IP Library › Granted Patent US 12,517,161
Granted Patent B2
US 12,517,161 · App. 19/169,371 · Granted Jan 6, 2026

Characterizing radio frequency sources using vapor cell sensors

Inventors: Mohammad Noaman (Waterloo, CA); Kent Arnold Nickerson (Waterloo, CA); Donald Booth (Kitchener, CA); James P. Shaffer (Kitchener, CA)
Assignee: Quantum Valley Ideas Laboratories
G01R29/0885
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,517,161
App. No.
19/169,371
Granted
Jan 6, 2026
Kind
B2
Abstract

In a general aspect, vapor cells are used to characterize radio frequency sources. In some aspects, a system includes a vapor cell sensor system and a signal processing system. The vapor cell sensor system has one or more vapor cells that can be positioned at an array of locations. The vapor cell sensor system generates output signals based on operating the one or more vapor cells in a radiating near-field region of an RF field. The signal processing system is configured to generate measurement data based on the output signals; the measurement data represents measured intensities of the RF field in the radiating near-field region. The signal processing system is also configured to generate a model of the RF field based on the measurement data. The model includes modeled intensities of the RF field in the radiating near-field region and a far-field region of the RF field.

Claims (63)

1 . A system comprising:

a vapor cell sensor system comprising one or more vapor cells that are configured to be positioned at an array of locations, the vapor cell sensor system configured to generate output signals based on operating the one or more vapor cells at the array of locations in a radiating near-field region of an RF field produced by an RF source; and

a signal processing system configured to perform operations that comprise:

generating, based on the output signals, measurement data representing measured intensities of the RF field in the radiating near-field region; and

generating, based on the measurement data, a model of the RF field produced by the RF source, the model comprising modeled intensities of the RF field in the radiating near-field region and a far-field region of the RF field.

2 . The system of claim 1 ,

wherein the array of locations corresponds to first and second measurement domains in a Fresnel region of the RF field, the Fresnel region being part of the radiating near-field region; and

wherein the measurement data represent measured intensities of the RF field on the first and second measurement domains.

3 . The system of claim 2 ,

wherein the first measurement domain corresponds to a first surface, and the second measurement domain corresponds to a second surface; and

wherein the first and second surfaces are separated by a distance greater than or equal to a wavelength of the RF field.

4 . The system of claim 3 , wherein the first and second surfaces are planar surfaces.

5 . The system of claim 3 , wherein the first and second surfaces are planar surfaces that are parallel to each other.

6 . The system of claim 3 ,

wherein generating the model comprises projecting the measured intensities to a third surface in a Fraunhofer region of the RF field, the Fraunhofer region being part of the far-field region; and

wherein the model comprises modeled intensities of the RF field at a plurality of locations on the third surface.

7 . The system of claim 3 ,

wherein the model comprises modeled phases of the RF field on the first and second surfaces; and

wherein generating the model comprises using a phase retrieval algorithm to generate the modeled phases based on the measurement data.

8 . The system of claim 7 , wherein generating the model comprises:

selecting boundary data comprising the measured intensities and the modeled phases of the RF field on at least one of the first and second surfaces; and

using an RF diffraction model to generate the modeled intensities of the RF field in one or both of the radiating near-field region and the far-field region based on the boundary data.

9 . The system of claim 1 , wherein the model comprises modeled intensities of the RF field at a plurality of points that represent an emission pattern of the RF source.

10 . The system of claim 1 , wherein the one or more vapor cells is a first vapor cell, and the vapor cell sensor system comprises an actuator system that is configured to move the first vapor cell to each location in the array of locations.

11 . The system of claim 1 , wherein the one or more vapor cells is a plurality of vapor cells, and the vapor cell sensor system comprises a mount that is configured to hold the plurality of vapor cells at respective locations of at least a portion of the array of locations.

12 . The system of claim 1 , wherein the one or more vapor cells are each configured to generate optical signals in response to receiving laser signals from a laser system, the optical signals based on transmission of the laser signals through a vapor of the vapor cell, the vapor configured to alter a transmission of the laser signals in response to interacting with the RF field produced by the RF source.

13 . The system of claim 12 ,

wherein the vapor cell sensor system comprises an optical detector that is configured to generate the output signals in response to receiving the optical signals from the one or more vapor cells, the optical signals having respective intensities that are represented by the output signals; and

wherein the signal processing system is configured to receive the output signals from the optical detector.

