Building intuition of iron evolution during solar cell processing through analysis of different process models
Name
339_2015_9317_ReferencePDF.pdf
Size
680.97 KB
Format
Adobe PDF
Checksum (MD5)
83f187b1f3b3be60a2e98e34f0f4c7c3
Author(s)
Morishige, Ashley Elizabeth
Laine, Hannu S.
Schön, Jonas
Haarahiltunen, Antti
Hofstetter, Jasmin
del Cañizo, Carlos
Schubert, Martin C.
Savin, Hele
Buonassisi, Anthony
Date Issued
July 2015
Journal
Applied Physics A
Publisher
Springer Berlin Heidelberg
Citation
Morishige, Ashley E., Hannu S. Laine, Jonas Schön, Antti Haarahiltunen, Jasmin Hofstetter, Carlos del Cañizo, Martin C. Schubert, Hele Savin, and Tonio Buonassisi. “Building Intuition of Iron Evolution During Solar Cell Processing through Analysis of Different Process Models.” Applied Physics A 120, no. 4 (July 14, 2015): 1357–1373.
Version
Author's final manuscript
Abstract
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[subscript 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[subscript 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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s00339-015-9317-7
