241 results about "Optical neural network" patented technology

The invention discloses an optimization method of an optical diffraction neural network, which is characterized in that a multi-target loss function is designed in combination with classification reasoning performance and diffraction efficiency performance, and diffraction layer phase parameter distribution of the optical neural network is jointly optimized, so that the diffraction efficiency of the trained optical diffraction neural network is greatly improved. Compared with a traditional cross entropy loss function training method, the method has the advantages that the loss function of the diffraction efficiency performance is added on the basis of the cross entropy loss function of the original classification performance, and the diffraction efficiency of the optical diffraction neural network is greatly improved. The trained optical diffraction neural network alleviates the problem of spurious distribution of an output plane field, has high signal contrast, enhances the robustness of the system, and further promotes the application of the optical diffraction neural network in an actual image classification scene.

An all-optical neural network that utilizes light beams and optical components to implement layers of the neural network is disclosed herein. The all-optical neural network includes an input layer, zero or more hidden layers, and an output layer. Each layer of the neural network is configured to simulate linear and nonlinear operations of a conventional artificial neural network neuron on an optical signal. In an embodiment, the optical linear operation is performed by a spatial light modulator and an optical lens. The optical lens performs a Fourier transformation on the set of light beams and sums light beams with similar propagation orientations. The optical nonlinear operation is implemented utilizing a nonlinear optical medium having an electromagnetically induced transparency characteristic whose transmission of a probe beam of light is controlled by the intermediate output of a coupling beam of light from the optical linear operation.

A neuromorphic optical computing architecture system includes: a multi-channel representation module, configured to encode, via a multi-spectral laser, an originally inputted target light field signal into coherent light having different wavelengths; an attention-aware optical neural network module including a bottom-up (BU) optical attention module and a top-down (TD) optical attention module, in which the coherent light having different wavelengths is input to the BU optical attention module and network training is performed on an attention-aware optical neural network, and the TD optical attention module performs, based on the trained attention-aware optical neural network, spectral and spatial transmittance modulation of multi-dimensional sparse features extracted by the BU optical attention module to obtain a final spatial light output; and an output module configured to detect and identify the final spatial light output on an output plane to obtain a location of an object in a light field and an identification result.

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.

The invention discloses an end-to-end optical computing chip based on multi-mode analog signal fusion processing, and belongs to the optical computing technology. Comprising a multi-mode input fusion front-end module, an optical fiber input interface and an end-to-end reasoning module. The multi-mode input fusion front-end module can convert different types of original analog signals such as images, spectrums and radio frequencies into unified broadband spectrum input signals. The end-to-end reasoning module constructs a deep optical neural network, and the deep optical neural network comprises a sensing-convolution integrated unit realized by an arrayed waveguide grating (AWG). The end-to-end reasoning module further comprises a photoelectric non-linear-pooling integrated unit, the average pooling function and the non-linear activation function are achieved at the same time, and light path loss is effectively compensated through injection of the supply light source. And finally, integrating the signals subjected to multi-layer processing by a full connection layer, and outputting a classification result by an output layer. According to the chip architecture, direct and efficient processing of multi-mode analog signals is realized.

Systems and methods are provided for devices and methods for implementing an optical neural network (ONN) by leveraging resonator structures, such on micro-ring resonators (MRRs). Examples include unit cells configured to perform a linear transformation on optical signals. Each unit cell comprises a plurality of signal mixing components optically coupled to between adjacent waveguides, where each signal mixing component corresponds to a distinct wavelength and is configured to mix optical signals on the adjacent waveguides at the distinct wavelength. Each unit cell also includes a plurality of phase tuning components each corresponding to a distinct wavelength and configured to adjust a phase of a mixed optical signal at the distinct wavelength.

