Quantifying defects in thin films using machine vision

Autor: Edward P. Booker, Curtis P. Berlinguette, Benjamin P. MacLeod, Thomas D. Morrissey, Fraser G. L. Parlane, Kevan E. Dettelbach, Nina Taherimakhsousi
Rok vydání: 2020
Předmět:
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
Zdroj: npj Computational Materials, Vol 6, Iss 1, Pp 1-6 (2020)
ISSN: 2057-3960
Popis: 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
Databáze: OpenAIRE
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