New insights into the thermally activated defects in n-type float-zone silicon

Zhu, Yan; Rougieux, Fiacre; Grant, Nicholas E.; Mullins, Jack; Guzman, Joyce Ann T. De; Murphy, John D.; Маркевич, В. П.; Coletti, Gianluca; Peaker, А. R.; Hameiri, Ziv

doi:10.1063/1.5123901

Cited by 13 publications

(16 citation statements)

References 19 publications

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“…Rougieux et al demonstrated that the detrimental, lifetime‐reducing impact of the bulk defects can be mitigated to some extent when wafers are thermally processed with hydrogen‐containing capping layers such as atomic layer deposition (ALD)–Al 2 O 3 or (preferably) plasma enhanced chemical vapor deposition (PECVD)–SiN x :H. [ 6 ] Very recently, it was shown that the bulk lifetime of a degraded sample can be improved by one order of magnitude by an annealing of the wafer with a PECVD–SiN x :H layer. [ 9 ]…”

Section: Resultsmentioning

confidence: 99%

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Kinetics of Bulk Lifetime Degradation in Float‐Zone Silicon: Fast Activation and Annihilation of Grown‐In Defects and the Role of Hydrogen versus Light

Hiller

Markevich

Guzman

et al. 2020

Physica Status Solidi (a)

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Float‐zone (FZ) silicon often has grown‐in defects that are thermally activated in a broad temperature window (≈300–800 °C). These defects cause efficient electron‐hole pair recombination, which deteriorates the bulk minority carrier lifetime and thereby possible photovoltaic conversion efficiencies. Little is known so far about these defects which are possibly Si‐vacancy/nitrogen‐related (VxNy). Herein, it is shown that the defect activation takes place on sub‐second timescales, as does the destruction of the defects at higher temperatures. Complete defect annihilation, however, is not achieved until nitrogen impurities are effused from the wafer, as confirmed by secondary ion mass spectrometry. Hydrogenation experiments reveal the temporary and only partial passivation of recombination centers. In combination with deep‐level transient spectroscopy, at least two possible defect states are revealed, only one of which interacts with H. With the help of density functional theory V1N1‐centers, which induce Si dangling bonds (DBs), are proposed as one possible defect candidate. Such DBs can be passivated by H. The associated formation energy, as well as their sensitivity to light‐induced free carriers, is consistent with the experimental results. These results are anticipated to contribute to a deeper understanding of bulk‐Si defects, which are pivotal for the mitigation of solar cell degradation processes.

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Section: Resultsmentioning

confidence: 99%

“…[ 6–8 ] Hydrogen (from H‐rich dielectric coatings) was proposed to mitigate the effects of bulk lifetime degradation. [ 6,9 ] We will demonstrate that the passivating effect of H is not permanent.…”

Section: Introductionmentioning

confidence: 93%

Kinetics of Bulk Lifetime Degradation in Float‐Zone Silicon: Fast Activation and Annihilation of Grown‐In Defects and the Role of Hydrogen versus Light

Hiller

Markevich

Guzman

et al. 2020

Physica Status Solidi (a)

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“…Activation anneals for aluminum oxide surface passivation are also often performed in this temperature range [20]. Previous studies found that these thermally activated defects can be partially passivated via hydrogenation [19], [21], [22]. Therefore, these defects could be activated during the surface passivation process, but also be partially passivated by the hydrogen introduced during the dielectric deposition process.…”

Section: Introductionmentioning

confidence: 99%

Electrical Characterization of Thermally Activated Defects in n-Type Float-Zone Silicon

Zhu

Rougieux

Grant

et al. 2021

IEEE J. Photovoltaics

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“…It is therefore primordial to identify and characterize bulk defects in order to eliminate or at least reduce their impact. Techniques such as temperature-and injectiondependent lifetimes spectroscopy (TIDLS) 5 enables characterization of a wide range of Si bulk defects, such as copper 6 , aluminium 7,8 , chromium 9,10 , iron 11,12 and many more [13][14][15][16][17] . To identify defects, the Shockley-Read-Hall (SRH) equation 18,19 that describes the impact of a defect on the overall minority carrier lifetime is often used to fit the lifetime measurements 20 .…”

Section: Introductionmentioning

confidence: 99%

“…By repeating the process for each temperature-dependent measurement, different DPSS curves are determined. These curves usually intersect at two points that indicate the possible combinations of the defect parameters, with one set of parameters in the upper bandgap and one in the lower bandgap [7][8][9][10][11][12][13]21 . This method, henceforth referred to as the 'traditional approach', has been refined over the years, such as linearizing the SRH equation under specific conditions 14,22 or applying faster convergence techniques to find the intersection points 21 .…”

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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New insights into the thermally activated defects in n-type float-zone silicon

Cited by 13 publications

References 19 publications

Kinetics of Bulk Lifetime Degradation in Float‐Zone Silicon: Fast Activation and Annihilation of Grown‐In Defects and the Role of Hydrogen versus Light

Kinetics of Bulk Lifetime Degradation in Float‐Zone Silicon: Fast Activation and Annihilation of Grown‐In Defects and the Role of Hydrogen versus Light

Electrical Characterization of Thermally Activated Defects in n-Type Float-Zone Silicon

Extracting bulk defect parameters in silicon wafers using machine learning models

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