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

• Published in: Applied physics. A, Materials science & processing Materials science & processing, 2015-09, Vol.120 (4), p.1357-1373
• Main Authors: Morishige, Ashley E., Laine, Hannu S., Schön, Jonas, Haarahiltunen, Antti, Hofstetter, Jasmin, del Cañizo, Carlos, Schubert, Martin C., Savin, Hele, Buonassisi, Tonio
• Format: Article
• Language: English
• Subjects: Characterization and Evaluation of Materials; Computer simulation; Condensed Matter Physics; Construction; Defects; Evolution; Iron; Machines; Manufacturing; Mathematical analysis; Mathematical models; Nanotechnology; Optical and Electronic Materials; Physics; Physics and Astronomy; Processes; Simulators; Surfaces and Interfaces; Thin Films
• ISSN: 0947-8396; 1432-0630
• DOI: 10.1007/s00339-015-9317-7

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 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 [ 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.