Physics-Informed Optical Neural Networks for Nanometer Alignment
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Solution Overview
Problem
Existing methods for measuring, aligning, and modeling optical systems face challenges in achieving nanometer-scale precision, dealing with complex interactions, lacking observational techniques, and being economically burdensome.
Innovation Solution
An Optical Intrinsic Neural Network (OINN) that incorporates known physical relationships into its architecture, using novel neural network layers to accurately measure, align, and describe optical systems, reducing the need for expensive equipment and time-consuming adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional brute force measurement and alignment methods are used, then comprehensive system parameters can be measured, but measurement precision and alignment accuracy deteriorate due to cumulative errors and inability to achieve nanometer-scale precision
Solution Approach 1:
The patent introduces an Optical Intrinsic Neural Network (OINN) as an intermediary computational model that bridges the gap between limited measurements and complete system characterization. The OINN acts as a virtual sensor that infers unmeasured parameters from available measurements, eliminating the need for direct nanometer-scale measurement of all components while maintaining high measurement precision through physics-informed neural network predictions.
Solution Approach 2:
The patent replaces traditional mechanical alignment methods (manual adjustments, physical measurement tools) with a computational approach using OINN. Instead of physically measuring and adjusting each component to nanometer precision, the system uses the neural network to compute optimal alignment parameters from coarser measurements, substituting mechanical precision requirements with computational inference capabilities.
2Reliability
If exhaustive search alignment methods are used, then theoretical design parameters can be matched, but alignment time and computational resources increase dramatically due to numerous degrees of freedom
Solution Approach 1:
The patent applies preliminary action by pre-training the OINN with theoretical optical models and design parameters before actual alignment tasks. The neural network is pre-loaded with knowledge of optimal optical system behavior, enabling it to quickly predict alignment parameters during operation without performing exhaustive searches in real-time, thus maintaining high reliability while reducing alignment time.
Solution Approach 2:
The patent implements feedback mechanisms where the OINN continuously predicts system performance based on current alignment parameters and compares predictions with actual measurements. This feedback loop enables rapid iterative optimization of alignment without exhaustive search, as the network learns from measurement residuals and adjusts parameters efficiently, reducing both time and computational resources while maintaining accuracy.
3Manufacturing precision
If manual iterative adjustments are used for alignment, then system parameters can be optimized, but cost and time consumption increase due to extensive human intervention and equipment usage
Solution Approach 1:
The patent enables self-service alignment through the OINN, which autonomously predicts optimal alignment parameters and guides the alignment process without requiring expert human intervention. The neural network serves as an automated alignment expert that processes measurements and generates correction instructions, eliminating the need for skilled operators to perform manual iterative adjustments while maintaining high alignment precision and reducing overall process complexity.
Data Source
AI summary
The present invention introduces an Optical Intrinsic Neural Network (OINN) for accurately measuring, aligning, modeling, and describing optical systems. This innovative approach combines neural network algorithms with traditional optical theoretical modeling, incorporating layers based on physical formulas. The OINN features optical propagation layers, modulator layers, and detection layers, each with parameters reflecting intrinsic physical meanings and incorporating noise models such as shot noise and thermal noise. The invention includes a method for training the OINN using a specially configured dataset to ensure precise alignment with true physical quantities. This facilitates accurate measurement, calibration, and simulation of complex optical systems, offering a cost-effective and precise solution to traditional challenges in optical system design and analysis.