Heterodyne detection system for nanoparticle detection using coherent Fourier scatterometry

Kolenov, Dmytro; Horsten, Roland; Pereira, Silvania F.

doi:10.1117/12.2525587

Cited by 5 publications

(8 citation statements)

References 9 publications

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“…Among various noise sources that might be present in our experiment [14], the power line interference and the baseline wandering can strongly affect the further detection and classification of particle signals. The 50-60 Hz local power-line frequency (bandwidth of < 1 Hz) can be mostly removed by analogue hardware during acquisition, and the remaining noise is removed digitally using the notch filter.…”

Section: Pre-processingmentioning

confidence: 99%

“…Alternatively, one can obtain high sensitivity and low power of the illumination by measuring the light that is scattered from the particles to the far field in a smart way such that the SNR can be improved as compared to dark field techniques. This is the core of the technique used in this paper, namely Coherent Fourier Scatterometry (CFS): it is a low cost, robust, and suitable for the detection of polystyrene latex (PSL) nanoparticles down to 50 nm in diameter, and possibly even smaller ones [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…Reliable numerical tools have been developed to understand the parameters that could influence the scattering process such as polarization, beam shaping, and how the data should be collected. Experimentally, besides a robust design, improvements directed to the detection system (such as noise suppression by introducing heterodyne detection system and beam shaping [14,18]) have been implemented. At last, the data processing is of extreme importance, and this is the main subject of this paper.…”

Section: Introductionmentioning

confidence: 99%

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

Kolenov

Pereira²

2020

Opt. Express

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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.

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Section: Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kolenov

Pereira²

2020

Opt. Express

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“…In order to maintain the high yield and quality in semiconductor manufacturing, particle contamination in the range of 1 µm down to 20 nm (in diameter) should be detected and, if possible, removed. Coherent Fourier scatterometry (CFS) has been suggested to fulfil the need for noninvasive and sensitive inspection of nanoparticles on surfaces [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network applied for nanoparticle classification using coherent scatterometry data

Kolenov¹,

Davidse²,

Cam³

et al. 2020

Appl. Opt.

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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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“…In this paper, we propose a method to classify 2D scattered maps of containing isolated nanoparticles on surfaces using CNN. These maps have been generated in a Coherent Fourier Scatterometry (CFS) setup [1][2][3][4]. In order to generate the scattered maps to train the data, polystyrene latex (PSL) nanospheres of calibrated sizes were spin-coated on a silicon wafer.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Applied for Nanoparticle Classification using Coherent Scaterometry Data

et al. 2020

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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.

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Heterodyne detection system for nanoparticle detection using coherent Fourier scatterometry

Cited by 5 publications

References 9 publications

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

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

Convolutional neural network applied for nanoparticle classification using coherent scatterometry data

Convolutional Neural Network Applied for Nanoparticle Classification using Coherent Scaterometry Data

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