickness. 52 Nevertheless this dependence is weak, and the ideality factor value is mainly determined by depletion-region recombination.…”

Section: Datasetmentioning

confidence: 99%

“…The typical simulated dependencies of the ideality factor are shown in Figure 2 and in Supplementary Material. The detailed discussion about

n_{Fe}

and n Fe − FeB values are presented elsewhere, 52 however it should be noted that (i) n can be the same for different values of SC parameters; (ii) dependencies of

n_{Fe}

and n Fe − FeB differ not only in absolute values but also in behavior, although insignificantly.…”

Section: Simulation Detailsmentioning

confidence: 99%

“…The detailed discussion about n Fe and n Fe À FeB values are presented elsewhere,52 however it should be noted that (i) n can be the same for different values of SC parameters; (ii) dependencies of n Fe and n Fe À FeB differ not only in absolute values but also in behavior, although insignificantly.Deep neural network training requires a large number of samples. In order to build a training dataset, we used IV characteristics simulated by using 4 d p values, 9 N B values, 11 T values, and 19 N Fe values, which are regularly distributed (for T and d p in linear scale, for N Fe and N B in logarithmic scale) over the ranges 150-240 μm, 10 15 -10 17 cm À3 , 290 À 340 K, and 10 10 -10 13 cm À3 , respectively.…”

mentioning

confidence: 99%

See 3 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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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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“…As seen from Figure 4C, at

T = 340

K, the maximum SRE is about 20, and SRE below 0.01 is observed for 55% of the samples whereas these values are equal to 0.02 and 83% at

T = 290

Section: Resultsmentioning

confidence: 92%

Section: Datasetmentioning

confidence: 99%

“…The typical simulated dependencies of the ideality factor are shown in Figure 2 and in Supplementary Material. The detailed discussion about

n_{Fe}

and n Fe − FeB values are presented elsewhere, 52 however it should be noted that (i) n can be the same for different values of SC parameters; (ii) dependencies of