Rating and sorting of mc-Si as-cut wafers in solar cell production using PL imaging

Haunschild, Jonas; Reis, I.; Chipei, Teodora; Demant, Matthias; Thaidigsmann, B.; Linse, M.; Rein, Stefan

doi:10.1016/j.solmat.2012.05.027

Cited by 18 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To overcome these limitations, this study proposes a machine learning (ML) based approach to extract the defect parameters from lifetime curves. ML-based methods are already used at the PV system level, for example for fault detection 23,24 or to identify cracks in modules using luminescence imaging techniques [25][26][27][28] . ML has also been used in non-Si applications to find relevant material parameters for fabrication of CIGS solar cells 29 , multijunction solar cells 30 , organic solar cells 31 , or perovskite solar cells 32 .…”

Section: Introductionmentioning

confidence: 99%

Extracting bulk defect parameters in silicon wafers using machine learning models

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Extracting bulk defect parameters in silicon wafers using machine learning models

et al. 2020

View full text Add to dashboard Cite

show abstract

“…area wafer rather than total area. [186][187][188] Hence, it was possible to greatly reduce the nonuniformity in the area increase, which suppressed the efficiency reduction by ≈2%p when going from a single cell of 15.8 cm × 15.8 cm to a 10 000 cm 2 module with a 100-fold increase in area. The 2-3 mA cm −2 J SC decrease can be attributed to the gap between cells generated in cell-to-cell connections and the cell-frame distance for encapsulation and safety.…”

Section: Silicon Solar Cellsmentioning

confidence: 99%

Historical Analysis of High‐Efficiency, Large‐Area Solar Cells: Toward Upscaling of Perovskite Solar Cells

et al. 2020

View full text Add to dashboard Cite

1% at 0.4798 cm 2 , and 14.0% at 1.045 cm 2 , respectively, while the corresponding module efficiencies are 25.1% at 866.45 cm 2 , 24.4% at 13 177 cm 2 , 19.2% at 841 cm 2 , 19.0% at 23 573 cm 2 , and 12.3% at 14.322 cm 2 , respectively. [2-4] Silicon modules account for over 90% of the solar cell market share. [5] Although thin-film solar modules compete with silicon solar cells, the efficiency of the former is lower by ≈5 percentage points (%p). [2-4] Recently, a perovskite solar cell was reported, which used an organic metalhalide hybrid material with an ABX 3 perovskite crystal structure as the lightabsorbing layer. [6-8] This type of perovskite solar cell has attracted much attention as a next-generation solar cell. Kojima et al. first reported a perovskite solar cell with an efficiency of 3.8% in 2009. [9] Initially, perovskite solar cells did not receive much attention because of their poor stability and efficiency. However, research has increased since Park and co-workers reported an efficiency exceeding 9% with stability over 500 h in ambient conditions. [10,11] In the last six years, the efficiency of perovskite solar cells increased from 14.1% to 25.2%, which is the thirdhighest single-junction efficiency reported thus far. [2-4] However, perovskite solar cells are facing commercialization issues. For their successful application to the industry, the following problems must first be addressed: [12,13]-Upscaling (high-efficiency, large-area module demonstration)-Stability (performance degradation over times)-Toxicity (lead (Pb) and cesium (Cs) issues). Stability and toxicity problems were introduced relatively early in the literature. [10,11] Immense efforts were devoted to finding the origin of and solution to the degradation of perovskite solar cells. Owing to the dedicated efforts of countless researchers, the degradation factors were identified as the humidity, light, [14,15] heat, [16,17] and electric fields. [12,18] Degradation issues have been addressed with the development of stable perovskite compositions, electron-transfer layers (ETLs) and hole-transfer layers (HTLs), and encapsulations. Several groups have reported perovskite solar cells that passed the International Electrotechnical Commission (IEC) stability test, while Grätzel and co-workers reported a cell that remained stable for one year. [19-22] Thus, stability has been significantly improved. The status and problems of upscaling research on perovskite solar cells, which must be addressed for commercialization efforts to be successful, are investigated. An 804 cm 2 perovskite solar module has been reported with 17.9% efficiency, which is significantly lower than the champion perovskite solar cell efficiency of 25.2% reported for a 0.09 cm 2 aperture area. For the realization of upscaling high-quality perovskite solar cells, the upscaling and development history of conventional silicon, copper indium gallium sulfur/ selenide and CdTe solar cells, which are already commercialized with modules of sizes up to ≈25 000 cm 2 , are reviewed. ...

show abstract

“…Measurements at bare silicon ingots allow for a determination of bulk lifetime and surface recombination velocity , as well as for a quantification of interstitial iron concentrations . Further, PL imaging of unpassivated silicon wafers allows for determination of the most recombination active wafer regions, such as dislocation clusters and edge regions in multicrystalline silicon and thus enables a first quality rating of the material . Apart from areas of strongly recombination active crystal defects, the charge carrier density in as‐cut wafers is limited by recombination at the unpassivated surfaces.…”

Section: Development Of Photoluminescence‐based Measurement Techniquesmentioning

confidence: 99%

Material limits of multicrystalline silicon from state‐of‐the‐art photoluminescence imaging techniques

Schindler

Giesecke

Michl

et al. 2016

Progress in Photovoltaics

View full text Add to dashboard Cite

This work reports on state-of-the-art silicon material characterization by calibrated photoluminescence (PL) imaging. PL imaging techniques allow for a characterization of a large variety of material properties, ranging from bulk and surface recombination properties of bare silicon ingots, to crystal structure, dopant concentration, and bulk lifetime of silicon wafers up to material limiting impurities. In combination with solar cell simulations, injection-dependent PL imaging of processed wafers provides a method for determining the material's efficiency potential. In this contribution, we present two methods for imaging the concentration of interstitial iron at passivated silicon slices and at unpassivated silicon ingots. The latter is achieved by our latest progress in PL-based charge carrier bulk lifetime measurements with highest sensitivity, giving access to the bulk recombination properties of the ingot. Additionally, we present an improved analysis of specific loss mechanisms in silicon based on de-smeared injection-dependent lifetime images of processed wafers, which is demonstrated on the example of the bulk-related efficiency limitations in n-type high-performance multicrystalline silicon. This latest progress rounds off the established techniques, enhancing the potential of PL-based imaging techniques for a comprehensive assessment of silicon material quality, from limitations in bare silicon ingots to efficiency loss mechanisms in processed samples

show abstract

Rating and sorting of mc-Si as-cut wafers in solar cell production using PL imaging

Cited by 18 publications

References 5 publications

Extracting bulk defect parameters in silicon wafers using machine learning models

Extracting bulk defect parameters in silicon wafers using machine learning models

Historical Analysis of High‐Efficiency, Large‐Area Solar Cells: Toward Upscaling of Perovskite Solar Cells

Material limits of multicrystalline silicon from state‐of‐the‐art photoluminescence imaging techniques

Contact Info

Product

Resources

About