Impurity‐to‐efficiency simulator: predictive simulation of silicon solar cell performance based on iron content and distribution

Hofstetter, Jasmin; Fenning, David P.; Bertoni, Mariana I.; Lelièvre, Jean‐François; Cañizo, Carlos del; Buonassisi, Tonio

doi:10.1002/pip.1062

Cited by 51 publications

(34 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These Simulators differ in two significant ways: (1) physics: they make different assumptions regarding the governing physics of nucleation, precipitation, growth, and dissolution of iron-silicide precipitates, and (2) implementation: they use different coding environments, with unique mesh assumptions and numerical solvers. Most Simulators have been validated by experimental results, for different processing conditions and input wafer impurity types and concentrations [25][26][27]. Because of differences in coding and validation, it can be difficult for a third party to compare models and determine the most relevant underlying physics for a wider range of industrially relevant processing and material conditions.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we combine into one coding environment the salient features of iron Process Simulators developed at Aalto University [25], Fraunhofer Institute for Solar Energy Systems [26], and Massachusetts Institute of Technology jointly with Universidad Politécnica de Madrid [27]. Our goals are: (1) to elucidate the essential physics at each process step, (2) to determine the necessary Model complexity to accurately simulate today's materials and processes, and (3) to guide future materials, device, and Process Simulation development by building intuition for the behavior of iron.…”

Section: Introductionmentioning

confidence: 99%

“…At least eight research groups have developed tools to simulate the evolution of iron during solar cell processing [21][22][23][24][25][26][27][28]. These Simulators differ in two significant ways: (1) physics: they make different assumptions regarding the governing physics of nucleation, precipitation, growth, and dissolution of iron-silicide precipitates, and (2) implementation: they use different coding environments, with unique mesh assumptions and numerical solvers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Building intuition of iron evolution during solar cell processing through analysis of different process models

et al. 2015

View full text Add to dashboard Cite

An important aspect of Process Simulators for photovoltaics is prediction of defect evolution during device fabrication. Over the last twenty years, these tools have accelerated process optimization, and several Process Simulators for iron, a ubiquitous and deleterious impurity in silicon, have been developed. The diversity of these tools can make it difficult to build intuition about the physics governing iron behavior during processing. Thus, in one unified software environment and using self-consistent terminology, we combine and describe three of these Simulators. We vary structural defect distribution and iron precipitation equations to create eight distinct Models, which we then use to simulate different stages of processing. We find that the structural defect distribution influences the final interstitial iron concentration ([Fe,]) more strongly than the iron precipitation equations. We identify two regimes of iron behavior: (1) diffusivity-limited, in which iron evolution is kinetic ally limited and bulk [Fe,] predictions can vary by an order of magnitude or more, and (2) solubility-limited, in which iron evolution is near thermodynamic equilibrium and the Models yield similar results. This rigorous analysis provides new intuition that can inform Process Simulation, material, and process development, and it enables scientists and engineers to choose an appropriate level of Model complexity based on wafer type and quality, processing conditions, and available computation time.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Building intuition of iron evolution during solar cell processing through analysis of different process models

et al. 2015

View full text Add to dashboard Cite

show abstract

“…This step has been widely investigated and it has been highlighted that the gettering efficiency depends strongly on the as-grown iron concentration and distribution [6][7][8]. Some defect engineering tools have been developed recently based on this understanding to enhance the purification effect, such as the so-called extended gettering [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Lifetime improvement after phosphorous diffusion gettering on upgraded metallurgical grade silicon

Peral

Mı́guez²,

Ordás³

et al. 2014

Solar Energy Materials and Solar Cells

View full text Add to dashboard Cite

Wide experimental evidence of the phosphorus diffusion gettering beneficial effect on solar grade silicon is found by measuring electron effective lifetime and interstitial iron concentration in as-grown and post processed samples from two ingots of upgraded metallurgical grade silicon produced by Ferrosolar. Results after two different P-diffusion processes are compared: P emitter diffusion at 850ºC followed by fast cool-down (called "standard process") or followed by slow cool-down (called "extended process"). It is shown that final lifetimes of this low cost material are in the range of those obtained with conventional material. The extended process can be beneficial for wafers with specific initial distribution and concentration of iron, e.g. materials with high concentration of big Fe precipitates, while for other cases the standard process is enough efficient. An analysis based on the comparison of measured lifetime and dissolved iron concentration with theoretical calculations helps to infer the initial iron distribution and concentration, and according to that, choose the more effective type of gettering.

show abstract

“…Iron, for example, has been well-studied, and kinetics process simulation tools exist to engineer its distribution in the material. [6][7][8][9]33 The impact of processing steps on chromium (both precipitated and interstitial) has not been studied as extensively, although the detrimental nature of the impurity is well-known. The maximum allowable chromium contamination in the silicon melt ranges from 1 Â 10 15 cm À3 to 2 Â 10 17 cm À3 depending on the growth process, device architecture, and target efficiency.…”

mentioning

confidence: 99%

Synchrotron-based analysis of chromium distributions in multicrystalline silicon for solar cells

Jensen

Hofstetter

Morishige

et al. 2015

Applied Physics Letters

View full text Add to dashboard Cite

Chromium (Cr) can degrade silicon wafer-based solar cell efficiencies at concentrations as low as 1010 cm−3. In this contribution, we employ synchrotron-based X-ray fluorescence microscopy to study chromium distributions in multicrystalline silicon in as-grown material and after phosphorous diffusion. We complement quantified precipitate size and spatial distribution with interstitial Cr concentration and minority carrier lifetime measurements to provide insight into chromium gettering kinetics and offer suggestions for minimizing the device impacts of chromium. We observe that Cr-rich precipitates in as-grown material are generally smaller than iron-rich precipitates and that Cri point defects account for only one-half of the total Cr in the as-grown material. This observation is consistent with previous hypotheses that Cr transport and CrSi2 growth are more strongly diffusion-limited during ingot cooling. We apply two phosphorous diffusion gettering profiles that both increase minority carrier lifetime by two orders of magnitude and reduce [Cri] by three orders of magnitude to ≈1010 cm−3. Some Cr-rich precipitates persist after both processes, and locally high [Cri] after the high-temperature process indicates that further optimization of the chromium gettering profile is possible.

show abstract

Impurity‐to‐efficiency simulator: predictive simulation of silicon solar cell performance based on iron content and distribution

Cited by 51 publications

References 40 publications

Building intuition of iron evolution during solar cell processing through analysis of different process models

Building intuition of iron evolution during solar cell processing through analysis of different process models

Lifetime improvement after phosphorous diffusion gettering on upgraded metallurgical grade silicon

Synchrotron-based analysis of chromium distributions in multicrystalline silicon for solar cells

Contact Info

Product

Resources

About