Convolutional Neural Network Applied for NanoparticleClassification using Coherent Scatterometry Data
2020
The analysis of 2D scattering maps generated in scatterometry
experiments for detection and classification of nanoparticles 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 datasets of nanoparticles deposited on wafers for
four different classes of polystyrene particles (with diameters of 40, 50,
60, and 80 nm) plus a 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 an 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 rejecting only 10%
of original data. The developed Python and PyTorch codes, as well as
dataset, are available online.
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