Machine learning techniques applied for the detection of nanoparticles on surfaces using coherent Fourier scatterometry

Optics Express
D Kolenov, S F Pereira

Abstract

We present an efficient machine learning framework for detection and classification of nanoparticles on surfaces that are detected in the far-field with coherent Fourier scatterometry (CFS). We study silicon wafers contaminated with spherical polystyrene (PSL) nanoparticles (with diameters down to λ/8). Starting from the raw data, the proposed framework does the pre-processing and particle search. Further, the unsupervised clustering algorithms, such as K-means and DBSCAN, are customized to be used to define the groups of signals that are attributed to a single scatterer. Finally, the particle count versus particle size histogram is generated. The challenging cases of the high density of scatterers, noise and drift in the dataset are treated. We take advantage of the prior information on the size of the scatterers to minimize the false-detections and as a consequence, provide higher discrimination ability and more accurate particle counting. Numerical and real experiments are conducted to demonstrate the performance of the proposed search and cluster-assessment techniques. Our results illustrate that the proposed algorithm can detect surface contaminants correctly and effectively.

References

May 29, 2015·Nature·Yann LeCunGeoffrey Hinton
Jan 3, 2016·The Review of Scientific Instruments·S RoyP van der Walle
Oct 11, 2017·IEEE Journal of Selected Topics in Quantum Electronics : a Publication of the IEEE Lasers and Electro-optics Society·Jacob TruebM Selim Ünlü
Jun 6, 2018·The Review of Scientific Instruments·S TamaruS Yuasa
Mar 17, 2019·Optics Express·Jinlong ZhuLynford L Goddard
Jan 1, 2018·Nature Electronics·N G OrjiA E Vladar
Dec 12, 2019·OSA Continuum·Mark-Alexander HennBryan M Barnes

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