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公开(公告)号:US11656386B2
公开(公告)日:2023-05-23
申请号:US17224338
申请日:2021-04-07
Applicant: Purdue Research Foundation
Inventor: Alexander V. Kildishev , Di Wang , Zhaxylyk A. Kudyshev , Maowen Song , Alexandra Boltasseva , Vladimir M. Shalaev
IPC: G02B5/00 , G11B7/125 , G11B7/1381
CPC classification number: G02B5/008 , G11B7/125 , G11B7/1381
Abstract: A plasmonic system is disclosed. The system includes at least one polarizer that is configured to provide at least one linearly polarized broadband light beam, an anisotropic plasmonic metasurface (APM) assembly having a plurality of nanoantennae each having a predetermined orientation with respect to a global axis representing encoded digital data, the APM assembly configured to receive the at least one linearly polarized broadband light beam and by applying localized surface plasmon resonance reflect light with selectable wavelengths associated with the predetermined orientations of the nanoantennae, and at least one analyzer that is configured to receive the reflected light with selectable wavelength, wherein the relative angles between each of the at least one analyzers and each of the at least one polarizers are selectable with respect to the global axis, thereby allowing decoding of the digital data.
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公开(公告)号:US12159369B2
公开(公告)日:2024-12-03
申请号:US17858722
申请日:2022-07-06
Applicant: Purdue Research Foundation
Inventor: Zhaxylyk A. Kudyshev , Demid Sychev , Zachariah Olson Martin , Simeon I. Bogdanov , Xiaohui Xu , Alexander Kildishev , Alexandra Boltasseva , Vladimir Shalaev
IPC: G06T3/40 , G02B21/00 , G06T3/4046 , G06T3/4053
Abstract: A method of providing super-resolved images of a photon emitting particle is disclosed, which includes providing a machine-learning (ML) platform, wherein the ML platform is configured to receive pixel-based sparse autocorrelation data and generate a predicted super-resolved image of a photon emitting particle, receiving photons from the photon emitting particle by two or more photon detectors, each generating an electrical pulse associated with receiving an incident photon thereon, generating sparse autocorrelation data from the two or more photon detectors for each pixel within an image area, and inputting the pixel-based sparse autocorrelation data to the ML platform, thereby generating a predicted super-resolved image of the imaging area, wherein the resolution of the super-resolved image is improved by √n as compared to a classical optical microscope limited by Abbe diffraction limit.
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公开(公告)号:US20210325577A1
公开(公告)日:2021-10-21
申请号:US17224338
申请日:2021-04-07
Applicant: Purdue Research Foundation
Inventor: Alexander V. Kildishev , Di Wang , Zhaxylyk A. Kudyshev , Maowen Song , Alexandra Boltasseva , Vladimir M. Shalaev
IPC: G02B5/00 , G11B7/125 , G11B7/1381
Abstract: A plasmonic system is disclosed. The system includes at least one polarizer that is configured to provide at least one linearly polarized broadband light beam, an anisotropic plasmonic metasurface (APM) assembly having a plurality of nanoantennae each having a predetermined orientation with respect to a global axis representing encoded digital data, the APM assembly configured to receive the at least one linearly polarized broadband light beam and by applying localized surface plasmon resonance reflect light with selectable wavelengths associated with the predetermined orientations of the nanoantennae, and at least one analyzer that is configured to receive the reflected light with selectable wavelength, wherein the relative angles between each of the at least one analyzers and each of the at least one polarizers are selectable with respect to the global axis, thereby allowing decoding of the digital data.
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公开(公告)号:US20230177642A1
公开(公告)日:2023-06-08
申请号:US17858722
申请日:2022-07-06
Applicant: Purdue Research Foundation
Inventor: Zhaxylyk A. Kudyshev , Demid Sychev , Zachariah Olson Martin , Simeon I. Bogdanov , Xiaohui Xu , Alexander Kildishev , Alexandra Boltasseva , Vladimir Shalaev
CPC classification number: G06T3/4053 , G06T3/4046 , G02B21/0072
Abstract: A method of providing super-resolved images of a photon emitting particle is disclosed, which includes providing a machine-learning (ML) platform, wherein the ML platform is configured to receive pixel-based sparse autocorrelation data and generate a predicted super-resolved image of a photon emitting particle, receiving photons from the photon emitting particle by two or more photon detectors, each generating an electrical pulse associated with receiving an incident photon thereon, generating sparse autocorrelation data from the two or more photon detectors for each pixel within an image area, and inputting the pixel-based sparse autocorrelation data to the ML platform, thereby generating a predicted super-resolved image of the imaging area, wherein the resolution of the super-resolved image is improved by √n as compared to a classical optical microscope limited by Abbe diffraction limit.
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公开(公告)号:US20190353830A1
公开(公告)日:2019-11-21
申请号:US16411038
申请日:2019-05-13
Applicant: Purdue Research Foundation
Inventor: Alexander V. Kildishev , Di Wang , Zhaxylyk A. Kudyshev , Maowen Song , Alexandra Boltasseva , Vladimir M. Shalaev
IPC: G02B5/00 , G11B7/1381 , G11B7/125
Abstract: A plasmonic system is disclosed. The system includes at least one polarizer that is configured to provide at least one linearly polarized broadband light beam, an anisotropic plasmonic metasurface (APM) assembly having a plurality of nanoantennae each having a predetermined orientation with respect to a global axis representing encoded digital data, the APM assembly configured to receive the at least one linearly polarized broadband light beam and by applying localized surface plasmon resonance reflect light with selectable wavelengths associated with the predetermined orientations of the nano antennae, and at least one analyzer that is configured to receive the reflected light with selectable wavelength, wherein the relative angles between each of the at least one analyzers and each of the at least one polarizers are selectable with respect to the global axis, thereby allowing decoding of the digital data.
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