14 . A method comprising:

generating output signals by operating one or more vapor cells of a vapor cell sensor system at an array of locations in a radiating near-field region of an RF field, the RF field produced by an RF source; and

by operation of a signal processing system:

generating, based on the output signals, measurement data representing measured intensities of the RF field in the radiating near-field region, and

generating, based on the measurement data, a model of the RF field produced by the RF source, the model comprising modeled intensities of the RF field in the radiating near-field region and a far-field region of the RF field.

15 . The method of claim 14 ,

wherein the array of locations corresponds to first and second measurement domains in a Fresnel region of the RF field, the Fresnel region being part of the radiating near-field region; and

wherein the measurement data represent measured intensities of the RF field on the first and second measurement domains.

16 . The method of claim 15 ,

wherein the first measurement domain corresponds to a first surface, and the second measurement domain corresponds to a second surface; and

wherein the first and second surfaces are separated by a distance greater than or equal to a wavelength of the RF field.

17 . The method of claim 16 , wherein the first and second surfaces are planar surfaces.

18 . The method of claim 16 , wherein the first and second surfaces are planar surfaces that are parallel to each other.

19 . The method of claim 16 ,

wherein generating the model comprises projecting the measured intensities to a third surface in a Fraunhofer region of the RF field, the Fraunhofer region being part of the far-field region; and

wherein the model comprises modeled intensities of the RF field at a plurality of locations on the third surface.

20 . The method of claim 16 ,

wherein the model comprises modeled phases of the RF field on the first and second surfaces; and

wherein generating the model comprises using a phase retrieval algorithm to generate the modeled phases based on the measurement data.

21 . The method of claim 20 , wherein generating the model comprises:

selecting boundary data comprising the measured intensities and the modeled phases of the RF field on at least one of the first and second surfaces; and

using an RF diffraction model to generate the modeled intensities of the RF field in one or both of the radiating near-field region and the far-field region based on the boundary data.

22 . The method of claim 14 , wherein the model comprises modeled intensities of the RF field at a plurality of points that represent an emission pattern of the RF source.

23 . The method of claim 14 ,

wherein the one or more vapor cells is a first vapor cell;

wherein the vapor cell sensor system comprises an actuator system that is configured to move the first vapor cell; and

wherein the method comprises moving, by operation of the actuator system, the first vapor cell to each location in the array of locations.

24 . The method of claim 14 , wherein the one or more vapor cells is a plurality of vapor cells, and the vapor cell sensor system comprises a mount that is configured to hold the plurality of vapor cells at respective locations of at least a portion of the array of locations.

25 . The method of claim 14 , wherein generating the output signals comprises:

generating, by operation of each vapor cell, respective optical signals in response to receiving laser signals at the vapor cell from a laser system, the respective optical signals based on transmission of the laser signals through a vapor of the vapor cell, the vapor configured to alter a transmission of the laser signals in response to interacting with the RF field produced by the RF source.

26 . The method of claim 25 ,

wherein the vapor cell sensor system comprises an optical detector;

wherein generating the output signals comprises generating, by operation of the optical detector, the output signals in response to receiving the optical signals at the optical detector from the one or more vapor cells, the optical signals having respective intensities that are represented by the output signals; and

