The present disclosure relates to semiconductor structures and, more particularly, to optical neuro-mimetic devices and methods of manufacture. The structure includes: a plurality of photodetectors and electrical circuitry that converts photocurrent generated from the photodetectors into electrical current and then sums up the electrical current to mimic neural functionality.

A digital signal modulation method for a photon artificial intelligence computing chip, including: modulating one or more groups of digital electrical signals into optical signals; where the group of digital electrical signals comprises several timing signals being outputted in sequence in a channel within a fixed period; where each timing signal has the same base clock and signal time length; where each timing signal conveying N-bit digital information has 2.sup.N−1 base clocks, the number of the base clocks of a high-level signal or the number of a digital signal “1” in the timing signal is a signal value of the timing signal, and the signal value is equal to a value of the N-bit digital information being transmitted; and where the timing signal is a modulating signal for converting the electrical signal to the optical signal. Compared with the existing calculation scheme based on digital-to-analog conversion and analog signal modulation, the calculation scheme in the present invention avoids using a digital-to-analog converter with a high cost and power consumption, can be directly connected with the digital signals of the electronic chip, and avoids quantization error during the digital-to-analog conversion of digital signals.

Various embodiments provide for computational systems including multiple circuit packages, each circuit package comprising an electronic integrated circuit having multiple processing elements and intra-chip bidirectional photonic channels connecting the processing elements into an electro-photonic network, with inter-chip bidirectional photonic channels connecting the processing elements across the electro-photonic networks of the multiple circuit packages into a larger electro-photonic network.

Component errors prevent linear photonic circuits from being scaled to large sizes. These errors can be compensated by programming the components in an order corresponding to nulling operations on a target matrix X through Givens rotations X.fwdarw.T.sup.†X, X.fwdarw.XT.sup.†. Nulling is implemented on hardware through measurements with feedback, in a way that builds up the target matrix even in the presence of hardware errors. This programming works with unknown errors and without internal sources or detectors in the circuit. Modifying the photonic circuit architecture can reduce the effect of errors still further, in some cases even rendering the hardware asymptotically perfect in the large-size limit. These modifications include adding a third directional coupler or crossing after each Mach-Zehnder interferometer in the circuit and a photonic implementation of the generalized FFT fractal. The configured photonic circuit can be used for machine learning, quantum photonics, prototyping, optical switching/multicast networks, microwave photonics, or signal processing.

Method and system for intelligent decision-making photonic signal processing, where the system comprises a multi-functional input unit, an electro-optical conversion module, a signal processing module, a photoelectric conversion module, a multi-functional output unit, and an artificial intelligence chip. The invention combines the advantages of photonic high-speed, wide-band, and electronic flexibility, combined with heterogeneous photoelectron hybrid integration, packaging and other processes, along with deep learning algorithm, is an intelligent electronic information system that may simultaneously realize digital and analog signal processing.

The present disclosure advantageously provides an Optical Hardware Accelerator (OHA) for an Artificial Neural Network (ANN) that includes a communication bus interface, a memory, a controller, and an optical computing engine (OCE). The OCE is configured to execute an ANN model with ANN weights. Each ANN weight includes a quantized phase shift value θ.sub.i and a phase shift value ϕ.sub.i. The OCE includes a digital-to-optical (D/O) converter configured to generate input optical signals based on the input data, an optical neural network (ONN) configured to generate output optical signals based on the input optical signals, and an optical-to-digital (O/D) converter configured to generate the output data based on the output optical signals. The ONN includes a plurality of optical units (OUs), and each OU includes an optical multiply and accumulate (OMAC) module.

Provided is a reservoir computing system including a reservoir having a random laser for emitting a non-linear optical signal with respect to an input signal. The reservoir computing system also includes a converter for converting the non-linear optical signal into an output signal by applying a conversion function. The conversion function is trained by using a training input signal and a target output signal.

An integrated optical circuit for an optical neural network is provided. The integrated optical circuit is configured to process a phase-encoded optical input signal and to provide a phase-encoded output signal depending on the phase-encoded optical input signal. The phase-encoded output signal emulates a synapse functionality with respect to the phase-encoded optical input signal. A related method and a related design structure are further provided.

A microscopy system comprises a microscope for analyzing a sample, a computing device for processing measurement signals and at least one microphone for capturing sounds. The computing device is configured to evaluate captured sounds in order to identify a microscope activity in progress or command an intervention in the microscope activity in progress or identify ambient sounds based on microscope sounds.

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.