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公开(公告)号：US20240311628A1
公开(公告)日：2024-09-19
申请号：US18604226
申请日：2024-03-13
申请人： Clerio Vision, Inc.
摘要： Optical elements for diffractive deep neural networks include one or more subsurface layers of diffractive optical elements. An optical element for a diffractive deep neural network includes a substrate and one or more subsurface layers of diffractive optical elements formed within the substrate. The substrate is made from an optical material having a base refractive index. Each of the one or more subsurface layers includes a respective subset of the diffractive optical elements. Each of the diffractive optical elements is formed within a respective sub-volume of the respective subsurface layer via induced changes in refractive index of the optical material to configure the diffractive optical element to function as a neuron in the diffractive deep neural network.
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公开(公告)号：US20240289601A1
公开(公告)日：2024-08-29
申请号：US18589814
申请日：2024-02-28
发明人： Amir HANDELMAN
CPC分类号： G06N3/0675 , G06N3/061
摘要： A system and method for generating a photonic neural network device made of bio-organic materials including: array of optical inputs, light-emitted self-assembly bio-organic fibers and crystals, incorporated diffractive optical element (DOE) in the self-assembly bio-organic fibers and crystals, voltage/current/light controlled in-line polarizers and switches, conductive wires. The system is configured to support light propagation from to input to output, giving each input proper weights in form of different intensity.
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公开(公告)号：US20240289600A1
公开(公告)日：2024-08-29
申请号：US18175970
申请日：2023-02-28
CPC分类号： G06N3/067 , G02B6/29343 , G02B6/29382 , G02B6/29395 , G06E3/008
摘要： Systems and methods are provided for general matrix multiplication using wavelength parallel processing of a photonic tensor core. Examples of the systems and methods disclosed herein include encoding a second matrix into a plurality of optical signals based on a plurality of free spectral ranges (FSRs) of an array of resonator structures, the resonator structures having resonances tuned based on a first matrix. The optical signals can be input into input waveguides optically coupled to the array of resonator structures. A third matrix, representative of the first matrix multiplied by the second matrix, can be generated based on optical power output from the array of resonator structures.
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公开(公告)号：US20240256849A1
公开(公告)日：2024-08-01
申请号：US18633306
申请日：2024-04-11
申请人： Raytheon Company
发明人： Ping Piu KUO
IPC分类号： G06N3/067 , G06N3/0464
CPC分类号： G06N3/0675 , G06N3/0464
摘要： An apparatus includes a frequency comb configured to generate first carrier signals having at least one first frequency spacing. The apparatus also includes multiple modulators each configured to modulate an amplitude of one of the first carrier signals and generate a modulated carrier signal. The apparatus further includes a two-dimensional array of optical couplers configured to perform one-dimensional discrete Fourier transforms in a first direction using the modulated carrier signals. The apparatus also includes an array of coherent detectors and first demultiplexers optically coupled to outputs of the array of optical couplers and to the coherent detectors. The apparatus further includes a local oscillator (LO) bank or array configured to generate second carrier signals having at least one second frequency spacing different from the at least one first frequency spacing. In addition, the apparatus includes second demultiplexers optically coupled to outputs of the LO bank or array and to the coherent detectors.
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公开(公告)号：US20240232617A1
公开(公告)日：2024-07-11
申请号：US18394052
申请日：2023-12-22
申请人： TSINGHUA UNIVERSITY
发明人： Xing Lin , Zhengyang DUAN , Haiou ZHANG , Hang CHEN , Ziyang ZHENG , Hongkai XIONG
摘要： A dual adaptive training method of photonic neural networks (PNN), includes constructing, in a computer, a PNN numerical model including a PNN physical model and a systematic error prediction network model, where the PNN physical model is an error-free ideal PNN physical model of a PNN physical system, the systematic error prediction network model is an error model of the PNN physical system; determining measurement values of the PNN physical system and measurement values of the PNN numerical model, where the measurement values of the PNN physical system include final output values of the PNN physical system, and the measurement values of the PNN numerical model include final output values of the PNN numerical model; determining a similarity loss function based on comparison results between the measurement values of the PNN physical system and the measurement values of the PNN numerical model; determining a task loss function based on fused results of the measurement values of the PNN physical system and the measurement values of the PNN numerical model; and optimizing and updating parameters of the PNN numerical model based on the similarity loss function and the task loss function for in situ training of the PNN physical model.
