Building intuition of iron evolution during solar cell processing through analysis of different process models
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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Berlin Heidelberg
2016
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| Online Access: | http://hdl.handle.net/1721.1/103180 https://orcid.org/0000-0001-9352-8741 https://orcid.org/0000-0001-8345-4937 |
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| author | Morishige, Ashley Elizabeth Laine, Hannu S. Schön, Jonas Haarahiltunen, Antti Hofstetter, Jasmin del Cañizo, Carlos Schubert, Martin C. Savin, Hele Buonassisi, Anthony |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Morishige, Ashley Elizabeth Laine, Hannu S. Schön, Jonas Haarahiltunen, Antti Hofstetter, Jasmin del Cañizo, Carlos Schubert, Martin C. Savin, Hele Buonassisi, Anthony |
| author_sort | Morishige, Ashley Elizabeth |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:35:31Z |
| format | Article |
| id | mit-1721.1/103180 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:35:31Z |
| publishDate | 2016 |
| publisher | Springer Berlin Heidelberg |
| record_format | dspace |
| spelling | mit-1721.1/1031802022-09-23T13:09:07Z Building intuition of iron evolution during solar cell processing through analysis of different process models Morishige, Ashley Elizabeth Laine, Hannu S. Schön, Jonas Haarahiltunen, Antti Hofstetter, Jasmin del Cañizo, Carlos Schubert, Martin C. Savin, Hele Buonassisi, Anthony Massachusetts Institute of Technology. Department of Mechanical Engineering Morishige, Ashley Elizabeth Hofstetter, Jasmin Buonassisi, Anthony 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. National Science Foundation (U.S.) United States. Dept. of Energy (NSF CA No. EEC-1041895) Tekes (Agency) (project ‘‘PASSI’’ (project No. 2196/31/ 2011)) Academy of Finland (project ‘‘Low- Cost Photovoltaics.’’) German Federal Ministry for the Environment, Nature Conservation and Nuclear (research cluster ‘‘SolarWinS’’ (contract No. 0325270A-H)) Alexander von Humboldt-Stiftung (Feodor Lynen Postdoctoral Fellowship) Massachusetts Institute of Technology. Department of Mechanical Engineering (Peabody Visiting Professorship) Harvard University (Real Colegio Complutense, RCC Fellowship) Finnish Cultural Foundation (grant No. 00150504) 2016-06-21T21:50:06Z 2016-06-21T21:50:06Z 2015-07 2015-04 2016-05-23T12:09:54Z Article http://purl.org/eprint/type/JournalArticle 0947-8396 1432-0630 http://hdl.handle.net/1721.1/103180 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. https://orcid.org/0000-0001-9352-8741 https://orcid.org/0000-0001-8345-4937 en http://dx.doi.org/10.1007/s00339-015-9317-7 Applied Physics A Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ Springer-Verlag Berlin Heidelberg application/pdf Springer Berlin Heidelberg Springer Berlin Heidelberg |
| spellingShingle | Morishige, Ashley Elizabeth Laine, Hannu S. Schön, Jonas Haarahiltunen, Antti Hofstetter, Jasmin del Cañizo, Carlos Schubert, Martin C. Savin, Hele Buonassisi, Anthony Building intuition of iron evolution during solar cell processing through analysis of different process models |
| title | Building intuition of iron evolution during solar cell processing through analysis of different process models |
| title_full | Building intuition of iron evolution during solar cell processing through analysis of different process models |
| title_fullStr | Building intuition of iron evolution during solar cell processing through analysis of different process models |
| title_full_unstemmed | Building intuition of iron evolution during solar cell processing through analysis of different process models |
| title_short | Building intuition of iron evolution during solar cell processing through analysis of different process models |
| title_sort | building intuition of iron evolution during solar cell processing through analysis of different process models |
| url | http://hdl.handle.net/1721.1/103180 https://orcid.org/0000-0001-9352-8741 https://orcid.org/0000-0001-8345-4937 |
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