The invention relates to a distributed optical fiber sensing event identification method and device based on optical calculation, and relates to the technical field of optical fiber sensing event identification. Two-dimensional time-frequency signals of distributed sensing information are collected through a coherent detection type phi-OTDR system, and accurate recognition of sensing events is achieved on an optical path through optical calculation. The optical calculation comprises an optical convolution operation and an optical full connection operation, a two-dimensional time-frequency signal is processed into a one-dimensional tensor, and the neural network weight is loaded to the light through the cooperation of a light intensity amplitude modulator and a programmable multi-wavelength laser array, so that the optical neural network calculation is completed, and finally the corresponding light intensity of four wavelengths is obtained. Accurate identification of sensing events is realized. Sensing signals are processed through optical calculation, dependence on electronic calculation resources is avoided, and the response speed and the anti-interference capacity of a sensing system are improved; meanwhile, through distributed processing, the fault tolerance and reliability of the system are enhanced.

The invention discloses a three-dimensional heterogeneous integrated optical computing chip, and belongs to the field of optical computing. The computing chip consists of three parts: a spatial diffraction optical neural network core particle, a plane matrix reconfigurable optical neural network component and a plane switchable optical switch module, the single or multiple spatial diffraction optical neural network core particles, the plane matrix reconfigurable optical neural network assembly and the plane switchable optical switch module are combined together in a three-dimensional heterogeneous integration mode, and efficient multi-task optical calculation processing is achieved. The spatial diffraction optical neural network and the plane matrix reconfigurable optical neural network are combined with each other, a traditional optical calculation framework is broken through, the method has wide application prospects in the optical calculation field, and the blank of related technical fields is filled.

The invention provides a multiband photoresponse nerve synapse device based on a two-dimensional and organic heterostructure, an array and application, and belongs to the technical field of organic electric solid-state devices. The device provided by the invention adopts a three-end bottom gate top contact structure and comprises a gate substrate, an insulating layer, a channel layer and a source / drain electrode, and the channel layer forms an II-type heterojunction by a two-dimensional semiconductor material with a narrow-band gap characteristic and an organic semiconductor material. The heterostructure realizes ultraviolet to far infrared (365nm-10 [mu] m) multiband photoelectric response, and shows synaptic plasticity under light pulse modulation, including short-term enhancement, long-term enhancement and learning-forgetting-re-learning characteristics. Large-area array preparation of the device can be realized through methods such as chemical vapor deposition, solution spin coating, jet printing or layer-by-layer self-assembly. The technology not only can be used for brain-like learning and memory simulation, but also can be applied to the fields of sensing and memory integrated intelligent systems, optical neural networks, artificial vision, multi-mode intelligent sensing and the like.

The invention relates to the technical field of optical computing, and discloses an optical neural network and an implementation method thereof, and an optical neural network chip, and the method comprises the steps: an input layer converts and processes an input external signal, obtains an optical signal, and inputs the optical signal to a hidden layer; according to the task requirement of an external signal, the hidden layer dynamically adjusts the weight parameter of the optical neural network under the external magnetic field control condition; an optical signal output by the hidden layer is transmitted to the nonlinear activation layer; aiming at the task requirement of an external signal, the nonlinear activation layer dynamically adjusts the nonlinear characteristic of an activation function under an external magnetic field control condition; and the output layer converts the optical signal output by the nonlinear activation layer into an electric signal, carries out corresponding processing, and outputs a final result. Therefore, the optical neural network is endowed with dynamic adjustability and rapid adjustment capability among different network layers, the weight parameter and the nonlinear function of the full-optical domain dynamic adjustment optical neural network are realized, and the overall operation efficiency is improved.

An optical neural network chip (10) having a reconfigurable information path, a design method, and a device. The chip comprises: a chip substrate (100) and an optical diffraction layer (200) arranged on the chip substrate (100). The optical diffraction layer (200) comprises a plurality of reconfigurable diffraction modulation units. The refractive index of the optical diffraction layer (200) can be reconfigured on the basis of optical characteristics of, the number of, the number of layers of, and the spatial distribution of the reconfigurable diffraction modulation units, and a cluster modulation effect of the reconfigurable diffraction modulation units on an optical signal can be used to achieve multi-degree-of-freedom modulation of one or more optical signal wavefront propagation characteristics. Thus, the problems in the related art of the large physical size of modulators, a low integration level, and limited regulation sensitivity and precision of reconfigured optical neural network chips (10) are solved.

