Iron distribution in silicon after solar cell processing: Synchrotron analysis and predictive modeling

Fenning, David P.; Hofstetter, Jasmin; Bertoni, Mariana I.; Hudelson, S.; Rinio, Markus; Lelievre, Jean Francoise; Lai, Barry; Cañizo, Carlos del; Buonassisi, Tonio

doi:10.1063/1.3575583

Cited by 44 publications

(43 citation statements)

References 16 publications

(16 reference statements)

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“…For these time-temperature profile parameters, the [Fe,] at the beginning of the 600 °C anneal is close to the solid solubility (^10 10 cm~3) [40], so no significant change in [Fe,] or precipitates is observed during the low-temperature plateau. The relative invariance of the precipitate distribution during a post-PDG anneal has also been observed experimentally [88].…”

Section: Different Pdg Approaches (Preanneal Peak Standard Pdg Annementioning

confidence: 59%

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: Different Pdg Approaches (Preanneal Peak Standard Pdg Annementioning

confidence: 59%

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

et al. 2015

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“…An increase in mean precipitate size after gettering has been seen experimentally and in simulation previously for heavily contaminated material for the edge of an ingot. 33,46 Likewise, at the top of the 200 ppmw ingot, the distribution is largely unchanged, with a small shift toward smaller precipitates.…”

Section: Iron-silicide Precipitate Distribution After Standard Promentioning

confidence: 97%

“…33 The as-grown Fe i concentration, which has little impact on final Fe i concentration when a large fraction of total iron is precipitated, was assumed to be 1 Â 10 13 cm À3 for all simulations.…”

Section: à3mentioning

confidence: 99%

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Precipitated iron: A limit on gettering efficacy in multicrystalline silicon

Fenning

Hofstetter

Bertoni

et al. 2013

Journal of Applied Physics

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A phosphorus diffusion gettering model is used to examine the efficacy of a standard gettering process on interstitial and precipitated iron in multicrystalline silicon. The model predicts a large concentration of precipitated iron remaining after standard gettering for most as-grown iron distributions. Although changes in the precipitated iron distribution are predicted to be small, the simulated post-processing interstitial iron concentration is predicted to depend strongly on the as-grown distribution of precipitates, indicating that precipitates must be considered as internal sources of contamination during processing. To inform and validate the model, the iron distributions before and after a standard phosphorus diffusion step are studied in samples from the bottom, middle, and top of an intentionally Fe-contaminated laboratory ingot. A census of iron-silicide precipitates taken by synchrotron-based X-ray fluorescence microscopy confirms the presence of a high density of iron-silicide precipitates both before and after phosphorus diffusion. A comparable precipitated iron distribution was measured in a sister wafer after hydrogenation during a firing step. The similar distributions of precipitated iron seen after each step in the solar cell process confirm that the effect of standard gettering on precipitated iron is strongly limited as predicted by simulation. Good agreement between the experimental and simulated data supports the hypothesis that gettering kinetics is governed by not only the total iron concentration but also by the distribution of precipitated iron. Finally, future directions based on the modeling are suggested for the improvement of effective minority carrier lifetime in multicrystalline silicon solar cells.

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“…f1-XRF has proven useful in probing the behavior of iron in multicrystalline silicon especially [5], given its occurrence at readily observable grain boundaries [6] and dislocations [7]. The evolution of the iron distribution as a function of silicon solar cell processing has been investigated for p-type multicrystalline silicon with standard tube-furnace gettering and firing processes [8], for in-line gettering of red zone material [9], and in a systematic investigation of higher gettering temperatures by several authors [10], [11]. Iron precipitates in contaminated regions of n-type multicrystalline silicon ingots are also under investigation to better understand their performance impact [12].…”

Section: Introductionmentioning

confidence: 99%

Iron precipitation upon gettering in phosphorus-implanted Czochralski silicon and its impact on solar cell performance

Fenning

Vähänissi

Hofstetter

et al. 2014

2014 IEEE 40th Photovoltaic Specialist Conference (PVSC)

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Abstract-Phosphorus implantation can provide a direct route to a high-performing emitter, with no surface dead layer and improved blue response, and potentially higher open-circuit voltage. Here, iron precipitation during gettering is investigated in phosphorus-implanted, low-oxygen monocrystalline silicon and its impact on device performance evaluated. Previously, it has been shown that higher levels of initial iron contamination lead to lower final interstitial iron concentration after gettering with ionimplanted emitters, resulting in longer final bulk diffusion lengths in the more-highly contaminated materials. In this contribution, we show that despite longer bulk diffusion lengths, the open circuit-voltage of devices made from the highly iron-contaminated material can be strongly reduced. Using synchrotron-based Xray fluorescence we reveal the presence of micron-sized iron precipitates in the near surface region. While not measured over wafer-sized areas, the density of these precipitates correlates with the annealing profile. Slow-cooling from the activation anneal and proceeding directly to a 620-750• C gettering anneal results in large precipitates that are indicated as the underlying cause for the disastrous open-circuit voltage. On the other hand, quickly cooling to room temperature and then re-inserting the wafers for gettering results in very small precipitates that do not appear to have significant detrimental affects on open-circuit voltage. It is thus critical to consider the precipitation behavior of iron during gettering of ion-implanted emitters -even in monocrystalline silicon -and during low-temperature annealing in general.

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Iron distribution in silicon after solar cell processing: Synchrotron analysis and predictive modeling

Cited by 44 publications

References 16 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

Precipitated iron: A limit on gettering efficacy in multicrystalline silicon

Iron precipitation upon gettering in phosphorus-implanted Czochralski silicon and its impact on solar cell performance

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