Optical Neural Network Beam Splitter Compensation
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Solution Overview
Problem
The poor beam-splitting accuracy of beam splitters in optical neural networks leads to performance issues and reduced data processing accuracy, making it difficult to meet the computing power and power consumption requirements in the era of big data and AI applications.
Innovation Solution
The solution involves compensating the beam splitter in an optical neural network by adjusting the parameters of internal phase shifters in the interference optical path structures to achieve a desired beam-splitting ratio, thereby improving the accuracy and performance of data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional beam splitters are used in optical neural networks, then the device structure is simple, but the beam-splitting accuracy is low leading to poor data processing performance
Solution Approach 1:
The patent divides a single beam splitter function into multiple components: a first beam splitter, a second beam splitter, and an optical path difference controller. This segmentation allows each component to be optimized independently, with the controller adjusting the optical path difference to achieve accurate beam splitting ratios despite manufacturing variations in the individual beam splitters.
Solution Approach 2:
The patent implements a feedback mechanism where the optical path difference controller continuously adjusts the optical path difference between the first and second optical paths based on detected output light intensity. This feedback loop compensates for deviations in beam splitter performance, maintaining high beam-splitting accuracy dynamically.
2Manufacturing precision
If beam splitter manufacturing variations are tolerated, then the manufacturing process is easier, but the data processing accuracy deteriorates
Solution Approach 1:
The patent changes the controllable parameter from fixed beam splitter splitting ratios to adjustable optical path difference. By controlling the optical path difference dynamically, the system can compensate for manufacturing variations in the beam splitters themselves, making the manufacturing process easier while maintaining high accuracy through parameter adjustment.
Solution Approach 2:
The patent transforms a static beam splitting system into a dynamic one by introducing real-time control of the optical path difference. This dynamic adjustment capability allows the system to adapt to manufacturing variations and maintain optimal performance without requiring extremely precise manufacturing tolerances.
3Reliability
If high beam-splitting accuracy is achieved through precise manufacturing, then data processing performance improves, but manufacturing cost and difficulty increase
Solution Approach 1:
The patent introduces an optical path difference controller as an intermediary component between the beam splitters and the output. This mediator compensates for manufacturing imperfections in the beam splitters, ensuring high data processing accuracy without requiring the beam splitters themselves to be manufactured with extreme precision.
Solution Approach 2:
The system performs self-correction through the optical path difference controller, which automatically adjusts the optical path difference to compensate for beam splitter variations. This self-service mechanism maintains high reliability without requiring external calibration or manual adjustment during operation.
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 enhances the data processing accuracy and performance of optical neural networks by compensating for beam splitter deviations, enabling more accurate and efficient linear optical modules, which is crucial for meeting the demands of big data and AI computing.
Implementation Method 1
an optical interference unit of the optical neural network includes a first interference optical path structure, a phase shifter, and a second interference optical path structure
Implementation Method 2
calculating parameters of internal phase shifters of the first interference optical path structure and the second interference optical path structure
Data Source
AI summary
Disclosed are a data processing method and apparatus based on an optical neural network, a computer-readable storage medium, and an optical neural network. The method includes: acquiring initial optical information and final output optical information as well as intermediate input optical information and intermediate output optical information at input/output ports of the phase shifter of an input optical signal in a case that beam-splitting ratios of the beam splitters of the two interference optical path structure satisfy a beam-splitting compensation condition; calculating parameters of the internal phase shifters of the two interference optical path structures in a case that the initial optical information and the intermediate input optical information as well as the intermediate output optical information and the final output optical information satisfy a preset beam-splitting condition of the optical neural network; and performing data processing by using the optical neural network based on the parameters.