Dissolution and gettering of iron during contact co-firing

Lelièvre, Jean‐François; Hofstetter, Jasmin; Peral, Ana; Hoces, I.; Recart, F.; Cañizo, Carlos del

doi:10.1016/j.egypro.2011.06.133

Cited by 25 publications

(23 citation statements)

References 10 publications

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“…Thus, the reduction in [Fe,] after P-diffusion can be erased [67,75]. Firing time-temperature profiles with lower peak temperatures and a slower cooling from the peak as suggested in [75] can reduce [Fe,].…”

Section: Firing In the Presence Of A Phosphorus-rich Layermentioning

confidence: 99%

“…Cooling is an inherent component of solar cell manufacturing processes, and the precise time-temperature profiles are essential for controlling the post-processing iron distribution and solar cell performance [18,73,75]. For iron evolution during cooling at 10°C/min from 1150 to 500 °C after the heating step discussed in this section, see Online Resource 5.…”

Section: Heating Up To Full Precipitate Dissolutionmentioning

confidence: 99%

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

et al. 2015

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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.

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Section: Firing In the Presence Of A Phosphorus-rich Layermentioning

confidence: 99%

Section: Heating Up To Full Precipitate Dissolutionmentioning

confidence: 99%

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

et al. 2015

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“…This increase can be attributed to the dissolution of iron precipitates [5]- [7], and also to the re-injection of iron from the emitter into the bulk [8], [9]. However, adequate defect engineering tools can compensate completely or partly this increase thanks to an external gettering into the phosphorus and aluminum layers during the temperature plateau.…”

mentioning

confidence: 99%

“…Predictive Impurity-to-Efficiency simulations [5], [6] show a relative increase of dissolved iron during the standard co-firing step between 200% and 1000% for materials with high total contamination concentration (with initial total Fe concentrations between 10 15 and 10…”

mentioning

confidence: 99%

Impact of Extended Contact Cofiring on Multicrystalline Silicon Solar Cell Parameters

Peral

Dastgheib-Shirazi

Fano

et al. 2017

IEEE J. Photovoltaics

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“…[28,29] Iron precipitates are known to cause charge carrier recombination, [30,31] and are known to be difficult to getter via PDG. [32][33][34] They can also dissolve during thermal processing following the PDG, which is typical in, e.g., silicon solar cell processing, [35] which increases their harmful impact. [36] In order to facilitate the hard core removal, a modified PDG process was implemented.…”

Section: Resultsmentioning

confidence: 99%

Electronic Quality Improvement of Highly Defective Quasi‐Mono Silicon Material by Phosphorus Diffusion Gettering

Liu

Vähänissi

Laine

et al. 2017

Adv Elect Materials

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Quasi‐mono silicon (QM‐Si) attracts interest as a substrate material for silicon device processing with the promise to yield single‐crystalline silicon quality with multicrystalline silicon cost. A significant barrier to widespread implementation of QM‐Si is ingot edge‐contamination caused by the seed material and crucible walls during crystal growth. This work aims to recover the scrap material in QM‐Si manufacturing with a process easily adaptable to semiconductor device manufacturing. A phosphorus diffusion process at 870 °C for 60 min significantly improves the electronic quality of a QM‐Si wafer cut from a contaminated edge brick. The harmonic minority carrier recombination lifetime of the wafer, a key predictor of ultimate device performance, experiences a tenfold increase from 17 to 178 µs, which makes the scrap QM‐Si material usable for device fabrication. Local areas with suboptimal (<50 µs) lifetimes remaining can be further improved by a high temperature anneal before the phosphorus diffusion process.

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Dissolution and gettering of iron during contact co-firing

Cited by 25 publications

References 10 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

Impact of Extended Contact Cofiring on Multicrystalline Silicon Solar Cell Parameters

Electronic Quality Improvement of Highly Defective Quasi‐Mono Silicon Material by Phosphorus Diffusion Gettering

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