Coherent Fourier scatterometry for detection of nanometer-sized particles on a planar substrate surface

Roy, S.; Assafrao, A. C.; Pereira, Silvania F.; Urbach, H. P.

doi:10.1364/oe.22.013250

Cited by 22 publications

(15 citation statements)

References 26 publications

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“…The sample was placed on a Physik Instrumente piezo-electric actuator operating within its linear range. The actuator can move the sample in the x-y plane [31,32].…”

Section: Fig 3 the Differential Detection Principle Is Shown In Panmentioning

confidence: 99%

Exploiting evanescent-wave amplification for subwavelength low-contrast particle detection

et al. 2017

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“…The sample was placed on a Physik Instrumente piezo-electric actuator operating within its linear range. The actuator can move the sample in the x-y plane [31,32].…”

Section: Fig 3 the Differential Detection Principle Is Shown In Panmentioning

confidence: 99%

Exploiting evanescent-wave amplification for subwavelength low-contrast particle detection

et al. 2017

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“…In biology, they assist in schemes for the detection of bacteria [1] and viruses [2]. They are important in the pharmaceutical [3,4], food processing [5,6], and the semiconductor industries [7]. Particularly important are applications aimed at environmental monitoring and protection [8,9].…”

Section: Introductionmentioning

confidence: 99%

Identification of Model Particle Mixtures Using Machine-Learning-Assisted Laser Diffraction

et al. 2022

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We put forward and demonstrate with model particles a smart laser-diffraction analysis technique aimed at particle mixture identification. We retrieve information about the size, shape, and ratio concentration of two-component heterogeneous model particle mixtures with an accuracy above 92%. We verify the method by detecting arrays of randomly located model particles with different shapes generated with a Digital Micromirror Device (DMD). In contrast to commonly-used laser diffraction schemes—In which a large number of detectors are needed—Our machine-learning-assisted protocol makes use of a single far-field diffraction pattern contained within a small angle (∼0.26°) around the light propagation axis. Therefore, it does not need to analyze particles of the array individually to obtain relevant information about the ensemble, it retrieves all information from the diffraction pattern generated by the whole array of particles, which simplifies considerably its implementation in comparison with alternative schemes. The method does not make use of any physical model of scattering to help in the particle characterization, which usually adds computational complexity to the identification process. Because of its reliability and ease of implementation, this work paves the way towards the development of novel smart identification technologies for sample classification and particle contamination monitoring in industrial manufacturing processes.

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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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Coherent Fourier scatterometry for detection of nanometer-sized particles on a planar substrate surface

Cited by 22 publications

References 26 publications

Exploiting evanescent-wave amplification for subwavelength low-contrast particle detection

Exploiting evanescent-wave amplification for subwavelength low-contrast particle detection

Identification of Model Particle Mixtures Using Machine-Learning-Assisted Laser Diffraction

Convolutional neural network applied for nanoparticle classification using coherent scatterometry data

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