Optical Neural Network for All-Optical Signal Correction
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Solution Overview
Problem
Existing optical neural networks are unable to process both the real and imaginary parts of an optical signal simultaneously, and they require conversion of signals to electrical form for processing, which limits their effectiveness in mitigating dispersive and non-linear effects in optical transmission lines.
Innovation Solution
An optical neural network integrated into a transmitting-receiving module that uses a power divider, optical delay components, an optical control component for amplitude and phase weighting, a coupler, and a non-linear node to process the complex optical signal directly, compensating for linear and non-linear distortions without converting the signal to electrical form.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If optical signal is converted to electrical signal for processing, then signal processing capability is improved, but system complexity and energy consumption increase
Solution Approach 1:
The patent replaces electrical signal processing systems with an all-optical neural network system. The optical neural network uses optical components (optical modulators, optical delay elements, optical adders) to perform neural network operations directly on optical signals, eliminating the need for optical-to-electrical conversion and subsequent electrical processing, thus reducing system complexity while maintaining processing capability
Solution Approach 2:
The patent introduces an optical neural network as an intermediary system between the optical signal source and the output. This optical neural network acts as a mediator that processes optical signals using optical computation, avoiding the need for electrical conversion while achieving the desired signal processing functions through optical interference and nonlinear optical effects
2Ease of operation
If optical signal is converted to electrical signal for processing, then signal processing capability is improved, but energy consumption increases
Solution Approach 1:
The patent substitutes energy-intensive electrical processing systems with energy-efficient optical processing. The optical neural network performs computations using optical interference and nonlinear optical effects, which consume significantly less energy than electrical signal processing, while maintaining full signal processing capability for correcting dispersion and nonlinear effects
Solution Approach 2:
The patent enables continuous optical signal processing without interruption for conversion. The optical neural network processes optical signals continuously in the optical domain, maintaining the continuous nature of optical transmission and avoiding energy losses associated with repeated optical-to-electrical and electrical-to-optical conversions
3Device complexity
If linear processing only is performed on optical signal, then device complexity is reduced, but signal correction capability is insufficient
Solution Approach 1:
The patent changes the processing approach from linear to nonlinear optical processing. The optical neural network utilizes nonlinear optical effects (such as Kerr effect, four-wave mixing) to perform nonlinear computations that can effectively correct both linear dispersion and nonlinear distortion effects in optical fibers, enhancing signal correction capability while keeping the device structure relatively simple
Solution Approach 2:
The patent employs composite optical processing by combining linear optical components (optical delay elements, optical couplers) with nonlinear optical elements (optical modulators, nonlinear optical media). This composite approach enables the system to handle both linear and nonlinear signal distortions effectively without requiring overly complex device architecture
4Device complexity
If real and imaginary parts are processed separately, then device complexity is reduced, but processing efficiency decreases
Solution Approach 1:
The patent merges the processing of real and imaginary parts into a unified optical neural network architecture. The optical neural network processes both components simultaneously through parallel optical paths and optical interference, enabling comprehensive signal processing in a single integrated device rather than requiring separate processing systems for each component
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
Enables real-time correction of both linear and non-linear distortions in optical signals within the optical domain, reducing the burden on digital microprocessors and allowing for energy-efficient, all-optical signal processing without requiring changes to the optical fiber or encoding methods, thereby improving signal quality and reducing computational resources.
Implementation Method 1
an optical control component (14) which weights (wi) each delayed copy with an amplitude ai and a phase Φi which are electrically regulated and preferably independently of each other
Implementation Method 2
a non-linear node (16) to which a complex sum is sent out of the coupler
Data Source
AI summary
An optical transmitting-receiving module with neural network, including a power divider which distributes an input signal to be corrected and sends it in waveguides to an optical delay component which imparts time delays to copies of the optical signal; an optical control component that weights each delayed copy with an amplitude and a phase that are electrically adjusted during a training procedure; and a coupler which recombines the N signals and a non-linear node to which a complex sum is sent at the output of the coupler.