Machine learning techniques applied for detection of nanoparticles on surfaces using Coherent Fourier Scatterometry

TL;DR

This paper introduces a machine learning framework that effectively detects and classifies nanoparticles on surfaces using Coherent Fourier Scatterometry, even in challenging conditions like high density and noise.

Contribution

The study develops a novel combination of pre-processing, unsupervised clustering, and prior size information to improve nanoparticle detection accuracy in CFS data.

Findings

Accurate detection of nanoparticles down to λ/8 size.

Effective handling of high-density and noisy datasets.

Demonstrated success in real and numerical experiments.

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 $\lambda/8$). 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.