Optical System Design Automation via Machine Learning
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Solution Overview
Problem
Current software programs for designing optical systems can only optimize initial designs and lack the ability to structurally alter them, relying heavily on the initial design's suitability, which limits their effectiveness in achieving satisfactory results, especially when the initial design does not have a suitable structure.
Innovation Solution
A machine learning method using a neural network is employed to generate and optimize optical system designs by training on a database of existing systems through reinforcement learning and imitation learning, allowing for the automated creation of initial designs that can be further optimized.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional optimization software is used to optimize an initial optical design, then optimization speed and efficiency are improved, but the ability to structurally alter the design is lost
Solution Approach 1:
The design process is segmented into two distinct phases: (1) structural design phase using machine learning to determine the optimal configuration of optical surfaces, and (2) optimization phase using conventional software to refine parameters. This segmentation allows each tool to excel at its specialized task while avoiding its limitations.
Solution Approach 2:
A machine learning model serves as an intermediary between the initial design and conventional optimization software. The ML model generates structurally sound initial designs that are then passed to conventional optimizers, bridging the gap between structural creativity and parameter optimization.
2Device complexity
If conventional optimization software is used without structural modification capability, then software simplicity is maintained, but design quality deteriorates when initial design is unsuitable
Solution Approach 1:
The machine learning model performs preliminary structural design actions before conventional optimization software is applied. By pre-establishing an appropriate structural framework, the ML model ensures that subsequent optimization can achieve high design quality without the software needing structural modification capabilities.
3Manufacturing precision
If expert knowledge is used to create initial designs, then design quality is improved, but automation is reduced
Solution Approach 1:
The machine learning model is trained on existing optical designs and automatically generates initial designs without requiring expert human intervention. The system serves itself by learning from historical data and applying that knowledge autonomously to create high-quality initial designs.
Solution Approach 2:
The machine learning model copies successful patterns and structures from existing optical designs in its training data. By learning from proven designs, the model can generate new initial designs that inherit the quality characteristics of expert-designed systems while maintaining full automation.
Data Source
AI summary
Methods and apparatuses for designing optical systems are provided. In this case, on a plurality of known optical systems, machine learning is carried out in order to train a computing device. After this training, the computing device can generate a design for an optical system on the basis of parameters describing desired properties of an optical system.