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    PULSE SHAPING USING DIFFRACTIVE NETWORK DEVICE WITH MODULAR DIFFRACTIVE LAYERS
    2.
    发明公开
    PULSE SHAPING USING DIFFRACTIVE NETWORK DEVICE WITH MODULAR DIFFRACTIVE LAYERS 审中-公开

    公开(公告)号：US20230251189A1

    公开(公告)日：2023-08-10

    申请号：US18010207

    申请日：2021-06-28

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Deniz Mengu ,  Yair Rivenson ,  Muhammed Veli

    IPC: G01N21/3586 ,  G02B5/18

    CPC classification number: G01N21/3586 ,  G02B5/1847

    Abstract: A diffractive network is disclosed that utilizes, in some embodiments, diffractive elements, which are used to shape an arbitrary broadband pulse into a desired optical waveform, forming a compact and passive pulse engineering system. The diffractive network was experimentally shown to generate various different pulses by designing passive diffractive layers that collectively engineer the temporal waveform of an input terahertz pulse. The results constitute the first demonstration of direct pulse shaping in terahertz spectrum, where the amplitude and phase of the input wavelengths are independently controlled through a passive diffractive device, without the need for an external pump. Furthermore, a modular physical transfer learning approach is presented to illustrate pulse-width tunability by replacing part of an existing diffractive network with newly trained diffractive layers, demonstrating its modularity. This learning-based diffractive pulse engineering framework can find broad applications in e.g., communications, ultra-fast imaging and spectroscopy.

    DIFFRACTIVE DEEP NEURAL NETWORKS WITH DIFFERENTIAL AND CLASS-SPECIFIC DETECTION
    3.
    发明申请
    DIFFRACTIVE DEEP NEURAL NETWORKS WITH DIFFERENTIAL AND CLASS-SPECIFIC DETECTION 有权

    公开(公告)号：US20220327371A1

    公开(公告)日：2022-10-13

    申请号：US17616983

    申请日：2020-06-05

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Jingxi Li ,  Deniz Mengu ,  Yi Luo

    IPC: G06N3/067 ,  G06N3/08 ,  G06N20/00 ,  G02B27/42

    Abstract: A diffractive optical neural network device includes a plurality of diffractive substrate layers arranged in an optical path. The substrate layers are formed with physical features across surfaces thereof that collectively define a trained mapping function between an optical input and an optical output. A plurality of groups of optical sensors are configured to sense and detect the optical output, wherein each group of optical sensors has at least one optical sensor configured to capture a positive signal from the optical output and at least one optical sensor configured to capture a negative signal from the optical output. Circuitry and/or computer software receives signals or data from the optical sensors and identifies a group of optical sensors in which a normalized differential signal calculated from the positive and negative optical sensors within each group is the largest or the smallest of among all the groups.

    DEVICES AND METHODS EMPLOYING OPTICAL-BASED MACHINE LEARNING USING DIFFRACTIVE DEEP NEURAL NETWORKS
    4.
    发明申请
    DEVICES AND METHODS EMPLOYING OPTICAL-BASED MACHINE LEARNING USING DIFFRACTIVE DEEP NEURAL NETWORKS 有权

    公开(公告)号：US20210142170A1

    公开(公告)日：2021-05-13

    申请号：US17046293

    申请日：2019-04-12

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Xing Ling ,  Deniz Mengu ,  Yi Luo

    IPC: G06N3/08 ,  G02B5/18 ,  G02B27/42 ,  G06N3/04 ,  G06K9/62 ,  G06K9/00 ,  G06K9/20

    Abstract: An all-optical Diffractive Deep Neural Network (D2NN) architecture learns to implement various functions or tasks after deep learning-based design of the passive diffractive or reflective substrate layers that work collectively to perform the desired function or task. This architecture was successfully confirmed experimentally by creating 3D-printed D2NNs that learned to implement handwritten classifications and lens function at the terahertz spectrum. This all-optical deep learning framework can perform, at the speed of light, various complex functions and tasks that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D2NNs. In alternative embodiments, the all-optical D2NN is used as a front-end in conjunction with a trained, digital neural network back-end.

