Convolutional Neural Network Applied for Nanoparticle Classification using Coherent Scaterometry Data

Abstract

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.

Convolutional neural network applied for nanoparticle classification using coherent scatterometry data

“…This will allow to speed up in characterizing large amounts of data with the potential to nearly real-time inspection. The dataset and the codes used to generate the typical results of this paper are available online [34,35].…”