Machine learning driven thickness detection in 2D materials
In recent times, two-dimensional (2D) materials have attracted significant attention and revolutionized various electronic device applications due to their heterostructure and unique properties not found in their bulk counterparts. However, the lack of large-scale area characterizing methods of accu...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10356/76702 |
| _version_ | 1826113246116970496 |
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| author | Chen, Douglas Yuanhao |
| author2 | Liu Zheng |
| author_facet | Liu Zheng Chen, Douglas Yuanhao |
| author_sort | Chen, Douglas Yuanhao |
| collection | NTU |
| description | In recent times, two-dimensional (2D) materials have attracted significant attention and revolutionized various electronic device applications due to their heterostructure and unique properties not found in their bulk counterparts. However, the lack of large-scale area characterizing methods of accurate and intelligent detection of 2D nanostructures has hindered the rapid development of 2D materials. Thus, a more time efficient and accurate method is required. In this research, we have implemented the combination of machine learning and optical detection of 2D materials and its nanostructures with accurate predictions of certain MoS2 layers. Machine- learning optical identification (MOI) was established to realize accurate predictions of optical microscope images using RGB information of these 2D materials and their nanostructure. The results have proven that the MOI method is efficient in accurate characterizations of the thickness of large-scale area molybdenum disulfide (MoS2) impurities, as well as the identification of adhesive present during the mechanical exfoliation process in the sample preparation stage. With the successful implementation of AI and nanoscience, machine learning driven thickness detection in 2D materials can certainly drive further fundamental research in the 2D material and its wafer- scale device application space. |
| first_indexed | 2024-10-01T03:20:03Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/76702 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:20:03Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/767022023-03-04T15:38:14Z Machine learning driven thickness detection in 2D materials Chen, Douglas Yuanhao Liu Zheng School of Materials Science and Engineering DRNTU::Engineering::Materials In recent times, two-dimensional (2D) materials have attracted significant attention and revolutionized various electronic device applications due to their heterostructure and unique properties not found in their bulk counterparts. However, the lack of large-scale area characterizing methods of accurate and intelligent detection of 2D nanostructures has hindered the rapid development of 2D materials. Thus, a more time efficient and accurate method is required. In this research, we have implemented the combination of machine learning and optical detection of 2D materials and its nanostructures with accurate predictions of certain MoS2 layers. Machine- learning optical identification (MOI) was established to realize accurate predictions of optical microscope images using RGB information of these 2D materials and their nanostructure. The results have proven that the MOI method is efficient in accurate characterizations of the thickness of large-scale area molybdenum disulfide (MoS2) impurities, as well as the identification of adhesive present during the mechanical exfoliation process in the sample preparation stage. With the successful implementation of AI and nanoscience, machine learning driven thickness detection in 2D materials can certainly drive further fundamental research in the 2D material and its wafer- scale device application space. Bachelor of Engineering (Materials Engineering) 2019-04-04T08:04:53Z 2019-04-04T08:04:53Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/76702 en Nanyang Technological University 31 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Materials Chen, Douglas Yuanhao Machine learning driven thickness detection in 2D materials |
| title | Machine learning driven thickness detection in 2D materials |
| title_full | Machine learning driven thickness detection in 2D materials |
| title_fullStr | Machine learning driven thickness detection in 2D materials |
| title_full_unstemmed | Machine learning driven thickness detection in 2D materials |
| title_short | Machine learning driven thickness detection in 2D materials |
| title_sort | machine learning driven thickness detection in 2d materials |
| topic | DRNTU::Engineering::Materials |
| url | http://hdl.handle.net/10356/76702 |
| work_keys_str_mv | AT chendouglasyuanhao machinelearningdriventhicknessdetectionin2dmaterials |