wherein the method comprises receiving the output signals at the signal processing system from the optical detector.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 4, 2025
From: NOAMAN, MOHAMMAD; NICKERSON, KENT ARNOLD; BOOTH, DONALD; SHAFFER, JAMES P.
To: QUANTUM VALLEY IDEAS LABORATORIES
Reel/Frame 070736/0707 →
Continuity (2)
Provisional Application 63575170 · Apr 5, 2024
Related Publication 20250314683A1 · Oct 9, 2025
References Cited (65)
US 9970973B2 · Anderson et al. · 2018 [cited by applicant]
US 10509065B1 · Shaffer · 2019 [cited by applicant]
US 10564201B1 · Shaffer · 2020 [cited by applicant]
US 10605840B1 · Amarloo et al. · 2020 [cited by applicant]
US 10802066B1 · Keaveney et al. · 2020 [cited by applicant]
US 10859981B1 · Ramirez-serrano et al. · 2020 [cited by applicant]
US 11054453B2 · Amarloo et al. · 2021 [cited by applicant]
US 11112298B2 · Amarloo et al. · 2021 [cited by applicant]
US 11209473B2 · Amarloo et al. · 2021 [cited by applicant]
US 11300599B1 · Amarloo · 2022 [cited by applicant]
US 11366430B2 · Ramirez-serrano et al. · 2022 [cited by applicant]
US 11681016B1 · Bohaichuk et al. · 2023 [cited by applicant]
US 11874311B1 · Dixon et al. · 2024 [cited by applicant]
US 11885842B1 · Pandiyan et al. · 2024 [cited by applicant]
US 11885904B2 · Bohaichuk et al. · 2024 [cited by applicant]
US 11899051B1 · Pandiyan · 2024 [cited by applicant]
US 20070247241A1 · Braun · 2007 [cited by examiner]
US 20160363617A1 · Anderson · 2016 [cited by examiner]
US 20190187198A1 · Anderson · 2019 [cited by examiner]
US 20210250101A1 · Gordon · 2021 [cited by examiner]
US 20210286063A1 · Amarloo · 2021 [cited by applicant]
US 20220196716A1 · Anderson et al. · 2022 [cited by applicant]
US 20220252685A1 · Popescu · 2022 [cited by applicant]
US 20220276293A1 · Compton et al. · 2022 [cited by applicant]
US 20220326289A1 · Anderson et al. · 2022 [cited by applicant]
US 20230152362A1 · Tilles · 2023 [cited by applicant]
US 20230296658A1 · Bohaichuk et al. · 2023 [cited by applicant]
CA 3085935 · 2019 [cited by applicant]
CA 3157522 · 2021 [cited by applicant]
CA 3159327 · 2021 [cited by applicant]
CA 3178752 · 2021 [cited by applicant]
Holloway, Christopher L., et al. “Atom-based RF electric field metrology: from self-calibrated measurements to subwavelength and near-field imaging.” IEEE Transactions on Electromagnetic Compatibility 59.2 (2017): 717-7… [cited by examiner]
Abbey, et al., “Keyhole coherent diffractive imaging”, Nature Physics, Mar. 9, 2008, 6 pages. [cited by applicant]
Bauschke, et al., “Phase retrieval, error reduction algorithm, and Fienup variants: a view from convex optimization”, J. Opt. Soc. Am. A/vol. 19, No. 7/Jul. 2002, 1334-1345, 12 pages. [cited by applicant]
Bohaichuk, et al., “A Three-Photon Rydberg Atom-Based Radio Frequency Sensing Scheme with Narrow Linewidth”, arXiv:2304.07409 [physics.atom-ph] (2023), 2023, 11 pages. [cited by applicant]
Bohaichuk, et al., “The Origins of Rydberg Atom Electrometer Transient Response and its Impact on Radio Frequency Pulse Sensing”, arXiv:2203.01733, Mar. 3, 2022, 21 pgs. [cited by applicant]
Chen, et al., “Advances in Phase Retrieval by Transport of Intensity Equation”, Proceedings of the 7th International Conference on Photonics, Optics and Laser Technology (PHOTOPTICS 2019), pp. 168-173, 2019, 6 pages. [cited by applicant]
Chen, et al., “Multiple wavelength diffractive imaging”, Physical Review A 79, 023809 (2009), Feb. 6, 2009, 4 pages. [cited by applicant]
Chubar, et al., “Memory and CPU efficient computation of the Fresnel free-space propagator in Fourier optics simulations”, Optics Express 28750, vol. 27, No. 20, Sep. 23, 2019, 10 pages. [cited by applicant]
Dixon, Katelyn, “Rydberg atom-based Electrometry Using a Self-Heterodyne Frequency Comb Readout and Preparation Scheme”, Physical Review Applied 19, 034078 (2023)., 2023, 8 Pages. [cited by applicant]
