Optical Neural Network for High-Speed Signal Processing
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Solution Overview
Problem
Current digital signal processors (DSPs) face limitations in processing high-speed optical signals due to operational speed and power consumption constraints, and they do not provide high-quality processing for optical telecommunication links with intensity modulation and direct detection systems, especially in ultra-high capacity networks.
Innovation Solution
An optical neural network (ONN) is used for processing optical signals, which includes layers of optical neurons with weighted interconnections controlled by phase shifters, allowing for optical convergence and enabling ultra-high data rate processing directly in the optical domain without the need for electronic conversion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital signal processors (DSPs) are used to process optical signals, then data processing capability is provided, but operational speed is limited and power consumption is high
Solution Approach 1:
The patent replaces electronic DSP processing with optical neural network processing. The optical neural network uses optical components (waveguides, interferometers, modulators) to perform signal processing directly in the optical domain, eliminating the need for electro-optic conversion and electronic processing. This substitution enables processing speeds matching the optical signal rates (100 GBauds and above) while reducing power consumption compared to electronic DSP systems.
2Productivity
If digital signal processors (DSPs) are used to process optical signals, then data processing capability is provided, but power consumption is greater than 20 Watts
Solution Approach 1:
The patent replaces power-hungry electronic DSP operations with low-power optical processing. The optical neural network performs matrix multiplications and nonlinear operations using optical interference and modulation, consuming only the power needed for optical modulation and detection rather than high-power electronic processing. This reduces power consumption from over 20 Watts to levels suitable for ultra-high capacity networks.
3Productivity
If DSP-based processing is used for optical telecommunication links, then signal processing is performed, but processing quality is insufficient for intensity modulation and direct detection systems
Solution Approach 1:
The patent replaces electronic DSP processing with optical neural network processing specifically designed for intensity modulation and direct detection (IM/DD) systems. The optical neural network performs equalization, signal recovery, and detection directly in the optical domain, maintaining signal integrity and achieving high processing quality for IM/DD systems where electronic DSP approaches fail.
4Productivity
If optical signals are converted to electronic domain for processing, then DSP processing can be performed, but the conversion process adds complexity and limits speed
Solution Approach 1:
The patent extracts the signal processing function from the electronic domain and implements it directly in the optical domain using an optical neural network. This eliminates the electro-optic conversion stage and its associated complexity (converters, synchronization, clock recovery), while maintaining full processing capability for equalization, signal recovery, and detection.
Solution Approach 2:
The patent substitutes electronic processing systems with optical processing systems, replacing complex electro-optic conversion infrastructure with integrated photonic circuits that perform all necessary signal processing operations directly on the optical signal.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for efficient handling of ultra-high data rate signals in optical transmission systems, reducing the need for equalization at the receiver end and providing a simple and effective method for processing high-speed optical signals, while minimizing power consumption and operational complexity.
Implementation Method 1
The weightings are established by phase-shifting the input optical signals prior to the summation. The phase shift controller controls the phase-shifting so as to obtain required weightings for the input optical signals.
Implementation Method 2
Yichen Shen et al: 'Deep learning with cohererent nanaphotonic circuits' describes an artificial neural network, which consists of a set of input artificial neurons connected to at least one hidden layer and an output layer. In particular an Optical Neural Network, ONN, architecture is described, where signals are encoded in the amplitude of optical pulses propagating in integrated photonic waveguides where they pass through an optical interference unit, OIU
Implementation Method 3
optical signals are sampled by an optical sampler. The optical samples are processed by an optical neural network (ONN) which includes layers of optical neurons.
Data Source
Figure 1A
Figure 1B~1C
Figure 1D
AI summary
An apparatus for optical processing of a signal includes an optical sampler, an optical neural network and a phase shift controller. The optical sampler obtains parallel optical samples by sampling a continuous optical signal. The optical neural network optically processes the parallel optical samples and provides a final output optical signal resulting from the processing. The optical neural network includes interconnected optical neurons. Each of the optical neurons inputs multiple optical signals and outputs an optical signal which is a weighted sum of the input optical signals. The weightings are established by phase-shifting the input optical signals prior to the summation. The phase shift controller controls the phase-shifting so as to obtain required weightings for the input optical signals.