    MISALIGNMENT-RESILIENT DIFFRACTIVE OPTICAL NEURAL NETWORKS
    7.
    发明公开
    MISALIGNMENT-RESILIENT DIFFRACTIVE OPTICAL NEURAL NETWORKS 审中-公开

    公开(公告)号：US20230162016A1

    公开(公告)日：2023-05-25

    申请号：US17920778

    申请日：2021-05-21

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Deniz Mengu ,  Yair Rivenson

    IPC: G06N3/067

    CPC classification number: G06N3/067

    Abstract: A diffractive optical neural network includes one more layers that are resilient to misalignments, fabrication-related errors, detector noise, and/or other sources of error. A diffractive optical neural network model is first trained with a computing device to perform a statistical inference task such as image classification (e.g., object classification). The model is trained using images or training optical signals along with random misalignments of the plurality of layers, fabrication-related errors, input plane or output plane misalignments, and/or detector noise, followed by computing an optical output of the diffractive optical neural network model through optical transmission and/or reflection resulting from the diffractive optical neural network and iteratively adjusting complex-valued transmission and/or reflection coefficients for each layer until optimized transmission/reflection coefficients are obtained. Once the model is optimized, the physical embodiment of the diffractive optical neural network is manufactured.

    DIFFRACTIVE ALL-OPTICAL COMPUTING FOR QUANTITATIVE PHASE IMAGING
    8.
    发明申请
    DIFFRACTIVE ALL-OPTICAL COMPUTING FOR QUANTITATIVE PHASE IMAGING 有权

    公开(公告)号：US20250138296A1

    公开(公告)日：2025-05-01

    申请号：US18721509

    申请日：2023-01-13

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Deniz Mengu

    IPC: G02B21/14 ,  G02B27/42 ,  G02B27/52

    Abstract: Quantitative phase imaging (QPI) is a label-free computational imaging technique that provides optical path length information of objects. Here, a diffractive QPI network architecture is disclosed that can synthesize the quantitative phase image of an object by converting the input phase information of a scene or object(s) into intensity variations at the output plane. A diffractive QPI network is a specialized all-optical device designed to perform a quantitative phase-to-intensity transformation through passive diffractive/reflective surfaces that are spatially engineered using deep learning and image data. Forming a compact, all-optical network that axially extends only ˜200-300λ (λ=illumination wavelength), this framework replaces traditional QPI systems and related digital computational burdens with a set of passive substrate layers. All-optical diffractive QPI networks can potentially enable power-efficient, high frame-rate and compact phase imaging systems that might be useful for various applications, including, e.g., on-chip microscopy and sensing.

    SCALE-, SHIFT-, AND ROTATION-INVARIANT DIFFRACTIVE OPTICAL NETWORKS
    9.
    发明公开
    SCALE-, SHIFT-, AND ROTATION-INVARIANT DIFFRACTIVE OPTICAL NETWORKS 审中-公开

    公开(公告)号：US20230401436A1

    公开(公告)日：2023-12-14

    申请号：US18249726

    申请日：2021-10-22

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Deniz Mengu ,  Yair Rivenson

    IPC: G06N3/067 ,  G06N3/084 ,  G06N3/045

    CPC classification number: G06N3/067 ,  G06N3/084 ,  G06N3/045 ,  G02B1/002

    Abstract: A method of forming an optical neural network for processing an input object image or optical signal that is invariant to object transformations includes training a software-based neural network model to perform one or more specific optical functions for a multi-layer optical network having physical features located in each of the layers of the optical neural network. The training includes feeding different input object images or optical signals that have random transformations or shifts and computing at least one optical output of optical transmission and/or reflection through the optical neural network using an optical wave propagation model and iteratively adjusting transmission/reflection coefficients for each layer until optimized transmission/reflection coefficients are obtained. A physical embodiment of the optical neural network is then made that has a plurality of substrate layers having physical features that match the optimized transmission/reflection coefficients obtained by the trained neural network model.

    MACHINE VISION USING DIFFRACTIVE SPECTRAL ENCODING
    10.
    发明公开
    MACHINE VISION USING DIFFRACTIVE SPECTRAL ENCODING 审中-公开

    公开(公告)号：US20230153600A1

    公开(公告)日：2023-05-18

    申请号：US17920774

    申请日：2021-05-04

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Jingxi Li ,  Deniz Mengu ,  Yair Rivenson

    IPC: G06N3/067 ,  G02B27/42

    CPC classification number: G06N3/067 ,  G02B27/4272

    Abstract: A machine vision task, machine learning task, and/or classification of objects is performed using a diffractive optical neural network device. Light from objects passes through or reflects off the diffractive optical neural network device formed by multiple substrate layers. The diffractive optical neural network device defines a trained function between an input optical signal from the object light illuminated at a plurality or a continuum of wavelengths and an output optical signal corresponding to one or more unique wavelengths or sets of wavelengths assigned to represent distinct data classes or object types/classes created by optical diffraction and/or reflection through/off the substrate layers. Output light is captured with detector(s) that generate a signal or data that comprise the one or more unique wavelengths or sets of wavelengths assigned to represent distinct data classes or object types or object classes which are used to perform the task or classification.

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