Applied Physics A

, Volume 120, Issue 4, pp 1357–1373 | Cite as

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

  • Ashley E. Morishige
  • Hannu S. Laine
  • Jonas Schön
  • Antti Haarahiltunen
  • Jasmin Hofstetter
  • Carlos del Cañizo
  • Martin C. Schubert
  • Hele Savin
  • Tonio Buonassisi


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 ([\(\hbox {Fe}_i\)]) more strongly than the iron precipitation equations. We identify two regimes of iron behavior: (1) diffusivity-limited, in which iron evolution is kinetically limited and bulk [\(\hbox {Fe}_i\)] 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.


Precipitate Size Iron Distribution Precipitate Density Total Iron Concentration Precipitate Dissolution 



This material is based upon work supported by the National Science Foundation (NSF) and the Department of Energy (DOE) under NSF CA No. EEC-1041895. Authors from Aalto University acknowledge the financial support from Finnish Technology Agency under the project “PASSI” (project No. 2196/31/2011). A. E. Morishige’s research visit to Aalto University in 2013 was supported by the Academy of Finland under the project “Low-Cost Photovoltaics.” Authors from Fraunhofer ISE acknowledge the financial support by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety within the research cluster “SolarWinS” (contract No. 0325270A-H). A. E. Morishige acknowledges Niall Mangan (MIT) for helpful discussions and the financial support of the Department of Defense through the NDSEG fellowship program. H. S. Laine acknowledges the financial support of the Finnish Cultural Foundation through grant No. 00150504. J. Hofstetter acknowledges support by the A. von Humboldt Foundation through a Feodor Lynen Postdoctoral Fellowship. C. del Cañizo acknowledges the support of the Department of Mechanical Engineering at Massachusetts Institute of Technology through the Peabody Visiting Professorship and the Real Colegio Complutense at Harvard University through a RCC Fellowship.

Supplementary material

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Ashley E. Morishige
    • 1
  • Hannu S. Laine
    • 2
  • Jonas Schön
    • 3
  • Antti Haarahiltunen
    • 2
  • Jasmin Hofstetter
    • 1
  • Carlos del Cañizo
    • 4
  • Martin C. Schubert
    • 3
  • Hele Savin
    • 2
  • Tonio Buonassisi
    • 1
  1. 1.Department of Mechanical EngineeringMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Department of Micro- and NanosciencesAalto UniversityEspooFinland
  3. 3.Characterization of Process Materials and Silicon MaterialsFraunhofer Institute for Solar Energy SystemsFreiburgGermany
  4. 4.Instituto de Energía SolarUniversidad Politécnica de MadridMadridSpain

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