Systems and methods are provided for monolithic photonic integrated devices for implementing convolutional neural network (CNN). Examples provide for photonic devices comprising a first optical transform device configured to perform a first transform on input optical signals to output a plurality of intermediate optical signals, a plurality of optical modulation devices configured to perform a convolution on the intermediate optical signals, and second transform device configured to perform a second transform of the plurality of intermediate optical signals and output a plurality of output optical signals. Each of the modulation devices comprises an amplitude modulator and a phase modulator. The photonic device, including an optical source that generates an input optical signal, can be formed monolithically on a common substrate to provide a convolutional optical neural network (CONN) integrated onto a single chip.

Programmable photonic circuits of reconfigurable interferometers can be used to implement arbitrary operations on optical modes, providing a flexible platform for accelerating tasks in quantum simulation, signal processing, and artificial intelligence. A major obstacle to scaling up these systems is static fabrication error, where small component errors within each device accrue to produce significant errors within the circuit computation. Mitigating errors usually involves numerical optimization dependent on real-time feedback from the circuit, which can greatly limit the scalability of the hardware. Here, we present a resource-efficient, deterministic approach to correcting circuit errors by locally correcting hardware errors within individual optical gates. We apply our approach to simulations of large-scale optical neural networks and infinite impulse response filters implemented in programmable photonics, finding that they remain resilient to component error well beyond modern day process tolerances. Our error correction process can be used to scale up programmable photonics within current fabrication processes.

The invention discloses an optical neural network non-linear activation system, method and device, and relates to the technical field of optical neural networks, because a magneto-optical material is integrated in a phase adjustment waveguide arm of a non-linear activation unit, a controller can adjust the current activation function type and weight of the optical neural network according to the current activation function type and weight of the optical neural network. And controlling a magneto-optical material of the phase adjustment waveguide arm to generate a magneto-optical effect so as to perform phase adjustment on an optical signal transmitted through the phase adjustment waveguide arm, so that the nonlinear activation unit can adapt to the type and weight of a current activation function, and perform corresponding nonlinear calculation on a target input signal to obtain a target input signal. Dynamic switching of various nonlinear activation functions is realized, and the flexibility of the optical neural network is improved.

The invention discloses a system and a method for realizing positive and negative weighting in an optical domain, and the system comprises a multi-wavelength light source array which provides multi-channel optical signals, and each wavelength corresponds to an input channel; the adjustable bias photoelectric modulator arrays are in one-to-one correspondence with the light sources, a quiescent working point is biased in a positive / negative slope linear region of a transmission curve through an independent bias circuit, positive and negative weight symbol control is realized, and an input electric signal is loaded; the multi-wavelength micro-ring weighting unit is used for weighting the modulated optical signal by regulating and controlling the transmissivity of the micro-ring; the photoelectric detection unit adopts a single-path detector to receive a composite optical signal and convert the composite optical signal into an electric signal; and the capacitor blocking and signal synthesis unit filters a direct current component, synthesizes an alternating current weighted signal and outputs a real number weighted sum. The system does not need to balance detectors and redundant devices, simplifies hardware architecture, reduces cost and size, reduces power consumption, improves weighting precision and reliability, supports flexible expansion of multi-wavelength channels, adapts to requirements of a large-scale optical neural network, and provides support for optical calculation integration and low-power-consumption landing.

An all-analog optical neural network includes multiple all-analog optical neural network layers; a laser and splitter configured to distribute light signals from the laser equally across all of the multiple all-analog optical neural network layers; integrated MZI switches configured to switch the all-analog optical neural network to a hybrid backpropagation training configuration that measures the light signals in forward and backward directions, and a trains a linear portion of the all-analog optical neural network. Preferably, each of the all-analog optical neural networks comprises: an integrated silicon photonic neural network (PNN) of Mach-Zehnder interferometers (MZIs) and programmable phase shifters (η) configured to implement a programmable unitary matrix-vector multiplication (MVM) operation U; photonic meshes configured to send input forward and backward inference signals to the PNN and configured to measure using both amplitude and phase detection an output forward signal and a backward adjoint signal from the PNN.

An apparatus and methods are provided for an optical neural network architecture that utilizes parity-time (PT) symmetric couplers. The example PT symmetric optical neural network is based on layers using the PT symmetric couplers that each have two parallel waveguides. One waveguide applies gain while the other waveguide applies an equal loss to signals.