A method of performing phase retrieval and holographic image reconstruction of an imaged sample includes obtaining a single hologram intensity image of the sample using an imaging device. The single hologram intensity image is back-propagated to generate a real input image and an imaginary input image of the sample with image processing software, wherein the real input image and the imaginary input image contain twin-image and/or interference-related artifacts. A trained deep neural network is provided that is executed by the image processing software using one or more processors and configured to receive the real input image and the imaginary input image of the sample and generate an output real image and an output imaginary image in which the twin-image and/or interference-related artifacts are substantially suppressed or eliminated. In some embodiments, the trained deep neural network simultaneously achieves phase-recovery and auto-focusing significantly extending the DOF of holographic image reconstruction.

A method of performing phase retrieval and holographic image reconstruction of an imaged sample includes obtaining a single hologram intensity image of the sample using an imaging device. The single hologram intensity image is back-propagated to generate a real input image and an imaginary input image of the sample with image processing software, wherein the real input image and the imaginary input image contain twin-image and/or interference-related artifacts. A trained deep neural network is provided that is executed by the image processing software using one or more processors and configured to receive the real input image and the imaginary input image of the sample and generate an output real image and an output imaginary image in which the twin-image and/or interference-related artifacts are substantially suppressed or eliminated. In some embodiments, the trained deep neural network simultaneously achieves phase-recovery and auto-focusing significantly extending the DOF of holographic image reconstruction.

A method and an apparatus for camera-free light field video processing with all-optical neural network are disclosed. The method includes: mapping the light field video by a digital micro-mirror device (DMD) and an optical fiber coupler, a two-dimensional 2D spatial optical signal into a one-dimensional 1D input optical signal; realizing a multiply-accumulate computing model in a structure of all-optical recurrent neural network structure, and processing the 1D input signal to obtain the processed signal; and receiving the processed signal and outputting an electronic signal by a photodetector, or receiving the processed signal by a relay optical fiber for relay transmission of the processed signal. The method and system here realize light field video processing without the use of a camera and the whole system is all-optical, thus possessing the advantage in computing speed and energy-efficiency.

A method and an apparatus for camera-free light field video processing with all-optical neural network are disclosed. The method includes: mapping the light field video by a digital micro-mirror device (DMD) and an optical fiber coupler, a two-dimensional 2D spatial optical signal into a one-dimensional 1D input optical signal; realizing a multiply-accumulate computing model in a structure of all-optical recurrent neural network structure, and processing the 1D input signal to obtain the processed signal; and receiving the processed signal and outputting an electronic signal by a photodetector, or receiving the processed signal by a relay optical fiber for relay transmission of the processed signal. The method and system here realize light field video processing without the use of a camera and the whole system is all-optical, thus possessing the advantage in computing speed and energy-efficiency.

A system includes a first unit configured to generate a plurality of modulator control signals, and a processor unit. The processor unit includes: a light source or port configured to provide a plurality of light outputs, and a first set of optical modulators coupled to the light source or port and the first unit. The optical modulators in the first set are configured to generate an optical input vector by modulating the plurality of light outputs provided by the light source or port based on digital input values corresponding to a first set of modulator control signals in the plurality of modulator control signals, the optical input vector comprising a plurality of optical signals. The processor unit also includes a matrix multiplication unit that includes a second set of optical modulators. The matrix multiplication unit is coupled to the first unit, and is configured to transform the optical input vector into an analog output vector based on a plurality of digital weight values corresponding to a second set of modulator control signals in the plurality of modulator control signals applied to the second set of optical modulators. At least one optical modulator of at least one of the first set of optical modulators or the second set of optical modulators is configured to modulate an optical signal based on a first modulator control signal among the plurality of modulator control signals, and the first unit is configured to shape the first modulator control signal to include bandwidth-enhancement associated with a change in amplitude associated with a corresponding change in successive digital values corresponding to the first modulator control signal.

A system includes a first unit configured to generate a plurality of modulator control signals, and a processor unit. The processor unit includes: a light source or port configured to provide a plurality of light outputs, and a first set of optical modulators coupled to the light source or port and the first unit. The optical modulators in the first set are configured to generate an optical input vector by modulating the plurality of light outputs provided by the light source or port based on digital input values corresponding to a first set of modulator control signals in the plurality of modulator control signals, the optical input vector comprising a plurality of optical signals. The processor unit also includes a matrix multiplication unit that includes a second set of optical modulators. The matrix multiplication unit is coupled to the first unit, and is configured to transform the optical input vector into an analog output vector based on a plurality of digital weight values corresponding to a second set of modulator control signals in the plurality of modulator control signals applied to the second set of optical modulators. At least one optical modulator of at least one of the first set of optical modulators or the second set of optical modulators is configured to modulate an optical signal based on a first modulator control signal among the plurality of modulator control signals, and the first unit is configured to shape the first modulator control signal to include bandwidth-enhancement associated with a change in amplitude associated with a corresponding change in successive digital values corresponding to the first modulator control signal.

An optical neural network is constructed based on photonic integrated circuits to perform neuromorphic computing. In the optical neural network, matrix multiplication is implemented using one or more optical interference units, which can apply an arbitrary weighting matrix multiplication to an array of input optical signals. Nonlinear activation is realized by an optical nonlinearity unit, which can be based on nonlinear optical effects, such as saturable absorption. These calculations are implemented optically, thereby resulting in high calculation speeds and low power consumption in the optical neural network.

Techniques related to generating holographic images are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via a wave propagation model. The wave propagation model is used to generate holographic data, which is then adjusted according to one or more constraints associated with the holographic display that will be used to generate a holographic image based on the adjusted holographic data.

Techniques related to generating holographic images are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via a wave propagation model. The wave propagation model is used to generate holographic data, which is then adjusted according to one or more constraints associated with the holographic display that will be used to generate a holographic image based on the adjusted holographic data.

A phototransistor device to act as an artificial photonic synapse includes a substrate and a graphene source-drain channel patterned on the substrate. A perovskite quantum dot layer is formed on the graphene source-drain channel. The perovskite quantum dot layer is methylammonium lead bromide material. A method of operating the phototransistor device as an artificial photonic synapse includes applying a first fixed voltage to a gate of the phototransistor and a second fixed voltage across the graphene source-drain channel. A presynaptic signal is applied as stimuli across the graphene source-drain channel. The presynaptic signal includes one or more pulses of light or electrical voltage. A current across the graphene source-drain channel is measured to represent a postsynaptic signal.