A machine-learning-based optical microscopy technique for crystal orientation mapping
| Autor: | Matteo Seita, Xiaogang Wang, Mallory Wittwer |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Diffraction
Materials science Silicon Turbine blade business.industry Orientation (computer vision) chemistry.chemical_element Machine learning computer.software_genre Sample (graphics) law.invention chemistry Optical microscope law Microscopy Artificial intelligence business Throughput (business) computer |
| Zdroj: | AI and Optical Data Sciences II. |
| DOI: | 10.1117/12.2577469 |
| Popis: | Characterizing crystallographic orientation is essential for assessing structure-property relationships in crystalline solids. While diffraction methods have dominated this field, low throughput and high cost limit their applicability to small, specialized samples and restrict access to well-funded research institutions. We present a complementary optical technique that expands applicability and broadens access. This technique—which we call directional reflectance microscopy (DRM)—relies on acquiring a series of optical micrographs of chemically etched crystalline materials under different illumination angles. Using machine learning to correlate the directional reflectivity of the surface with the local etch-induced surface structure, DRM enables previously impossible crystal orientation mapping of large-scale, complex parts—such as entire multi-crystalline silicon solar cells, turbine blades, and complex parts produced by additive manufacturing technology. The simplicity, low cost, and enhanced sample throughput of our method promise to expand the availability of crystallographic orientation mapping significantly, making it readily available in education as well as academic research and industrial settings. |
| Databáze: | OpenAIRE |
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