Patent classifications
G03H2240/24
SYSTEM, APPARATUS AND METHOD FOR EXTRACTING THREE-DIMENSIONAL INFORMATION OF AN OBJECT FROM RECEIVED ELECTROMAGNETIC RADIATION
An apparatus and method to produce a hologram of an object includes an electromagnetic radiation assembly configured to receive a received electromagnetic radiation, such as light, from the object. The electromagnetic radiation assembly is further configured to diffract the received electromagnetic radiation and transmit a diffracted electromagnetic radiation. An image capture assembly is configured to capture an image of the diffracted electromagnetic radiation and produce the hologram of the object from the captured image.
System, apparatus and method for extracting three-dimensional information of an object from received electromagnetic radiation
An apparatus and method to produce a hologram of an object includes an electromagnetic radiation assembly configured to receive a received electromagnetic radiation, such as light, from the object. The electromagnetic radiation assembly is further configured to diffract the received electromagnetic radiation and transmit a diffracted electromagnetic radiation. An image capture assembly is configured to capture an image of the diffracted electromagnetic radiation and produce the hologram of the object from the captured image.
Devices and methods employing optical-based machine learning using diffractive deep neural networks
An all-optical Diffractive Deep Neural Network (D.sup.2NN) 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 D.sup.2NNs 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 D.sup.2NNs. In alternative embodiments, the all-optical D.sup.2NN is used as a front-end in conjunction with a trained, digital neural network back-end.
Nanovoided holographic structures and corresponding systems and methods
An interference structure having a nanovoided hologram material is described. The nanovoided hologram material may have an index of refraction difference of approximately 0.4. The interference structure may include about 10% to 90% nanovoids by volume. The interference structure may be formed using a mixture of a monomer, an initiator, and solvent. The mixture may be disposed on a substrate and irradiated with two sources of light spaced apart from each other and shining on the same region of the mixture to generate an interference pattern in the mixture, leading to the selective polymerization of regions of the mixture where there is constructive interference of light. Various other devices, methods, and systems are also disclosed.
Apodization of refractive index profile in volume gratings
A grating coupler may be fabricated by exposing a photopolymer layer to grating forming light for forming periodic refractive index variations in the photopolymer layer. The photopolymer layer may be exposed to apodization light for reducing an amplitude of the periodic refractive index variations in a spatially-selective manner. The apodization may also be achieved or facilitated by subjecting outer surface(s) of the photopolymer layer to a chemically reactive agent that causes the refractive index contrast to be reduced near the surface(s) of application. The apodized refractive index profile of the gratings facilitates the reduction of optical crosstalk between different gratings of the grating coupler.
APODIZATION OF REFRACTIVE INDEX PROFILE IN VOLUME GRATINGS
A grating coupler may be fabricated by exposing a photopolymer layer to grating forming light for forming periodic refractive index variations in the photopolymer layer. The photopolymer layer may be exposed to apodization light for reducing an amplitude of the periodic refractive index variations in a spatially-selective manner. The apodization may also be achieved or facilitated by subjecting outer surface(s) of the photopolymer layer to a chemically reactive agent that causes the refractive index contrast to be reduced near the surface(s) of application. The apodized refractive index profile of the gratings facilitates the reduction of optical crosstalk between different gratings of the grating coupler.
Method for obtaining full-color hologram optical element using photopolymer, and head-up display apparatus with the same
A method of manufacturing a full-color holographic optical element in a full-color holographic optical element manufacturing apparatus including a lens and a holographic recording medium located farther away than a focal length of the lens, the method including: allowing a signal beam including a mixture of laser beams having wavelengths of R (Red), G (Green), and B (Blue) to be incident on the lens; and recording a hologram in such a manner that a reference beam including a mixture of laser beams having wavelengths of R, G, and B is allowed to be incident on the holographic recording medium, wherein the holographic recording medium is configured with a single medium.
DEVICES AND METHODS EMPLOYING OPTICAL-BASED MACHINE LEARNING USING DIFFRACTIVE DEEP NEURAL NETWORKS
An all-optical Diffractive Deep Neural Network (D.sup.2NN) 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 D.sup.2NNs 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 D.sup.2NNs. In alternative embodiments, the all-optical D.sup.2NN is used as a front-end in conjunction with a trained, digital neural network back-end.
System, apparatus and method for extracting three-dimensional information of an object from received electromagnetic radiation
An apparatus and method to produce a hologram of an object includes an electromagnetic radiation assembly configured to receive a received electromagnetic radiation, such as light, from the object. The electromagnetic radiation assembly is further configured to diffract the received electromagnetic radiation and transmit a diffracted electromagnetic radiation. An image capture assembly is configured to capture an image of the diffracted electromagnetic radiation and produce the hologram of the object from the captured image.
HOLOGRAPHIC VIEWING DEVICE, AND HOLOGRAPHIC VIEWING CARD INCORPORATING IT
The invention relates to a holographic viewing device that enable printing or the like to be directly applied to a transmission hologram substrate without recourse to any frame for supporting and reinforcing a transmission hologram, thereby simplifying construction while enhancing aesthetic and decorative attributes, and a holographic viewing card incorporating it. The holographic viewing device enables a given image or message to be viewed near the positions of point light sources upon viewing the point light sources through a hologram, and comprises a transparent substrate 41, a hologram-formation layer 42 and a printing layer 45. The hologram-formation layer 42 may be any one of a phase type diffractive optical element having a relief structure 43 on its surface, a phase type diffractive optical element having a refractive index profile in its layer, and an amplitude type diffractive optical element having a transmittance profile in its layer.