Eisebitt, et al., “Lensless imaging of magnetic nanostructures by X-ray spectro-holography”, Nature vol. 432, 885-888, Dec. 16, 2004, 4 pages. [cited by applicant]
Fan, et al., “Atom based RF electric field sensing”, Journal of Physics B: Atomic, Molecular and Optical Physics 48 202001 (2015), Sep. 9, 2015, 17 pgs. [cited by applicant]
Fienup, Jr. , “Phase retrieval algorithms: a comparison”, Applied Optics, vol. 21, 2758-2769, Aug. 1, 1982, 12 pages. [cited by applicant]
Fienup, Jr. , “Reconstruction of an object from the modulus of its Fourier transform”, Optics Letters, vol. 3, No. 1, Jul. 1978, 27-29, 3 pages. [cited by applicant]
Gerchberg, et al., “A practical algorithm for the determination of the phase from image and diffraction plane pictures”, Optik 35, 1972, 6 pages. [cited by applicant]
Goodman, “Introduction to Fourier Optics”, McGraw-Hill Companies, Inc., 1968, 457 pgs. [cited by applicant]
Kelly, et al., “Numerical calculation of the Fresnel transform”, J. Opt. Soc. Am., vol. 31, No. 4, 755-764, 2014, 10 pages. [cited by applicant]
Noaman, et al., “Rydberg-Atom Sensors in Bichromatic Radio-Frequency Fields”, Phys. Rev. Applied 20, 024068., Aug. 28, 2023, 6 pages. [cited by applicant]
Noaman, et al., “Vapor Cell Characterization and Optimization for Applications in Rydberg Atom-Based Radio Frequency Sensing”, Proceedings vol. 12447, Quantum Sensing, Imaging, and Precision Metrology; 124470V (2023), M… [cited by applicant]
Pham, Minh, “New Algorithms in Computational Microscopy”, Ph.D. thesis, UCLA, 2020, 126 pages. [cited by applicant]
Rensburg, Danielj. , “Limitations of Near-Field Back Projection for Phased Array Tuning Applications”, 5 pages. [cited by applicant]
Rodriguez, et al., “Oversampling smoothness (OSS): an effective algorithm for phase retrieval of noisy diffraction intensities”, J Appl Crystallography 46(Pt 2), 312 (2013)., 13 pages. [cited by applicant]
Schmidt, et al., “Rydberg atom-based radio frequency sensors: amplitude regime sensing”, arXiv:2307.00121v1 [physics.atom-ph], retrieved on Jun. 30, 2023, 33 pages. [cited by applicant]
Schwettmann, et al., “Field-programmable gate array based locking circuit for external cavity diode laser frequency stabilization”, Rev.Sci.Instrum. 82, Oct. 7, 2011, 7 pgs. [cited by applicant]
Sedlacek, et al., “Atom Based Vector Microwave Electrometry Using Rubidium Rydberg Atoms in a Vapor Cell”, arXiv:1304.4299v1 [physics.atom-ph], Apr. 15, 2023, 5 pages. [cited by applicant]
Sedlacek, et al., “Microwave electrometry with Rydberg atoms in a vapour cell using bright atomic resonances”, Nature Physics, vol. 8, 819-824 (2012), Sep. 16, 2012, 6 pgs. [cited by applicant]
Shaffer, et al., “A read-out enhancement for microwave electric field sensing with Rydberg atoms”, Proc. SPIE vol. 10674, Quantum Technologies 2018, 2018, 12 pgs. [cited by applicant]
Shapira, et al., “Complete Modal Decomposition for Optical Waveguides”, Physical Review Letters 94, 143902, Apr. 14, 2005, 4 pages. [cited by applicant]
Shechtman, et al., “Phase Retrieval with Application to Optical Imaging”, arXiv:1402.7350v1, accessed Feb. 28, 2014, 25 pages. [cited by applicant]
Shechtman, et al., “Sparsity-based super-resolution and phase-retrieval in waveguide arrays”, Optics Express 24015, Oct. 1, 2013, 11 pages. [cited by applicant]
Williams, et al., “Fresnel Coherent Diffractive Imaging”, Physical Review Letters 97, 025506, Jul. 14, 2006, 5 pages. [cited by applicant]
Yoshida, et al., “On the Sample Complexity of Phase-Retrieval Receiver Based on 2-D Arrayed Photodetectors”, OFC 2020, 2020, 3 pages. [cited by applicant]
USPTO, Non-Final Office Action issued in U.S. Appl. No. 19/169,418 on Jul. 25, 2025, 34 pages. [cited by applicant]
ISA, International Search Report and Written Opinion issued in Application No. PCT/CA2025/050488 on Aug. 5, 2025, 11 pages. [cited by applicant]
ISA, International Search Report and Written Opinion issued in Application No. PCT/CA2025/050489 on Aug. 5, 2025, 8 pages. [cited by applicant]