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公开(公告)号：US20240232604A1
公开(公告)日：2024-07-11
申请号：US18610442
申请日：2024-03-20
发明人： Yichen Shen , Huaiyu Meng , Li Jing , Rumen Dangovski , Peng Xie , Matthew Khoury , Cheng-Kuan Lu , Ronald Gagnon , Maurice Steinman , Jianhua Wu , Arash Hosseinzadeh
IPC分类号： G06N3/067 , G02F1/00 , G02F1/225 , G02F3/02 , G06E1/04 , G06E3/00 , G06F17/14 , G06F17/16 , G06N3/08
CPC分类号： G06N3/0675 , G02F1/00 , G02F3/024 , G06E1/045 , G06E3/005 , G06E3/006 , G06E3/008 , G06F17/14 , G06F17/16 , G06N3/08 , G02F1/225
摘要： Systems and methods that include: providing input information in an electronic format; converting at least a part of the electronic input information into an optical input vector; optically transforming the optical input vector into an optical output vector based on an optical matrix multiplication; converting the optical output vector into an electronic format; and electronically applying a non-linear transformation to the electronically converted optical output vector to provide output information in an electronic format.
In some examples, a set of multiple input values are encoded on respective optical signals carried by optical waveguides. For each of at least two subsets of one or more optical signals, a corresponding set of one or more copying modules splits the subset of one or more optical signals into two or more copies of the optical signals. For each of at least two copies of a first subset of one or more optical signals, a corresponding multiplication module multiplies the one or more optical signals of the first subset by one or more matrix element values using optical amplitude modulation. For results of two or more of the multiplication modules, a summation module produces an electrical signal that represents a sum of the results of the two or more of the multiplication modules.-
公开(公告)号：US20240185051A1
公开(公告)日：2024-06-06
申请号：US18441649
申请日：2024-02-14
发明人： Armaghan ESHAGHI , Mahsa SALMANI , Sreenil SAHA
IPC分类号： G06N3/067
CPC分类号： G06N3/067
摘要： Layers of a neural network can be implemented on an analog computing platform operative to perform MAC operations in series or in parallel in order to cover all elements of an arbitrary size matrix. Embodiments include a convolutional layer, a fully-connected layer, a batch normalization layer, a max pooling layer, an average pooling layer, a ReLU function layer, a sigmoid function layer, as well as concatenations and combinations. Applications include point cloud processing, and in particular of the processing of point clouds obtained as part of a LiDAR application. To implement elements of point cloud data as optical intensities, the data can be linearly translated in order to be fully represented with non-negative values.
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公开(公告)号：US20240115171A1
公开(公告)日：2024-04-11
申请号：US18389997
申请日：2023-12-20
申请人： Spectricity
IPC分类号： A61B5/1455 , A61B5/00 , G06N3/067 , G06N3/08
CPC分类号： A61B5/1455 , A61B5/443 , G06N3/067 , G06N3/08 , A61B2562/0238 , A61B2562/046
摘要： A method for one or more modules of one or more processors of a spectral sensor system begins by receiving a plurality of synthetic spectra, where a synthetic spectrum of the plurality of synthetic spectra includes one or more known deviations from a reference spectrum. The method continues by generating a spectral output for each synthetic spectrum of the plurality of synthetic spectra and then training an artificial intelligence engine, using the combined spectral output to generate a trained neural network. The method then continues by calibrating, based on the trained neural network, a spectral response generated by another spectral sensor.
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公开(公告)号：US20240085600A1
公开(公告)日：2024-03-14
申请号：US18274557
申请日：2022-01-14
申请人： FUJIKURA LTD.
摘要： A light diffraction element unit includes a base material including a light-transmissive and flexible layer member, a light diffraction structure including microcells and disposed on a portion of a main surface of the base material, and a holding part holding the base material and including a layer member or a plate member having an opening that penetrates through a pair of main surfaces of the layer member or the plate member. The holding part holds an annular portion of the base material such that the light diffraction structure is encompassed in the opening. The annular portion surrounds the portion of the main surface of the base material.
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公开(公告)号：US11893479B1
公开(公告)日：2024-02-06
申请号：US18267431
申请日：2021-11-30
发明人： Jingjing Chen , Ruizhen Wu , Ping Huang , Lin Wang
IPC分类号： G06N3/067
CPC分类号： G06N3/067
摘要： A method for realizing a Hadamard product, a device and a storage medium, includes: acquiring a plurality of to-be-treated optical signals with unequal wavelengths; inputting the to-be-treated optical signals into a wavelength division multiplexer; by using the wavelength division multiplexer, feeding the to-be-treated optical signals to a micro-ring-resonator component, wherein the micro-ring-resonator component includes a plurality of micro-ring-resonator groups each of which is formed by two micro-ring resonators with equal radii; and applying a corresponding electric current to the micro-ring-resonator component, to obtain a result of the Hadamard product according to an outputted light intensity.
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