Measurement of Silicon Particles by Laser Surface Scanning and Angle‐Resolved Light Scattering

Huff, Howard R.; Goodall, Randal K.; Williams, Elton; Woo, Keung‐Shan; Liu, Benjamin Y. H.; Warner, Tom A.; Hirleman, Dan; Gildersleeve, K.; Bullis, W Murray; Scheer, Bradley W.; Stover, John C.

doi:10.1149/1.1837392

Cited by 29 publications

(5 citation statements)

References 1 publication

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“…It is a usual phenomenon occured in particle counter with light scattering principle when inspecting the particle other than PSL, calibration particle of the counter due to the different refractive index value. This phenomenon is reported in other studies [3,4,5]. And there are some contaminants in the result.…”

Section: Methodssupporting

confidence: 81%

Study on Uniform Deposition on 300 mm Silicon Wafer with Sub-100 nm Sized Particles for Cleaning Application

Lee

Hong

Oh³

et al. 2021

SSP

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A study for uniform deposition on whole area of wafer was conducted to help check the uniformity of cleaning technology between wafer center to edge. A new method of particle deposition was devised different from the conventional studies using the center nozzle and electric field. Our deposition chamber features wafer rotating method and deposition by the principle of convection and diffusion. In this study, we focused on the effect of wafer rotation speed and rotation number to particle deposition result. After setting the optimum condition, fine results with well deposited shape on whole area of the wafer and outstanding particle size uniformity of more than 70% were obtained. Although particle size shift phenomenon occurred in the measurement result using SP5 due to the intrinsic principle, SEM analysis demonstrated that particles with 60, 80 nm sized silica particles were well deposited on wafer. We believe the standard wafer made by our particle deposition system could be utilized and helpful for performance evaluation and development of wafer cleaning technologies.

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Section: Methodssupporting

confidence: 81%

Study on Uniform Deposition on 300 mm Silicon Wafer with Sub-100 nm Sized Particles for Cleaning Application

Lee

Hong

Oh³

et al. 2021

SSP

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“…Our experiment shows that 50 nm is often confused with 40 nm. The reason is that there is a small difference between the scattering cross sections generated by these two particle sizes (the scattering varies by the sixth power of the diameter [32]). It is clearly visible that most misclassification involves the background class.…”

Section: A Closed Set Classificationmentioning

confidence: 99%

“…We utilize calibrated samples of polystyrene latex (PSL) nanospheres, spin-coated on the silicon wafer, with diameters ranging from 40 to 80 nm to collect the training data. Polystyrene particles are standard for the calibration of surface inspection tools because they have wellcharacterized optical properties (low index of refraction, thus most challenging to detect) and a very tight monodisperse size distribution [32]. Furthermore, for the classification, we study the areas of the wafer where the nanoparticle is absent, contributing to the "background" class.…”

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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“…7 Although CFS was primarily used to measure and reconstruct periodic structures, 8,9 this approach possesses a high potential for the detection of single particles. 10,11 A particle in the focal point of a lens disturbs an incoming wave, even if the particle is of a size below the diffraction-limited resolution. Such a disruption of the wavefront by nanoparticles can be evaluated in the exit pupil plane.…”

Section: Introductionmentioning

confidence: 99%

Detection of geometrical patterns of self-assembled gold nanospheres and top-down fabricated nanostructures using coherent Fourier scatterometry

Krämer¹,

Kroth²,

Sure³

et al. 2023

Optical Measurement Systems for Industrial Inspection XIII

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Detection of nanoparticles gets more and more relevant in various fields of application, e.g., semiconductor technology, medicine or biology. A fast and universal detection method of nanoparticles is essential for these leading-edge technologies. Classical imaging methods struggle with the resolution of nanoparticles smaller than the diffraction limit. Established optical near-field methods are complex and time consuming. We demonstrate that using Coherent Fourier Scatterometry can overcome most of these challenges. For this purpose, we study the optical response of gold nanosphere arrangements on silicon wafers. In particular, we examine arrays of particles arranged in geometrical structures such as lines, squares, triangles, and L-shapes. The dimensions of these structures are in the order or even smaller than the Airy diameter. Correlations between the geometry of the particle arrangement and the intensity distributions in the Fourier plane are characterized by applying different analyzing methods. Thus, it is possible to distinguish between different structures and, furthermore, to extract their geometrical orientations. In addition, gold nanostructures of defined shape, but different sample thicknesses top-down fabricated on silicon are also investigated. These nanostructure shapes studied comprise the same geometries as mentioned above as well as spirals with a diameter comparable to the spot size of the used microscope objective.

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Measurement of Silicon Particles by Laser Surface Scanning and Angle‐Resolved Light Scattering

Cited by 29 publications

References 1 publication

Study on Uniform Deposition on 300 mm Silicon Wafer with Sub-100 nm Sized Particles for Cleaning Application

Study on Uniform Deposition on 300 mm Silicon Wafer with Sub-100 nm Sized Particles for Cleaning Application

Convolutional neural network applied for nanoparticle classification using coherent scatterometry data

Detection of geometrical patterns of self-assembled gold nanospheres and top-down fabricated nanostructures using coherent Fourier scatterometry

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