Deeply sub-wavelength non-contact optical metrology of sub-wavelength objects

Rendón-Barraza, Carolina; Chan, Eng Aik; Yuan, Guang; Adamo, Giorgio; Pu, Tanchao; Zheludev, Nikolay I.

doi:10.1063/5.0048139

Cited by 15 publications

(16 citation statements)

References 14 publications

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“…We have been enthusiastically developing Deeply Subwavelength Optical Microscopy enabled by neural network‐based artificial intelligence that can see far beyond the diffraction limit. [ 206–208, 59 ]…”

Section: The Future Of Super‐resolution Optical Microscopy At the Age...mentioning

confidence: 99%

“…In numerical experiments, we show that the technique could reach an accuracy beyond λ/1000, which brings it to the atomic scale. [ 59 ]…”

Section: The Future Of Super‐resolution Optical Microscopy At the Age...mentioning

confidence: 99%

“…We have been enthusiastically developing Deeply Subwavelength Optical Microscopy enabled by neural network-based artificial intelligence that can see far beyond the diffraction limit. [206][207][208]59] In conventional microscopy, the image of the object is constructed by a lens. Such imaging has a "diffraction limit" on resolution, which depends on the numerical aperture of the lens, but cannot exceed half of the wavelength of light.…”

Section: The Future Of Super-resolution Optical Microscopy At the Age...mentioning

confidence: 99%

“…[ 58 ] This method provided measurements of the width of sub‐wavelength slits on an opaque screen with an extraordinary accuracy of ≈ λ /130 for a single‐shot measurement or ≈ λ /260 (i.e., 2.4 nm) when combining measurements of diffraction patterns at different distances from the object, thus challenging the accuracy of scanning electron microscopy and ion beam lithography. [ 59 ] Resolution analysis based on the Shannon's theory of information transmission in linear systems in the presence of noise [ 60 ] can be found in Topic 6. It develops earlier work [ 48 ] and incorporates both SNR and a priori knowledge about the object (in the form of sparsity) into the resolution limit determined by the information theory.…”

Section: Introduction To Roadmapmentioning

confidence: 99%

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Roadmap on Label‐Free Super‐Resolution Imaging

Astratov,

Sahel,

Eldar

et al. 2023

Laser & Photonics Reviews

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Label‐free super‐resolution (LFSR) imaging relies on light‐scattering processes in nanoscale objects without a need for fluorescent (FL) staining required in super‐resolved FL microscopy. The objectives of this Roadmap are to present a comprehensive vision of the developments, the state‐of‐the‐art in this field, and to discuss the resolution boundaries and hurdles that need to be overcome to break the classical diffraction limit of the label‐free imaging. The scope of this Roadmap spans from the advanced interference detection techniques, where the diffraction‐limited lateral resolution is combined with unsurpassed axial and temporal resolution, to techniques with true lateral super‐resolution capability that are based on understanding resolution as an information science problem, on using novel structured illumination, near‐field scanning, and nonlinear optics approaches, and on designing superlenses based on nanoplasmonics, metamaterials, transformation optics, and microsphere‐assisted approaches. To this end, this Roadmap brings under the same umbrella researchers from the physics and biomedical optics communities in which such studies have often been developing separately. The ultimate intent of this paper is to create a vision for the current and future developments of LFSR imaging based on its physical mechanisms and to create a great opening for the series of articles in this field.

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Section: The Future Of Super‐resolution Optical Microscopy At the Age...mentioning

confidence: 99%

“…In numerical experiments, we show that the technique could reach an accuracy beyond λ/1000, which brings it to the atomic scale. [ 59 ]…”

Section: The Future Of Super‐resolution Optical Microscopy At the Age...mentioning

confidence: 99%

Section: The Future Of Super-resolution Optical Microscopy At the Age...mentioning

confidence: 99%

Section: Introduction To Roadmapmentioning

confidence: 99%

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Roadmap on Label‐Free Super‐Resolution Imaging

Astratov,

Sahel,

Eldar

et al. 2023

Laser & Photonics Reviews

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“…Deep learning methods have recently been demonstrated as a powerful tool for solving physical and in particular optical problems, for example, for deeply subwavelength optical imaging [30][31][32], analysis of scatterometry data [33,34], enhanced resolution of SEM images [35], MRI image analysis [36,37], inverse design of optical components [4,5] and many other image processing applications, revolutionising their future development. Deep learning methods are machine learning methods employing multilayer (3 and more layers) neural networks where subsequent layers extract finer level features from the raw input data.…”

Section: A Introduction To Neural Network Approachmentioning

confidence: 99%

Convolutional Neural Networks for Mode On-Demand High Finesse Optical Resonator Design

Karpov¹,

Kurdiumov²,

Horák³

2022

Preprint

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We demonstrate the use of machine learning through convolutional neural networks to solve inverse design problems of optical resonator engineering. The neural network finds a harmonic modulation of a spherical mirror to generate a resonator mode with a given target topology ("mode on-demand"). The procedure allows us to optimize the shape of mirrors to achieve a significantly enhanced coupling strength and cooperativity between a resonator photon and a quantum emitter located at the center of the resonator. In a second example, a double-peak mode is designed which would enhance the interaction between two quantum emitters, e.g., for quantum information processing.

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2D Super‐Resolution Metrology Based on Superoscillatory Light

Wang,

Chan,

Rendón‐Barraza

et al. 2024

Advanced Science

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Progress in the semiconductor industry relies on the development of increasingly compact devices consisting of complex geometries made from diverse materials. Precise, label‐free, and real‐time metrology is needed for the characterization and quality control of such structures in both scientific research and industry. However, optical metrology of 2D sub‐wavelength structures with nanometer resolution remains a major challenge. Here, a single‐shot and label‐free optical metrology approach that determines 2D features of nanostructures, is introduced. Accurate experimental measurements with a random statistical error of 18 nm (λ/27) are demonstrated, while simulations suggest that 6 nm (λ/81) may be possible. This is far beyond the diffraction limit that affects conventional metrology. This metrology employs neural network processing of images of the 2D nano‐objects interacting with a phase singularity of the incident topologically structured superoscillatory light. A comparison between conventional and topologically structured illuminations shows that the presence of a singularity with a giant phase gradient substantially improves the retrieval of object information in such an optical metrology. This non‐invasive nano‐metrology opens a range of application opportunities for smart manufacturing processes, quality control, and advanced materials characterization.

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Deeply sub-wavelength non-contact optical metrology of sub-wavelength objects

Cited by 15 publications

References 14 publications

Roadmap on Label‐Free Super‐Resolution Imaging

Roadmap on Label‐Free Super‐Resolution Imaging

Convolutional Neural Networks for Mode On-Demand High Finesse Optical Resonator Design

2D Super‐Resolution Metrology Based on Superoscillatory Light

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