Optimizing the morphology transition on MOVPE-grown (100) β-Ga2O3 film between step-flow growth and step-bunching: A machine learning-assisted approach
The step-bunching instability in (100) β-Ga2O3 films grown via metalorganic vapor phase epitaxy was investigated using a machine learning approach based on Random Forest (RF). This study reveals the interplay of Ga supersaturation (O2/Ga) and impurity effects as coexisting mechanisms driving the mor...
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-05-01
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| Series: | APL Materials |
| Online Access: | http://dx.doi.org/10.1063/5.0261944 |
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| author | Ta-Shun Chou Saud Bin Anooz Natasha Dropka Han-Hsu Chen Zbigniew Galazka Martin Albrecht Andreas Fiedler Andreas Popp |
| author_facet | Ta-Shun Chou Saud Bin Anooz Natasha Dropka Han-Hsu Chen Zbigniew Galazka Martin Albrecht Andreas Fiedler Andreas Popp |
| author_sort | Ta-Shun Chou |
| collection | DOAJ |
| description | The step-bunching instability in (100) β-Ga2O3 films grown via metalorganic vapor phase epitaxy was investigated using a machine learning approach based on Random Forest (RF). This study reveals the interplay of Ga supersaturation (O2/Ga) and impurity effects as coexisting mechanisms driving the morphological transition (from step-flow growth to step-bunching). The developed machine-learning framework accurately classifies growth morphology and offers valuable insights by correlating theoretical principles with experimental parameters. Critical growth parameters influencing the film morphology were identified. The corresponding strategy, high Ga supersaturation, is proposed to mitigate the step-bunching formation and maintain the desired step-flow growth mode. Despite the challenges posed by small datasets, the RF model demonstrates robust classification performance, establishing machine learning as a powerful tool for experimental science. |
| format | Article |
| id | doaj-art-2469412cee6b49a18c6ceea062d2bf5b |
| institution | Kabale University |
| issn | 2166-532X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | APL Materials |
| spelling | doaj-art-2469412cee6b49a18c6ceea062d2bf5b2025-08-20T03:37:02ZengAIP Publishing LLCAPL Materials2166-532X2025-05-01135051110051110-910.1063/5.0261944Optimizing the morphology transition on MOVPE-grown (100) β-Ga2O3 film between step-flow growth and step-bunching: A machine learning-assisted approachTa-Shun Chou0Saud Bin Anooz1Natasha Dropka2Han-Hsu Chen3Zbigniew Galazka4Martin Albrecht5Andreas Fiedler6Andreas Popp7Leibniz-Institut für Kristallzüchtung (IKZ), Max-Born-Str. 2, 12489 Berlin, GermanyLeibniz-Institut für Kristallzüchtung (IKZ), Max-Born-Str. 2, 12489 Berlin, GermanyLeibniz-Institut für Kristallzüchtung (IKZ), Max-Born-Str. 2, 12489 Berlin, GermanyLeibniz-Institut für Kristallzüchtung (IKZ), Max-Born-Str. 2, 12489 Berlin, GermanyLeibniz-Institut für Kristallzüchtung (IKZ), Max-Born-Str. 2, 12489 Berlin, GermanyLeibniz-Institut für Kristallzüchtung (IKZ), Max-Born-Str. 2, 12489 Berlin, GermanyLeibniz-Institut für Kristallzüchtung (IKZ), Max-Born-Str. 2, 12489 Berlin, GermanyLeibniz-Institut für Kristallzüchtung (IKZ), Max-Born-Str. 2, 12489 Berlin, GermanyThe step-bunching instability in (100) β-Ga2O3 films grown via metalorganic vapor phase epitaxy was investigated using a machine learning approach based on Random Forest (RF). This study reveals the interplay of Ga supersaturation (O2/Ga) and impurity effects as coexisting mechanisms driving the morphological transition (from step-flow growth to step-bunching). The developed machine-learning framework accurately classifies growth morphology and offers valuable insights by correlating theoretical principles with experimental parameters. Critical growth parameters influencing the film morphology were identified. The corresponding strategy, high Ga supersaturation, is proposed to mitigate the step-bunching formation and maintain the desired step-flow growth mode. Despite the challenges posed by small datasets, the RF model demonstrates robust classification performance, establishing machine learning as a powerful tool for experimental science.http://dx.doi.org/10.1063/5.0261944 |
| spellingShingle | Ta-Shun Chou Saud Bin Anooz Natasha Dropka Han-Hsu Chen Zbigniew Galazka Martin Albrecht Andreas Fiedler Andreas Popp Optimizing the morphology transition on MOVPE-grown (100) β-Ga2O3 film between step-flow growth and step-bunching: A machine learning-assisted approach APL Materials |
| title | Optimizing the morphology transition on MOVPE-grown (100) β-Ga2O3 film between step-flow growth and step-bunching: A machine learning-assisted approach |
| title_full | Optimizing the morphology transition on MOVPE-grown (100) β-Ga2O3 film between step-flow growth and step-bunching: A machine learning-assisted approach |
| title_fullStr | Optimizing the morphology transition on MOVPE-grown (100) β-Ga2O3 film between step-flow growth and step-bunching: A machine learning-assisted approach |
| title_full_unstemmed | Optimizing the morphology transition on MOVPE-grown (100) β-Ga2O3 film between step-flow growth and step-bunching: A machine learning-assisted approach |
| title_short | Optimizing the morphology transition on MOVPE-grown (100) β-Ga2O3 film between step-flow growth and step-bunching: A machine learning-assisted approach |
| title_sort | optimizing the morphology transition on movpe grown 100 β ga2o3 film between step flow growth and step bunching a machine learning assisted approach |
| url | http://dx.doi.org/10.1063/5.0261944 |
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