A hybrid neuromorphic computing device is provided, in which artificial neurons include light-emitting devices that provide weighted sums of inputs as light output. The output is detected by a photodetector and converted to an electrical output. Each neuron may receive output from one or more other neurons as initial input, allowing for high degrees of fan-out and fan-in, including true broadcast-to-all functionality.

A mobile terminal includes a sensor that acquires information from a surrounding environment, an LD that converts an electrical signal output from the sensor into an optical signal, an optical processor that extracts a feature quantity of the information transmitted by the optical signal and outputs an optical signal including an extraction result, a PD that converts an optical signal output from the optical processor into an electrical signal, and a communication circuit that transmits a signal output from the PD to a cloud server that performs processing of the FC layer of the DNN inference.

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.

Aspects relate to a photonic processing system, an integrated circuit, and a method of operating an integrated circuit to control components to modulate optical signals. A photonic processing system, comprising: a photonic integrated circuit comprising: a first electrically-controllable photonic component electrically coupling an input pin to a first output pin; and a second electrically-controllable photonic component electrically coupling the input pin to a second output pin.

Aspects relate to a photonic processing system, a photonic processor, and a method of performing matrix-vector multiplication. An optical encoder may encode an input vector into a first plurality of optical signals. A photonic processor may receive the first plurality of optical signals; perform a plurality of operations on the first plurality of optical signals, the plurality of operations implementing a matrix multiplication of the input vector by a matrix; and output a second plurality of optical signals representing an output vector. An optical receiver may detect the second plurality of optical signals and output an electrical digital representation of the output vector.

A fluxonic processor includes processes photonic synapse events and includes a transmitter that receives neuron signal and produces output photons; a neuron that receives a dendrite signal and produces the neuron signal from the dendrite signal; a dendrite that receives a synapse signal, and produces the dendrite signal from the synapse signal, the dendrite including: a dendritic receiver loop; a dendritic Josephson isolator; and a dendritic integration loop; and the synapse in electrical communication with the dendrite and that receives an input photon and produces the synapse signal from the input photon, the synapse including: a synaptic receiver; a synaptic Josephson isolator in communication with the synaptic receiver; and a synaptic integration loop that receives the synaptic receiver fluxons and produces the synapse signal from the synaptic receiver fluxons.

Systems and methods for designing a hybrid neural network comprising at least one physical neural network component and at least one digital neural network component. A loss function is defined within a design space composed of a plurality of voxels, the design space encompassing one or more physical structures of the at least one physical neural network component and one or more architectural features of the digital neural network. Values are determined for at least one functional parameter for the one or more physical structures, and the at least one architectural parameter for the one or more architectural features, using a domain solver to solve Maxwell's equations so that a loss determined according to the loss function is within a threshold loss. Final structures are defined for the at least one physical neural network component and the digital neural network component based on the values.

The invention relates to the field of photonic integrated circuits and provides an optical modulator and a photonic integrated system, which can suppress phase deviation caused by carrier diffusion. The optical modulator includes at least one phase shifter including a waveguide channel for transmitting optical signal, and a P-type doped region and a N-type doped region located on opposite sides of the waveguide channel. In the waveguide channel, an undoped intrinsic region is located between the P-type doped region and the N-type doped region. At least one end of the intrinsic region or close to the at least one end is provided with a blocking structure for blocking the diffusion of carriers from the intrinsic region along the waveguide propagation direction, so that the phase deviation caused by the diffusion of carriers can be suppressed, and the electrical crosstalk between adjacent phase shifters can be suppressed, thereby avoiding modulation signal distortion caused by the electrical crosstalk. As a result, the reliability and precision of the photonic integrated system can be improved.

Provided are an optical device which is capable of optically implementing an activation function of an artificial neural network and an optical neural network apparatus which includes the optical device. The optical device may include: a beam splitter splitting incident light into first light and second light; an image sensor disposed to sense the first light; an optical shutter configured to transmit or block the second light; and a controller controlling operations of the optical shutter, based on an intensity of the first light measured by the image sensor.

Methods, machines and systems for processing information are disclosed in which waves containing information select locations for processing (46). Associations between information containing waves may be made and recalled. In some embodiments information containing waves or sequences may be output as visual, auditory, tactile, motion, data or other forms. Software embodiments of the described mechanism are also included.