Artificial-intelligence-driven scanning probe microscopy
Enabling atomic-precision mapping and manipulation of surfaces, scanning probe microscopy requires constant human supervision to assess image quality and probe conditions. Here, the authors demonstrate DeepSPM, a machine learning approach allowing to acquire and classify data autonomously in multi-d...
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| Main Authors: | , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
19 March 2020
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| In: |
Communications Physics
Year: 2020, Volume: 3, Pages: 1-8 |
| ISSN: | 2399-3650 |
| DOI: | 10.1038/s42005-020-0317-3 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1038/s42005-020-0317-3 Verlag, lizenzpflichtig, Volltext: https://www.nature.com/articles/s42005-020-0317-3 
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| Author Notes: | A. Krull, P. Hirsch, C. Rother, A. Schiffrin & C. Krull |
| Summary: | Enabling atomic-precision mapping and manipulation of surfaces, scanning probe microscopy requires constant human supervision to assess image quality and probe conditions. Here, the authors demonstrate DeepSPM, a machine learning approach allowing to acquire and classify data autonomously in multi-day Scanning Tunnelling Microscopy experiments. |
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| Item Description: | Gesehen am 28.04.2020 |
| Physical Description: | Online Resource |
| ISSN: | 2399-3650 |
| DOI: | 10.1038/s42005-020-0317-3 |