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

D Kolenov,S F Pereira

doi:10.1364/oe.395233

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.

Highlights

Much research on detection and localization of deep-subwavelength objects based on optical scattering has been done, covering a wide range of particle types such as viruses, bacteria, dust and nanofabricated features [1,2,3,4]
The objective of this paper is to develop a full framework for particle size classification in scatterometry data consisting of pre-processing, signal search and histogram formation with an algorithm that can be directly targeted at data that is corrupted with noise and drift, as well as including mixed-size particles per sample
Throughout this section we experimentally study three different samples of the polystyrene latex (PSL) particles spin-coated on the silicon wafer

Summary

Introduction

Much research on detection and localization of deep-subwavelength objects based on optical scattering has been done, covering a wide range of particle types such as viruses, bacteria, dust and nanofabricated features [1,2,3,4]. In the context of the semiconductor industry, we can think of unwanted contamination on the silicon wafers in the nanometer-size scale. This contamination can occur at different stages of the lithography process, and it is important to check blank or patterned wafer as well as the mask (reticle) itself. To ensure the quality and high yield in semiconductor manufacturing, contamination due to isolated particles in the size range of from 20 nm to 1 in diameter should be detected and, if possible, localised and removed

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