Convolutional Neural Network Applied for Nanoparticle Classification using Coherent Scaterometry Data
1 Optics Research Group, Imaging Physics Department, Faculty of Applied Sciences, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands
2 Institut d’Optique Graduate School, 2 Avenue Augustin Fresnel, 91120 Palaiseau, France
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Published online: 20 August 2020
The analysis of 2D scattering maps generated in scatterometry experiments for detection and classification of nanoparticle on surfaces is a cumbersome and slow process. Recently, deep learning techniques have been adopted to avoid manual feature extraction and classification in many research and application areas, including optics. In the present work, we collected experimental dataset of nanoparticles deposited on wafers for four different classes of polystyrene particles (with diameters of 40, 50, 60, 80 nm) plus background (no particles) class. We trained a convolutional neural network, including its architecture optimization, and achieved 95% accurate results. We compared the performance of this network to a existing method based on line-by-line search and thresholding, demonstrating up to a twofold enhanced performance in particle classification. The network is extended by a supervisor layer that can reject up to 80% of the fooling images at the cost of only rejecting 10% of original data.
© The Authors, published by EDP Sciences, 2020
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