Quantifying defects in thin films using machine vision
| العنوان: | Quantifying defects in thin films using machine vision |
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| المؤلفون: | Edward P. Booker, Curtis P. Berlinguette, Benjamin P. MacLeod, Thomas D. Morrissey, Fraser G. L. Parlane, Kevan E. Dettelbach, Nina Taherimakhsousi |
| المصدر: | npj Computational Materials, Vol 6, Iss 1, Pp 1-6 (2020) |
| بيانات النشر: | Springer Science and Business Media LLC, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | genetic structures, Machine vision, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, FOS: Physical sciences, Applied Physics (physics.app-ph), 02 engineering and technology, 010402 general chemistry, 01 natural sciences, Convolutional neural network, Optical imaging, Software, FOS: Electrical engineering, electronic engineering, information engineering, lcsh:TA401-492, General Materials Science, Computer vision, Sensitivity (control systems), Thin film, lcsh:Computer software, business.industry, Image and Video Processing (eess.IV), Image content, Physics - Applied Physics, Electrical Engineering and Systems Science - Image and Video Processing, 021001 nanoscience & nanotechnology, 0104 chemical sciences, Computer Science Applications, ComputingMethodologies_PATTERNRECOGNITION, lcsh:QA76.75-76.765, Mechanics of Materials, Modeling and Simulation, lcsh:Materials of engineering and construction. Mechanics of materials, sense organs, Artificial intelligence, 0210 nano-technology, business |
| الوصف: | The sensitivity of thin-film materials and devices to defects motivates extensive research into the optimization of film morphology. This research could be accelerated by automated experiments that characterize the response of film morphology to synthesis conditions. Optical imaging can resolve morphological defects in thin films and is readily integrated into automated experiments but the large volumes of images produced by such systems require automated analysis. Existing approaches to automatically analyzing film morphologies in optical images require application-specific customization by software experts and are not robust to changes in image content or imaging conditions. Here we present a versatile convolutional neural network (CNN) for thin-film image analysis which can identify and quantify the extent of a variety of defects and is applicable to multiple materials and imaging conditions. This CNN is readily adapted to new thin-film image analysis tasks and will facilitate the use of imaging in automated thin-film research systems. Comment: 17 pages, 5 figures |
| تدمد: | 2057-3960 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ceea30ea5e12697249646bf6683e2ada https://doi.org/10.1038/s41524-020-00380-w |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....ceea30ea5e12697249646bf6683e2ada |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20573960 |
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